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.
But when you outsource annotation, how you structure the collaboration matters just as much as who 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 project
- A subscription-based service
In 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 Service
A 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 model
- Your 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 works
- Project 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 & terms
We 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 & rules
One-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 Service
A 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 model
- Your 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 works
- Initial 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 & terms
Unlike 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 & rules
Unlike 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 Look
Now, 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.
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