Annotation Economics

Outsourcing data annotation is becoming increasingly widespread as more companies developing AI and ML-powered products or services realize they don’t have the internal bandwidth to handle this job cost-effectively in-house. Building reliable, production-grade AI requires enormous volumes of data (often millions of examples) that need to be labeled accurately and consistently. In many cases, this labeling still has to be done manually or semi-manually, and having ML engineers or data scientists do it can be prohibitively expensive.That’s where annotation services come in. They enable ML and AI teams to delegate labeling tasks to a dedicated group of annotators who not only have the required expertise but can also scale up or down depending on the volume of data, timeline, and technical complexity of the project.But when you outsource annotation, how you structure the collaboration matters just as much as That’s where annotation services come in. They enable ML and AI teams to delegate labeling tasks to a dedicated group of annotators who not only have the required expertise but can also scale up or down depending on the volume of data, timeline, and technical complexity of the project. does the work. The engagement model you choose directly impacts your budget, timeline, and flexibility. Whether your dataset is already collected or still being assembled can make a big difference in which model is right for you.For our labeling services at CVAT, we offer two engagement models:A one-time annotation projectA subscription-based serviceIn this article, we’ll break down how each model works, what financial and operational benefits they bring, and help you decide which one works best for your use case.Model 1: One-Time Annotation ServiceA one-time annotation project is exactly what it sounds like: a fixed-scope engagement where a pre-collected dataset is labeled once, according to well-defined specifications. This model best fits teams working on well-defined, self-contained projects, such as building a proof-of-concept, training a production model, or preparing labeled data for a grant or publication. If your dataset is static, your requirements are clear, and speed and cost transparency are key, a one-time project is the most efficient path.When to choose this modelYour dataset is already fully collected.You have clear annotation guidelines (classes, formats, tools).You need to annotate the data once to feed it into a training pipeline, proof of concept (PoC), or minimum viable product (MVP).You want to keep the collaboration transactional and short-term.How it worksProject scoping. You share the dataset and annotation requirements, including task types, formats, and edge cases.Proof of Concept (PoC). We annotate a small sample to confirm feasibility, estimate complexity, and define per-object pricing.Proposal & agreement. We prepare a commercial offer and sign a fixed-scope contract covering delivery terms and specs.Full data transfer. You provide the complete dataset for annotation.Annotation execution. We assign a trained team, annotate the data according to your specs, and validate quality internally.Final delivery. Results are shared in full or by batch for larger projects.Review & approval. You validate the work; if needed, we handle corrections. Once approved, payment is processed.Pricing & termsWe require a minimum project value of $5,000, regardless of dataset size. That’s because even the smallest project involves fixed overhead, including several levels of communication, PoC, project management, team training, documentation, QA setup, etc. In most cases, cost is calculated per annotated object. This model is transparent: we count the actual number of objects in a dataset, multiply by the agreed rate determined during PoC, and provide full stats upon delivery. However, if in some cases the per object billing model isn’t applicable, we’ll offer an alternative billing model.For example: if your dataset contains 10,000 images and, based on a PoC, we estimate an average of 10 objects per image, that’s 100,000 objects total. At a per-object rate of $0.10, the total project cost would be $10,000.Deadlines & rulesOne-time projects are designed to be executed quickly and predictably. By default, our standard delivery window is within 1 month from the start of work (i.e., once the contract is signed and data is received).For smaller projects (near the $5,000 threshold), we typically deliver the full dataset in one batch.For larger projects, we may break the work into milestones or batches, each with its delivery timeline.Each batch is reviewed by the client, and payment is made upon acceptance of the results.If project complexity or data volume requires more time, we’ll agree on an adjusted timeline during the scoping phase. Model 2: Subscription-Based Annotation ServiceA subscription model offers more flexibility for companies that are still collecting their data or expect to annotate data incrementally over time. Instead of scoping and billing a fixed project, you reserve our annotation capacity for a specific period and send data as it becomes available. This model is ideal for teams working in agile, R&D-heavy environments where the dataset evolves, the specs might change, and rapid feedback loops are essential.When to choose this modelYour dataset is still being collected or updated regularly.You want to start annotation before the full dataset is ready.You need flexibility in timing, batch size, or annotation spec.You’re looking for a longer-term collaboration with predictable access to skilled annotators.How it worksInitial discussion. You describe your project, timeline, and data format.PoC. We annotate a small, representative sample to establish scope, complexity, and per-object pricing.Subscription agreement. We sign a 6-month service agreement (or longer, if needed), including all technical details and annotation rules.Data delivery begins. You send data as it becomes available — weekly, monthly, or in bursts.Ongoing annotation. We label incoming data promptly and return results in batches for your review.Continuous feedback loop. You can iterate on spec or adjust priorities. For significant changes, we re-estimate the scope if needed.Project tracking. We provide running stats so you always know how much of your quota has been used.Pricing & termsUnlike one-time projects, the subscription is prepaid and starts at $5,000 for 6 months, and includes reserved access to annotation resources throughout the subscription period. Subscription = one project: all data delivered under a subscription must follow the same annotation spec. Changes in scope (e.g. new classes or formats) may require a re-estimation. If the client does not send the expected amount of data to cover the anticipated subscription cost, the unused amount will not be refunded.Thanks to prepayment and resource commitment, subscription plans come with built-in discounts, typically 20% to 50% cheaper than one-time pricing. For example: A client plans to annotate around 100,000 objects but doesn’t yet have the full dataset.If they wait and come back later with all data, they’ll likely use a one-time project — at around $0.10/object, totaling $10,000.If they prefer to start immediately and send data gradually, they can choose a 6-month subscription. With a prepayment and volume estimate, we can offer a reduced per-object rate of $0.05–0.075, bringing the total closer to $5,000–$7,500.The result is the same, but the subscription allows them to start earlier, save money, and keep annotation continuous while their dataset grows.Deadlines & rulesUnlike a one-time service, with a subscription-based model, you’re not waiting for the full dataset to be ready. You get annotated data continuously, supporting your model development in real-time. However, to ensure we can process everything smoothly:Within the last 90 days of your subscription, you can send up to 75% of your total quota.In the final 60 days — up to 50%.In the last 30 days — only 25%.This helps us avoid last-minute overloads and ensures timely delivery.One-Time vs. Subscription: A Side-by-Side LookNow, let’s take a quick look at how the two models compare between each other:‍‍Which Pricing Model Is Right For You?So, how do you know which model is right for your project? Both one-time projects and subscription-based services are designed for different workflows and project stages. For instance, if you're a startup collecting traffic camera footage weekly, still experimenting with model architectures, and needing annotated data on an ongoing basis, a subscription gives you flexible access to annotation resources and helps you move faster while saving your budget.On the other hand, if you work at a robotics company with a completed dataset of indoor navigation footage, clear labeling rules, and a tight delivery deadline, a one-time project will get your data annotated quickly without any long-term commitment.In any case, the best choice depends on how far along you are with your dataset, your timeline, and how much flexibility you need.Still not sure where you fall? Tell us about your project and we’ll help you scope it and recommend the best path forward or visit labeling services page to learn more about our process.

You thought you had everything covered. But when your data annotation vendor gets back with error-filled datasets, you’re left with nothing but frustration. With deadlines to meet, you’re forced to seek a different, and hopefully, more competent data labeling service provider. Sounds horrifying, but that’s what some of our clients went through before they turned to us. Like it or not, partnering with a credible vendor can make a difference in your project. But with many vendors to choose from, how do you decide which suits your project needs? You’ll need to do the due diligence. And this guide will show you:Things to look for. Red flags to avoid.Questions to ask when interviewing vendors. Let’s start. Why It’s Worth Being Picky Data annotation, as you know, is a laborious process that demands precision, collaboration, and consistency. Not only does it require a sizeable team of annotators, but it also calls for coordination amongst project managers, machine learning engineers, domain experts, and annotators. On paper, you might find certain data annotation vendors attractive, particularly if they’re offering their service at a low price. However, not all vendors are equipped with an internal system that satisfies the project's requirements.For example, some of our clients initially chose the cheapest vendor, but they ended up with quality issues in their dataset. Likewise, vendors who charge an expensive fee may not guarantee a favorable outcome. So, it’s better to spend more time assessing vendors before making a choice. Otherwise, you risk costly reworks and project delays. Or worse, deploying a flawed machine learning model that compromises users.What to Look For in a Data Annotation Company We know finding the right data labeling company is tough, considering the number of them you’ll find in the market. Still, by vetting the candidates based on several criteria, you can narrow down the list and find a reliable one. Accuracy & QA ProcessesThe first thing to look for in any data labeling service provider is a strong quality check mechanism. If the annotated dataset is compromised, so would the resulting machine learning model. Imagine a medical imaging system struggling to identify a malignant tumor due to inconsistent annotations. The outcome? Disastrous. So, find out how a potential vendor validates their work before contracting them. For example, at CVAT, We train our annotators to adhere strictly to the client’s labeling guidelines.Then, we assess the annotated datasets with quality assurance techniques like Ground Truth, Consensus, and Honeypots. We provide a detailed QA report to our clients and are open to refining the dataset. Don’t stop at understanding the annotation process, but go a step further by requesting a proof of concept (PoC) from the vendor. In fact, we strongly recommend this move as it gives you stronger confidence in the vendor’s annotation quality. If the vendor can’t produce quality annotations from a small sample, chances are it won’t be able to in the actual project. Workforce Setup (Outsourcing vs. In-House)The way a data labeling company recruits, trains, and manages its annotators can affect your ML project. Some data labeling companies don’t have an internal annotation team. Instead, they rely on outsourced labelers, which they don’t have control over. All they do is act as an intermediary, pass on the jobs, and make a profit out of it.On the other hand, data annotation companies with an in-house labeling team can better adapt to changing project requirements. Such companies also have tighter control over who they hire as annotators, as well as the training that labelers undergo. At CVAT, we don’t outsource annotation jobs to others. Instead, we implement every annotation job we take and directly communicate the outcomes to our clients. Moreover, we thoroughly vet each annotator we hire. They’re put to tasks with test projects before we onboard them to our global annotation team. A professional team spread across the world is how we can offer 24/7 project execution across time zones. So, go for a data annotation company that operates with a professional in-house team, particularly if you’re training a complex model. Otherwise, be prepared to deal with noisy datasets, delays, or both. Security and ComplianceThe last thing you need is to suffer a data incident when you trust the vendor to keep your datasets safe. But such a scenario could happen if the vendor you appoint isn’t well-equipped with data security measures. Likewise, partnering with data annotation companies that fail to comply with data privacy laws like GDPR, CCPA, or HIPAA can invite legal troubles.So, the next time you’re evaluating data labeling vendors, find out how they handle data. At the bare minimum, they need to implement compliant measures to protect datasets from intentional or unintentional breaches. For example, we protect our clients’ data by: signing an NDA before commencing the project.complying with data privacy laws like GDPR in our workflow.applying security measures such as secure cloud integration.imposing controlled access on datasets to authorized personnel. Domain ExpertiseSome data annotation projects require domain experts to be part of the annotation workflow. Otherwise, the dataset they deliver might not be precisely labeled. For example, if you’re working on a medical imaging system that trains on medical datasets, you need trained annotators capable of differentiating tumors, fractures, and other anomalies. Domain expert input improves annotation accuracyA quick way to check if the vendor has the required expertise is through their portfolio and case studies. If they’ve worked on a similar project in your industry, they are most likely a good fit compared to others. Otherwise, follow what our clients do — assess the vendor through a PoC. Then, decide if they live up to their marketing pitch. Scalability & Turnaround TimeMost companies innovating with AI/ML models start with a simple prototype, which their vendor has no issue annotating. But as they grow, they need to annotate objects with diverse complexities and types. And that’s where operational limits, if any, start to show. With changing requirements, some data labeling vendors struggle to cope, resulting in costly delays. Worse, if they fail to adapt to new requirements, you will need to seek a different provider and adapt to a new workflow all over again. So, how do you spot scalability issues BEFORE you start a project?One giveaway is vendors who delay starting a project because they lack resources. Also, you might want to reconsider your option if the vendor charges more to prioritize your project. ‍Andrey Chernov, Head of Labeling at CVAT, stressed, “Make sure your vendor can clearly explain how they run their process and back up any promises they made.”As a precaution, find out if the vendor can cope with growing annotation workloads as your project scales. On top of that, you can also ask for the typical turnaround time that the vendor can commit to. At CVAT, most projects take 1 month to complete, but we strive to deliver faster. Pricing We’ve mentioned that pricing shouldn’t be a deciding factor when choosing a vendor. That said, price can be a useful guide, especially if vendors demonstrate quality in their pilot test. Another consideration is your budget, which the vendor’s price must fit into. And that’s where transparency comes into play. The vendor you choose should be upfront about the fee they charge, because no one enjoys hidden surprises. At CVAT, we price a project based on the following models. {{service-provider-table-1="/blog-banners"}}We don’t rush into a contract straightaway. Instead, we will work through your project requirements, list the tasks involved, and offer a transparent pricing model. Once we finalize the price, we’ll honor it throughout our engagement. Yes, no rude surprises for our clients. On top of that, we also provide volume discounts to clients as we encourage long-term partnerships. ‍Tip: To protect your interest, we strongly recommend that you finalize the price with the vendor and lock it with a contract. That’s the practice we do at CVAT to prevent misaligned expectations with our clients.Red Flags to Avoid The harsh reality is that not all data labeling service providers are committed to delivering high-quality results. Thankfully, you can call them out by some obvious traits they show. Lack of process transparency The vendor should be able to clearly explain their data annotation workflow. From data storage to how they distribute labeling tasks to annotators, a good vendor will take you through the stages — patiently. So, if all you get are vague responses, be wary about engaging that particular vendor.Unrealistic promisesEver met vendors that promise 100% annotation accuracy before understanding your project requirements? Well, that’s a major red flag. Any vendor worth collaborating with will take the time to ask questions, ask for representative data, and run a PoC before promising anything. Communication barrier If the vendor struggles to provide feedback to your development team, you may want to consider other options. Clear and timely communication, as we know, is pivotal to delivering quality datasets. Lack of expertiseSome vendors are adept in a specific industry, such as automotive, but unproven in others, like medical and agriculture. If you choose to go ahead, despite knowing the mismatch, you’re risking your project. In-House vs. Outsourced: Pros & Cons Amidst frustration, you might consider setting up an in-house data annotation team. But before you do that, consider the pros and cons of doing so. {{service-provider-table-2="/blog-banners"}}Having your own in-house labelers naturally provides greater control, but you’ll need to invest in setting up and scaling the team. Not all companies, especially smaller ones, can afford to invest in a team of labelers and annotation tools. Outsourcing, meanwhile, is more affordable, flexible, and allows you access to highly trained experts. When you outsource, you save resources and time that you can allocate to your core business area. Of course, we don’t deny the risks of outsourcing, such as data security, compliance, and quality control. However, with careful deliberation, you can find a service provider that addresses your concerns. For example, our data labeling pipeline is designed to be secure, expert-led, and scalable. Plus, all our feedback goes directly to your project team. Questions to Ask a Vendor Ideally, ask clarifying questions before you collaborate with a data labeling service provider. They’ll help you resolve doubts, along with questionable vendors. Below are some questions we thought helpful. What industries and types of data have you annotated before (e.g., images, video, text, audio)?Can you handle projects with large datasets? Are your annotators in-house or crowdsourced?What quality control processes do you have in place?What is your average annotation accuracy rate?What is your average turnaround time for similar projects?How do you protect sensitive or proprietary data?Do you have your own annotation platform, or do you work with client platforms?Do you support domain-specific expertise? How is pricing structured (per task, per hour, per dataset)?How often will we receive progress updates?Do you offer a small paid or unpaid pilot project before full engagement?Getting Started with a Trusted Provider Quality annotation is extremely important to ensure that ML models make accurate inferences. But not all data labeling service providers can live up to their promise. We hope you’ve learned how to find one with this guide. Otherwise, consider partnering with us. CVAT helps companies of all sizes produce accurate, consistent and efficient data annotation. Led by data annotation experts, here is what CVAT has to offer: High-quality annotations - We impose strict quality controls to ensure that the annotated datasets meet your requirements.Scalable workforce - We vet, hire, and train annotators worldwide to take on annotation projects of all sizes. Timeliness - We respect the time we agreed on with our clients and strictly adhere to deadlines. Platform expertise - We annotate with the annotation tools we created, putting our team at an advantage against others. Seasoned professionals - Our team doesn’t just label data; we know the intricacies of data and communicate directly with our clients. Brands like OpenCV.ai, Carviz, and Kombai trusted us with their annotation projects. And we hope you will, too. Want to skip the trial and error? Check out CVAT’s labeling services or get in touch.‍

In the first two parts of this article series, we discovered the cost of annotating images and videos yourself or with an in-house team. This part investigates the finances and resources you need to outsource the data annotation to the labeling service.‍‍However, let's first revisit our practical scenario: Imagine a leading robotics scientist developing a smart home assistant to distinguish between dirt and valuable objects in a home environment. Life's chaos often includes scattered toys, misplaced glasses, pet fur, and god knows what else. The proposed robot aims to clean efficiently and assist in locating misplaced items. Such functionality could benefit the elderly by helping them keep track of their possessions, for example. So, there is a niche for such products.‍As the project's lead, you are instrumental in guiding a compact research team that has gathered a dataset of 100,000 images, each depicting different room settings with items scattered across the floor. According to publicly available data, this dataset size is typical for robotics projects, ranging from thousands to millions of images. ‍With an average of 23 objects per image, the task involves annotating approximately 2.3 million objects. This series of articles explores various strategies for managing this large-scale annotation challenge, including do-it-yourself approaches, forming an in-house team, outsourcing, and utilizing crowdsourcing techniques.‍‍Welcome to the third part of our series, which explores the costs of outsourcing data annotation to cover the scientist's labeling needs.‍Case 1: You handle the task yourself or with minimal colleague help.Case 2: You hire annotators and annotate with your team.Case 3: You outsource the task to professionals.Case 4: Crowdsourcing.‍Case 3: You Outsource the Task to Professionals‍Let's start with a brief introduction and a statement that all data labeling companies operate similarly, with some variations that can significantly impact the quality of their labeling services. The devil is in the details. And CVAT.ai is not an exception. If the named scientist comes to us before jumping into the work, we will request some information from him and his team.‍Time and Stages‍To be precise, this is how the whole workflow will look, separated by stage and with time estimations. It might differ for different companies, so we are talking from our experience.‍We are not shy to state that our experience is vast and one of the best in the market, as we not only provide data labeling services but also own our data annotation platform. For clients, this means that we are flexible and can continuously adjust CVAT to make the annotation and validation process more efficient. Our clients can use the same platform internally and easily extend annotations. They also just log in to see how the data annotation process is going for them. ‍Without paying for anything., just try to annotate something, like millions of data scientists worldwide do. ‍But enough about us, let's see what annotation stages are there. ‍Stage 1: Annotation Proof of Concept (PoC)‍We will sign a Non-Disclosure agreement with the client to protect the data if necessary. We will request actual data samples (50-100 images or 1-2 videos) to start investigating it and see how it should be annotated.We will need the client's approved annotation specifications. At this stage, we will work together closely and ask questions to clarify corner cases and quality requirements. Following the efforts above, we will create a PoC and offer the precise project costs and durations.We will then send the client our proposal.‍We commit to initiating a PoC within one day of data reception and will provide detailed estimates and calculations within 3-5 days, depending on the project scope. Our initial project budget assessment is conducted with a high degree of accuracy. According to our experience, the final project cost typically deviates from the initial estimate by at most 10%.‍Stage 2: Documentation & Preparation‍Based on the conducted Proof of Concept (PoC), we will propose the most effective method for data annotation, refine and supplement the initial specification, and agree on the quality requirements and project annotation timelines.We will develop all the necessary documentation and sample agreements, including comprehensive information about our collaboration's terms and payment conditions. The client should only review the documentation and suggest any necessary revisions.Training the data annotation team is also entirely our responsibility. We will assign a dedicated manager who will be the direct and constant point of contact for resolving all operational issues and gathering all the necessary information about the project to build the training process for the annotation team.‍Document processing on our end will be completed within a week, barring any delays from the client. We immediately begin training and data annotation for expedited projects, bypassing bureaucratic delays.‍Stage 3: Annotation‍At this stage, we perform data annotation strictly following the instructions. However, we understand that requirements may change during the process, so we are always ready to be flexible and accommodate minor changes to the initial documentation.Since we understand that developing an AI model is a multi-step process, for large projects, we advocate delivering annotated data in batches without waiting for the entire dataset to be annotated. This approach allows our clients to conduct relevant experiments and adjust the process. The dedicated manager, responsible for the interim progress, will oversee the project from start to finish.We welcome regular feedback from the client and are ready to make additional revisions to the documentation as the project progresses to ensure the expected result.Typically, the most critical stage is annotating the first batch of data, during which all processes are fine-tuned, and the client's final requirements are understood. After successfully delivering the first batch of data, our team operates like a well-oiled machine, delivering high-quality results within the expected timelines.‍Most projects reach completion within one month.‍Stage 4: Validation‍We guarantee high-quality results to our clients because, before committing to specific obligations, we conduct experiments that help us understand the results we can deliver and how to improve them.‍We take full responsibility for a quality check; we can offer the following services for better results:‍Conduct manual and Сross Quality Assurance (QA), automate QA for Ground Truth (GT) annotation covering 3-10% of the dataset.Execute any final amendments at no additional cost and deliver a conclusive quality report.Compute and report quality metrics like Accuracy, Precision, Recall, Dice coefficient, and others, and provide a confusion matrix.Final validation and the conclusive report from our end will be completed within one week.‍Stage 5: Acceptance‍This is the final and best stage, where the client gets the final results.All that is left is to process payments and provide feedback regarding our labeling service.‍Following our previous article, in case there are no client delays and unexpected events, the whole process for the described project will take approximately 50 work days, 10 weeks, or 2.3 months. Of course, it depends on each case's requirements and circumstances.‍By entrusting us with your project, you commission a high-quality service with a pre-defined and documented guaranteed outcome. The client's role is limited to observing the process, accepting recommended changes from our side, reviewing the delivered data, and providing feedback on the results of the validated work. We take on all internal processes and guarantee the project's quality and timely delivery.‍Data Labeling Price‍Well, that’s a tricky question because the price heavily depends on the amount of data and the specific needs: the quality, the type of annotation, deadlines, and many more.‍Let’s use data publicly available online to estimate the cost of annotating 2,300,000 objects or 100,000 images. However, here's the issue—labeling service providers often lack transparency, and there aren't many published prices. Thus, we can only rely on fragments of information from sources like KILI Technology or Mindkosh to make our estimates. The number will usually be above $300,000 because semantic segmentation, used for this task, is one of the most expensive annotation types for now.‍But how much will it cost if the client comes to CVAT.ai? We used a flexible approach when we needed this amount of data to be annotated. Our pricing is built on the following assumptions:‍Estimation and Payment Models‍Per Object: This primary model charges for each data unit annotated—whether a frame, object, or attribute within an image or video. It suits projects with clearly defined unit sizes and quantities.Per Image/Video: Charges apply per image or video file processed, ideal for projects with consistent complexity or time demands per file.Per Hour: Costs are calculated based on the time annotators spend on the project, offering flexibility for projects with varying complexities or scope changes.‍Expected Project Budget Ranges‍$5K - $9.9K for Annotation Only, Manual, and Cross-Validation: This range is typical for projects focused on manual annotation, including thorough cross-validation for accuracy.Above $10K for Comprehensive Services: For budgets exceeding $10K, services extend beyond basic annotation to include AI engineer involvement, automated quality assurance, and potential custom AI solution development.The final cost of annotating 2,300,000 objects in CVAT.ai depends on the chosen approach. Using the "Per Object" method, the initial pricing begins at a set rate per unit. Due to the large volume, discounts ranging from 5% to 30% will be applied, reflecting our commitment to building long-term partnerships. By utilizing the highest discount tier, the total cost for annotating all objects will be approximately $225,400. This is an approximation, and the final price may vary based on the client's specific needs. Regardless of the exact cost, the results will be of the highest quality and delivered promptly.‍In general, you should expect the outsourcing price to be more than 1.5 times the cost of a potential in-house data annotation team. Hiring your data annotation team is one of the ways to achieve a better price while maintaining high quality. Read You hire annotators and annotate with your team for tips.‍Conclusion‍In summary, outsourcing your data annotation tasks to a professional service offers significant benefits in terms of time efficiency, quality assurance, and overall project management. While costs can vary based on project specifics, CVAT.ai provides a flexible pricing model that caters to different needs, ensuring high-quality results within a reasonable budget. With discounts available for larger volumes, we can offer competitive pricing without compromising quality.‍Next steps?‍Ready to label data with CVAT.ai? Email us: labeling@cvat.ai!Ensure you have all the necessary information—download our detailed takeaway now!‍‍‍Not a CVAT.ai user? Click through and sign up here‍Do not want to miss updates and news? Have any questions? Join our community:‍Facebook‍DiscordLinkedInGitterGitHub‍

In the first part of this series of articles, we emphasized the need for precise annotation of images and videos, essential for developing AI products capable of performing accurate analyses, making predictions, and delivering reliable outcomes. We focused on how time-consuming and money-consuming solo annotation might be.‍In this article, we will explore the costs and resources required to maintain an in-house team of data annotators.‍But before we jump into the topic, here's a reminder of our use case:‍A lead robotics scientist is creating a smart home assistant robot to differentiate between dirt and valuable items in a household setting. Life's chaos often includes scattered toys, misplaced glasses, pet fur, and god knows what else. The proposed robot would clean efficiently and help locate lost items, adding a layer of functionality beyond standard home cleaning devices. This can help elderly people keep track of their belongings, for example.‍As the lead scientist, you play a crucial role in this project. Along with your small research team, you've compiled a dataset of 100,000 images showing various room settings with items scattered on the floor. According to publicly available data, this dataset size is typical for robotics projects, which can range from thousands to millions of images.‍Each image features an average of 23 objects, so the task involves annotating approximately 2.3 million objects. This series presents various strategies to tackle this significant annotation task, including DIY methods, building an in-house team, outsourcing, and crowdsourcing.‍‍Welcome to part two of our series on the costs of data annotation. This article describes the cost of hiring annotators and building the annotation team yourself.‍Case 1: You handle the task yourself or with minimal help from colleagues.Case 2: You hire annotators and annotate with your team.Case 3: You outsource the task to professionals.Case 4: Crowdsourcing.‍Case 2: You hire annotators to annotate with your team‍Now, as with anything else on this planet, there are pros and cons to having an annotation team. Let's start with the advantages and address questions about the time required for annotation and the cost-effective impact of this approach.‍Here, we will calculate only the monthly expenses and costs. The minimum team to annotate 2.3 million objects consists of 35 annotators, supported by management personnel involved in onboarding, offboarding, and upskilling annotators.‍For these 35 annotators, one manager and 3-4 senior annotators are necessary to guide the team.‍Contracts and Team SizeData annotation teams vary in size from small (up to five members) to large groups, with larger teams requiring more coordination and management.‍Recruiting is straightforward for small teams, but complex for larger groups. Annotators may be full-time employees with fixed salaries or contractors. Contractors, however, pose challenges in retention and engagement due to their involvement in multiple projects and expectation for workload-aligned compensation.‍When working with contractors, as we do, extra effort is necessary to ensure availability. For instance, if you need 35 annotators, consider hiring between 60 to 70 to account for potential unavailability.Time and Costs‍From our experience the hiring process will take as much time as:‍Time to find a data annotation manager: 1 month or moreTime to find one annotator: Up to 1 monthTime to onboard one annotator: Up to 1 month‍You can conduct job interviews and onboarding concurrently. If you're fortunate, you might be able to hire between 5 to 10 annotators per month. But to hire and train a big data annotation team you need to have at least 3-4 months.‍Expenses wise it will be:Manager salary (per month): Up to $6000 (data from Indeed, June 2024)Annotators Salary (per hour): It depends on whether you can afford to hire abroad. If yes, starting from $1/h and up to $40 if you hire in the US or high level of qualification is required. Where to look for them? On Upwork, Indeed, LinkedIn—you name it. Again, the job posting price ranges from $0 to $500, in rare cases, with the help of the recruitment agency.Yes, if your service is as popular as platforms like CVAT.ai in the data annotation area, you can significantly reduce time and costs. Annotators will eagerly respond to your posted vacancies as soon as they are advertised.Set Up Time‍Next step is to prepare data: the dataset is the foundation of any robotics project.‍For this project, the scientist must sift through a vast collection of video footage to select relevant frames and then craft a comprehensive data annotation specification. In our case, this specification is planned to cover 40 different classes, each to be annotated with polygons individually. ‍On average, the complete guideline is 30-50 pages. It will include detailed instructions for annotating each class, examples of correct and incorrect annotations, and edge cases. Drafting this detailed specification is time-consuming; it might take several weeks. The data annotation specification will be updated during the project because it isn’t possible to describe all corner cases from the beginning.‍The time it takes to annotate each object with polygons will later be calculated, considering factors such as the object's complexity and size, the image's clarity, and the annotator's skill level.‍Simple Object (e.g., a rectangular object): 5-10 secondsModerately Complex Object (e.g., a car): 30-60 secondsHighly Complex Object (e.g., a human with detailed limb annotations): 1-3 minutes or more‍Operational Costs‍In addition to onboarding and training costs, the expenses for data annotation projects also include licenses and instance costs per annotator. Each annotator may require a license for the annotation software used, which can vary significantly in price depending on the complexity and capabilities of the software. ‍In the case of CVAT it will cost you $33 per seat or you can use the free open-source tools. ‍Remember that even free tools require time and resources to set up and support; time is money. So, while we say "free," it means that you can download and install the open-source tool, but the rest depends on your time, expertise, and effort (and how much of your paid time will be spent on this).‍Operational costs includes costs for accounting, contracts management and cannot be approximated, as they are company specific.‍Final calculations‍To calculate the total time required for 35 professional annotators to annotate 2,300,000 objects, where each object takes approximately 40 seconds in average to annotate, you can follow these steps:‍Calculate the Total Time for All Objects:‍Total time = 2,300,000 objects × 40 seconds per object = 92,000,000 seconds or 25,555.56 hours‍Divide by the Number of Annotators to Find Time per Annotator:‍Time per annotator = 25,555.56 hours / 35 annotators = 730.16 hours‍So, if all annotators work simultaneously and efficiently, each annotator will need about 18.25 work weeks, which is approximately 4.2 months, to complete the annotation of all 2,300,000 objects.‍To calculate the costs for the scenario described, let's break it down into its components and sum them up for the 4.2 months required for the project. We'll assume each annotator earns $550 per month and that there is a varying cost for licenses, from free to $33 per month. Additionally, management and validation cost is $6000 + 20% per month from the total cost of annotators.‍Total Salary Costs for Annotators (4.2 months): ‍Total annotator costs = $2,310 per annotator × 35 annotators = $80,850Management and validation Fees (for 4.2 Months):‍Total cost for a data annotation manager = $25,200Management and validation Fees = $80,850 * 20% = $16,170‍Conclusion: To annotate 100 000 images, that is 2,300,000 objects it will take 4.2 months and $122,220.‍To this number you need to add costs of the software licenses.‍Hidden and One Time Costs‍When calculating how much an annotation team costs it might be a good thing to take in account a one-time cost like hiring time and efforts. ‍As we’ve mentioned before, assembling a data annotation team starts with recruiting a crucial step that sets the tone for the team's development and effectiveness. Organizations typically choose between outsourcing recruitment or handling it internally.‍Time and Cost Estimates:‍Outsourcing RecruitmentTime: Recruitment agencies can expedite the process, typically taking 2 to 6 weeks to secure a position.Cost: Agencies charge a fee based on the position's annual salary, usually 15% to 30%.Internal RecruitmentTime: This method can take 4 to 8 weeks, depending on the efficiency of HR processes and candidate availability.Cost: Costs include job posting fees ($0 to $500) and internal HR labor (approximately $55,000 annually or $26 per hour). ‍The numbers provided are approximate and based on data from Indeed and LinkedIn; actual costs may vary and should be aligned with the company's internal processes. For example, at CVAT.ai, we have automated our hiring process, enabling us to recruit the best annotators on the market at competitive prices. We use Remote.com for onboarding candidates and are quite satisfied with this HR platform. Our annotators come from various countries, including Kenya, India, Nigeria, Ghana, Nepal, and Indonesia.‍Considerations for Hiring Relatives‍Small teams might consider hiring relatives for data annotation tasks. While this can add value in terms of trust and loyalty, it often leads to challenges such as the absence of professionalism and cost. Performance might not meet professional standards if the hiring criteria are not aligned with the job's technical demands.‍Management Overhead‍Post-recruitment, managing a data annotation team involves handling administrative tasks essential for maintaining AI development standards:‍Paperwork and Compliance: Managing contracts and compliance with labor laws.Financial Management: Overseeing accounts and payment systems.Work Environment Management: Providing training, managing workloads, and fostering a supportive work atmosphere.‍Additional Considerations‍Technology and Tools: Investments in data management and annotation tools can enhance efficiency.Team Dynamics: The interaction between team members and management style significantly impacts productivity.Market Conditions: Economic factors and labor availability can influence recruitment and operational costs.‍These elements are often seen as "hidden costs" and vary significantly by organization, affecting the overall expenses. They should be included in the final budget considerations due to their potential to impact the costs significantly.‍Conclusion‍What will be the total duration and costs of the entire project? Here, we are discussing the baseline minimal price, excluding hiring and hidden costs:‍Total Duration: 18.25 work weeks, which is approximately 4.2 months. Cost Range: The costs vary. They start from around $122,000 and may go up indefinetly, depending on team capacity and other factors, like where are you placed, do you hire locally or worldwide and so on. ‍What else should you take into account when reading this article?‍The calculation for the hiring process assumes linear and consistent recruitment and onboarding, which might not reflect real-world variations. Realistic scenarios may need buffer times for unexpected delays and additional costs for unplanned issues.The provided time and costs assume maximum efficiency. They may not account for variables such as sick leave, training efficacy, and turnover rates, which could significantly impact both time and cost estimates.Empirical data from similar past projects could further refine estimating the time taken to annotate objects and onboarding costs.‍Overall, the presented figures are reasonable but should be treated as approximations with potential for variation based on real-world execution.And that’s all for today. See you in the next article, where we will discuss how much it costs to outsource the data annotation to professionals.‍Not a CVAT.ai user? Click through and sign up here‍Do not want to miss updates and news? Have any questions? Join our community:‍Facebook‍DiscordLinkedInGitterGitHub‍

Creating computer vision AI systems requires meticulous training and fine-tuning of deep learning (DL) models using annotated images or videos. These annotations are crucial for developing AI products capable of accurate analysis, prediction, and generating reliable results. However, the process of image annotation significantly contributes to the overall cost of developing such systems. ‍"Instead of focusing on the code, companies should focus on developing systematic engineering practices for improving data in ways that are reliable, efficient, and systematic. In other words, companies need to move from a model-centric approach to a data-centric approach."— Andrew Ng, CEO and Founder of Landing AI‍How can you calculate the optimal price for image annotation to include in your budget? ‍We'll explore the various factors that influence the cost of image and video annotation. More importantly, we'll discuss why the price of image annotation should not be your only consideration when training and fine-tuning computer vision models.‍Prerequisites‍To better understand the dynamics of daily life, let’s consider a common scenario: life at home. ‍Most of us live in houses, often not alone but with families. These families can vary in size and composition—ranging from small units to large, bustling households with children, pets, and elderly members who require special attention and care.‍This variety can lead to issues that are relevant to all living areas: children might leave toys like LEGO pieces scattered on the floor, elderly individuals may misplace their glasses or other medical devices and struggle to find them, and pets could shed fur or leave other surprises around. All of these factors contribute to a household's everyday chaos.‍Certainly, several solutions are already available on the market, such as automatic vacuum cleaners and electric mops. However, let's consider the possibility that these devices might not be as smart as we need them to be.‍As a scientist leading a small research team, you aim to introduce an innovative product to the market—a smart home assistant robot. This advanced robot will differentiate between actual dirt and valuable items. It will clean up the former and signal the latter's presence, aiding in retrieving lost items. This functionality will not only keep homes cleaner but also make it easier to find misplaced objects.‍For research purposes, the scientist and their team have gathered a dataset comprising 100,000 images of various rooms with items scattered on the floor.‍‍The volume of 100,000 images comes from the average batch size we typically see in robotics projects. This number is supported by the available datasets in the public domain, where the quantity of images usually ranges from 10,000 to several million per dataset.‍Let’s assume that one image on average has 23 objects. So you need to annotate an average of 2,300,000 objects in total (or slightly fewer or more).‍This series of articles describes four cases on how to deal with such tasks:‍Case 1: You handle the task yourself or with minimal colleague help.Case 2: You hire annotators and try to build a team yourself. Case 3: You outsource the task to professionals. Case 4: Crowdsourcing‍Case 1: You handle the task yourself or with minimal help from colleagues‍A small disclaimer: annotating solo is fine for small amounts of data, but doesn’t work for big datasets. And here is why.The Annotation StageFor the robotics project, the scientist needs to select useful frames from the extensive video collection and create a detailed data annotation specification. Accurate and precise polygon annotations will be used to label objects in the images.‍Let’s assume, that according to the data annotation specification, 40 classes will be annotated using polygons, with each instance annotated separately. A basic description of how to annotate is necessary, noting that the full specification can take 30-50 pages and will include detailed instructions on how to annotate each class correctly with good, bad examples and corner cases. Writing a specification also requires time estimated in days and weeks.‍The time required to annotate an object using polygons can vary depending on several factors, including the complexity and size of the object, the clarity of the image, and the expertise of the annotator.On average, it can take anywhere from a few seconds to several minutes per object. Here are some general estimates:‍Simple Object (e.g., a rectangular object): 5-10 secondsModerately Complex Object (e.g., a car): 30-60 secondsHighly Complex Object (e.g., a human with detailed limb annotations): 1-3 minutes or more‍Detailed polygon annotations can take significantly longer for precise tasks, especially for objects with intricate details and irregular shapes.If the quality requirements permit, AI tools like the Segment Anything Model can be used to speed up the annotation process. However, for some tasks, these models often lack the precision needed and require extensive manual corrections.Let's focus on the task at hand. We are dealing with an image of a room scattered with small objects. Typically, a skilled annotator can label each object in about 40-50 seconds. However, since our scientists do not perform annotations daily, the expected speed of annotation in our case will be approximately 60 seconds (or 1 minute) per object.‍Now let’s talk about money and costs. It's important to note that sometimes people think that annotating themselves is cheap because they do not account for their time, which is paid time unless the annotation is done outside of working hours.Let's assume the robotics engineer is from the USA and annotation is done during working hours. We will research job postings on Indeed, the well-known job aggregator site, and then check the average salary before taxes.The average salary calculated from the data provided is approximately $42 per hour (for June 2024).‍All that's left is to add the cost of the annotation tool. This cost can be zero if the scientist is tech-savvy and can install a self-hosted solution. However, if that's not the case, the scientist will need a tool that may be free or cost some money.‍If you plan to annotate yourself or ask a colleague(s) to help you, so you can annotate as a small Team, in the case of CVAT it will cost you $33 per seat. ‍Here is a list of the most popular open-source data annotation tools that you can use for free*. ‍Remember that even tools that are free to download and install require time and resources to set up and support, and time is money. So, while we say "free," it means that you can download and install the tool, but the rest depends on your time, expertise, and effort (and how much of your paid time will be spent on this).‍Let’s sum it up:‍First, we calculate the total amount of hours that the scientist will need to annotate all objects:‍2,300,000 objects x * 60 seconds = 138,000,000 seconds.138,000,000 seconds / 3,600 = 38,333 hours (rounded to the whole number).‍In the best-case scenario, it will take:‍4,792 working days240 months or 20 years of one person‍If the scientist drops all other duties and dedicates 8 hours daily solely to annotation.‍The cost of the annotation will be:‍38,333 hours * $42 = $1,609,986 + cost of the tool on the top. ‍Note that the described approach lacks scalability. In the future, maintaining the dataset and addressing any emerging issues will be necessary. Additionally, deployment in a production environment typically requires a significantly larger volume of data. Of course, the engineer can ask colleagues to help, but it may reduce the time but not the cost.‍The Quality Assurance StageTo ensure quality assurance when annotating data independently, an automated system known as a "Honeypot" can be used.‍The Honeypot method is cost-effective but pretty time-consuming. It involves setting aside approximately 3% of your dataset, or about 3,000 images from a set of 100,000, specifically for quality checks.‍You will need to use a previously created specification that outlines your annotation requirements and standards. Annotate this selected subset of images yourself to serve as a benchmark. While this method saves time in the long run, it still requires an initial investment of time and resources to set up and perform these annotations, which translates to a monetary cost.‍***And that’s it. Feel free to leave any comments on our social networks, and we'll gladly respond. In our next update, we will answer the question of how much an in-house annotation team costs.‍‍Not a CVAT.ai user? Click through and sign up here‍Do not want to miss updates and news? Have any questions? Join our community:‍Facebook‍DiscordLinkedInGitterGitHub‍

In the dynamic field of computer vision, the data annotation process is fraught with challenges starting with selecting the right approach: ‍Outsource: When you are looking for the external team to annotate your data and often face issues with uncertain quality and the potential for scams, making it difficult to know whom to trust. Additionally, the quality of annotations from unknown providers frequently fails to meet expectations. It is also difficult to fix issues within annotations because third-party platforms are usually closed and require an expensive subscription for access.Internal team: On the other hand, relying on an internal team can also pose problems: onboarding, preparing required infrastructure, training of your data annotation team, takes considerable time, and without proper expertise, the process can be inefficient and prone to errors. Many teams rely on the Computer Vision Annotation Tool (CVAT) as the tool of choice, but only a few know how to use the data annotation platform properly.‍In both scenarios, there is the risk of missed deadlines, and these approaches can become costly without delivering guaranteed results.‍CVAT.ai Labeling Service stands out as a highly recognized data annotation service. CVAT.ai effectively addresses these issues, offering a reliable solution for your annotation tasks without the drawbacks typically associated with either of two approaches. By choosing CVAT.ai, you benefit from the expertise of a team that has developed one of the most popular open-source data annotation platforms for computer vision domain and has over 10 years of experience in the field. ‍This article answers four question:‍Why is CVAT.ai Labeling Service the best solution for your needs?Short and simple description of Labeling Project Stages with real numbers and timelines.What are the payment estimation models?Next steps?Why CVAT.ai?‍CVAT.ai labeling services are available to everyone, whether you are a small team requiring a little assistance within limited resources or a large company with extensive data to annotate.Below, we outline why CVAT.ai stands out among other annotation and labeling services.‍Securing Your Projects with Excellence‍We proudly own and develop the CVAT.ai, renowned as a leading data annotation platform in the computer vision field. Our modern and efficient tool supports all major data annotation scenarios and is compatible with a variety of data import/export formats.‍Mature Team and Flawless Project Management‍CVAT.ai team consists of seasoned professionals, each bringing years of expertise in data annotation to ensure that your projects are handled with the utmost proficiency and care. We prioritize direct communication, allowing you to engage with a dedicated manager for personalized service. ‍Qualified Annotators‍Our team consists of highly skilled annotators, trained and certified directly by us.They are distributed worldwide, and we select the best annotators from various countries, including Kenya, India, Vietnam, Pakistan, Nigeria, and others. With their extensive experience in data annotation, they handle your projects with expertise and precision, tailored to meet your specific needs and requirements.Scalability of the TeamOur infrastructure allows us to rapidly expand our team of annotators to meet the demands of any project size. Whether your project needs 5 or 200 annotators, we can adjust our team size to deliver high-quality results on time.‍We are qualified to train new annotators, ensuring they meet standards of quality and precision. This flexible scalability means we can efficiently handle significant increases in workload, guaranteeing that we always deliver high-quality results within your project timelines, regardless of the project's scope.‍High Quality of Annotations‍At CVAT.ai, we are committed to maintaining the highest quality of annotation across all projects. Our strict quality control measures ensure that every annotator achieves and upholds specified standards.‍We use advanced tools and methodologies to deliver precise, accurate, and consistent data annotations. ‍Automated QA‍To ensure top quality in our labeling services, we use Automated QA (Quality Assurance). CVAT.ai platform uses algorithms to check the annotated data automatically, comparing it against a set of correct answers (“honey pots”) to spot any errors quickly and evaluate annotation quality for a whole dataset statistically.‍This method boosts the accuracy of data annotation, cuts down on time and costs for manual checks, and is especially useful for large projects where checking everything by hand isn't practical.‍Commitment to Timeliness‍At CVAT.ai, we maintain high-quality standards and strict adherence to deadlines, which helps us manage urgent projects effectively. For perspective on our timelines: small projects usually take less than one month for annotation. For larger projects, we can adjust to requested deadlines by mobilizing a bigger team of annotators when necessary.‍Stages of the Annotation Project‍These stages represent a workflow designed to ensure high-quality results in data annotation projects, The workflow is based on the effective communication and collaboration between the customer and CVAT throughout the process. Stage 1: Annotation Proof of Concept (PoC) ‍From you: You can provide and sign an NDA with us, before we even start working, so your data and information is secure.Provide samples of real data (50-100 images or 1-2 videos)Provide initial specifications, and any additional useful information.‍From us: Conduct precise PoC annotationClarify any corner casesProvide accurate estimates of project costs and timelinesPresent a formal proposal.‍We are ready to launch a Proof of Concept (PoC) within one day after receiving the data and can provide an accurate estimate and calculations within 3-5 days, depending on the project. Typically, the final budget deviates from the initial estimate by no more than 10% in either direction.‍Stage 2: Documentation & Preparation‍From you:Correct and approve the Statement of Work (SoW)Send the data.‍From us: Prepare the final SoWFinalize all payment terms and annotation requirementsCalculate and agree about quality metricsAssign and train the Data Annotation (DA) team.‍It will take up to one week to process documents from our side, assuming there are no delays from your side. For urgent projects, we can begin training the team and annotating data at this stage, without waiting for the completion of bureaucratic procedures.‍Stage 3: Annotation‍From you: Address any concerns and communicate with a dedicated manager for any questions.‍From us: Perform the annotation in accordance with the approved specifications and deadlines Provide intermediate reports through a dedicated manager.‍Most projects are completed within one month.‍Stage 4: Validation‍From you: Check the provided data, collect comments to fix issues, and review provided metrics.‍From us:Conduct manual and cross Quality Assurance (QA) via tools, automate QA for Ground Truth (GT) annotation covering 3-5% of the datasetMake any final corrections for free and deliver the final quality report.Calculate metrics such as Accuracy, Precision, Recall, Dice coefficient, Intersection over Union (IoU) and others and provide a confusion matrix report.‍From our side, we will conduct the final validation and provide a final report within one week.‍Stage 5: Acceptance‍From you:Accept the annotations and reportsMake payments (for large projects, payments are preferred in multiple batches after completing each batch).Leave us a feedback about labeling service‍Payment estimation models‍Here is a detailed description of various estimation and payment models for CVAT.ai labeling services, elaborating on the methods and conditions:‍Different Estimation and Payment Models:‍Per Object (the main model): Billing is based on each unit of data annotated, such as per annotated frame, object, or attribute within an image or video. It's most effective for projects with well-defined unit sizes and quantities.Per Image/Video: Billing is based on each image or video file processed. This model is straightforward and suitable for projects where the complexity or time required per image/video is relatively uniform.Per Hour: Billing is based on the amount of time annotators spend on the project. This method is flexible and can adapt to varying project complexities and unexpected changes in scope.‍Expected Project Budget Limits:‍Start from $5K - $9.9K for Annotation Only, Manual and Cross Validation: This budget range is typically for projects focusing solely on manual annotation services, including detailed cross-validation to ensure accuracy and consistency.>$10K for Comprehensive Services Including AI Engineer Engagement, Automated QA: Projects exceeding $10K not only involve basic annotation but also include the engagement of AI engineers who contribute to more complex tasks such as setting up automated quality assurance processes and potentially developing custom AI solutions.‍Discounts:‍5-30% Depending on Data Volume: We are always open to offering significant discounts to both new and loyal customers, as we are committed to fostering long-term collaborations.‍These payment models offer flexibility to accommodate a wide range of projects, from straightforward image annotation to complex projects involving advanced AI technologies and extensive quality assurance. The pricing structure and discounts incentivize larger and longer-term engagements by providing cost benefits as project scopes increase.‍Next steps?‍Ready to label data with CVAT.ai? Click here to book a call and get started or send us an email!Ensure you have all the information you need at your fingertips—download our detailed takeaway now!Not a CVAT.ai user? Click through and sign up here‍Do not want to miss updates and news? Have any questions? Join our community:‍Facebook‍DiscordLinkedInGitterGitHub