Data Labeling RFP Template (Free): Requirements and Scoring

How to run a Data Labeling RFP

A data labeling evaluation is mostly about your data: what types you have, how much, how sensitive it is, and how precisely the labels are defined. Settle those first, then make vendors prove themselves on a sample of it. The example schedule below runs about 11 weeks from kickoff to contract award; add time if an external workforce needs a security review.

1. Define the labeling work (weeks 1-2). List the data types, annotation types and a draft ontology, and write or update your labeling guidelines. Record the numbers vendors will price against: items or hours of audio and video per month, average objects per item, number of annotators and reviewers, and how the volume varies. Set the quality target and how you'll measure it.
2. Assemble the buying team. Include the ML engineers and data scientists who use the labels, whoever runs labeling operations, data engineering for storage and pipelines, a domain expert who can rule on edge cases, and security, privacy, procurement and legal. Bring security and privacy in early if the data contains personal or confidential information, or if an outside workforce will see it.
3. Decide on software, services or both, and whether you need an RFI (weeks 2-3). Decide whether you're buying a platform, a labeling workforce or both (see below). If that's still open, or your long list mixes platforms and service providers, send a short RFI first and use the answers to build a shortlist of three to five. A two-week RFI adds about two weeks to the example schedule.
4. Tailor and issue the RFP (weeks 3-6). Delete data types and requirements that don't apply, adjust priorities, and remove the 'Managed services' lines if you're buying software only. Attach a de-identified sample dataset and your guidelines so vendors can price accurately. Give vendors three weeks to respond, with a written question-and-answer window in the first week.
5. Score the responses (weeks 6-7). Have each evaluator score independently against the rubric before comparing notes, and set aside any vendor that misses a Must-have. Check every 'yes' for whether the feature is generally available today, part of a services engagement, or still on the roadmap.
6. Run a pilot batch with two or three vendors (weeks 7-10). Give each vendor the same sample batch and guidelines, and hold back a gold set they don't see. Compare accuracy against the gold set, agreement between annotators, throughput, how quickly guideline questions are resolved, and whether the export loads into your training pipeline without rework. Call references with similar data types.
7. Negotiate and award (weeks 10-11). Use the pricing table to compare quotes on the same volume assumptions. Settle unit definitions, quality-based acceptance and rework, renewal caps and data export before you sign, not after.

Labeling software, managed labeling services, or both?

Data labeling purchases come in three shapes. With software only, you license a platform and your own staff or contractors do the labeling. With managed services, a vendor supplies the annotators, project management and quality checks, usually on its own platform, and you pay for labeled output or annotator time. Many buyers need both: a platform they control, plus workforce capacity from the same vendor or a separate one.

Your RFP should say which you're buying, because it changes the requirements, the pricing table and the scoring. This template works for all three. Lines that start with 'Managed services:' apply only when the vendor supplies the workforce; delete them for a software-only purchase. If you buy the platform and the workforce from different vendors, require the workforce provider to work in your platform, so you keep the ontology, labels and quality history if you change providers.

Questions that help you decide:

• Do we have, or want to build, an in-house labeling team, and who would manage it?
• Does the work need domain experts, such as clinicians, lawyers or native speakers, who are hard to hire for short projects?
• Are we allowed to show this data to an outside workforce, and in which countries?
• Is our labeling volume steady, or does it come in bursts that a managed workforce could absorb?
• Do we want to own the labeling tool and ontology so we can change workforce providers later?

Data Labeling RFP requirements

Each line is written to paste straight into your RFP. Must-have means a vendor that cannot meet it is out; Nice-to-have earns extra points. Change the priorities to fit your situation, and delete what doesn't apply.

Functional

Technical and architecture

Integration

Security and compliance

Implementation and support

Commercial and pricing

Questions to ask Data Labeling vendors

These questions are designed to separate vendors, not to collect brochure answers. Ask for evidence: a demo, a document or a reference.

1. Using our sample data, demonstrate a project end to end: ontology setup, pre-labeling with one of our models, consensus and review, and export into our training format. How long did the setup take, and who did it?
2. How does the platform calculate inter-annotator agreement for each annotation type we use (boxes, masks, entity spans, transcripts, rankings)? Show a report broken down by class and by annotator.
3. We split one class into two halfway through a project. Walk us through what happens to existing labels, how affected items are sent back for relabeling, and how exports show which ontology version each label was made under.
4. Which pre-labeling and AI-assist models are included, and can the platform use our own models? Where do those models run, and is any of our data sent to a third-party model provider?
5. Which of the LLM data tasks in our requirements (preference ranking, fine-tuning data, red-teaming, evaluation sets) are product features today, and which are delivered as custom services work? Show the task interface and the export format.
6. Managed services: who are the annotators (employees, contractors or crowd workers), where are they located, how are they vetted and trained on our guidelines, and can our work be limited to a named, dedicated team?
7. Managed services: how will you measure quality on our work, who builds and owns the gold set, and what happens commercially when a batch misses the agreed target?
8. How does an annotator raise an unclear item? Show how the question reaches our team, and how the answer is added to the guidelines and shared with every annotator on the project.
9. When the platform reads data from our storage, what does it copy or derive in your environment (thumbnails, video frames, embeddings, model outputs), where is that kept, and how is it deleted?
10. Which features are unavailable, delayed or priced differently in your VPC or on-premises deployment compared with the hosted service?
11. What is the largest project you support today in items, video length, point-cloud size and concurrent annotators, and what editor performance should we expect at that size?
12. Price this worked example: [number] images with an average of [number] objects each, [number] hours of audio, and [number] reviewer seats. Show how the price changes if the objects per image double.
13. Which features in your proposal are generally available today, and which are in beta or on the roadmap? Give dates for the roadmap items.
14. Provide a reference customer with similar data types and volumes, ideally one that moved to your platform or service from another labeling tool or from a fully in-house process.

Scoring rubric

Score each criterion from 1 to 5, multiply by its weight, and add the results. The weights below are a starting point; agree on your own before any proposals arrive so the scoring can't be bent around a favorite.

Scoring scale

• 5 Exceeds: meets every Must-have and most Nice-to-haves, shown in a demo or proof of concept, with a matching reference customer.
• 4 Strong: meets every Must-have and some Nice-to-haves, with clear evidence.
• 3 Adequate: meets most Must-haves; gaps have a credible workaround or a dated roadmap commitment.
• 2 Weak: misses one or more Must-haves, or the answer is vague.
• 1 Poor: does not meet the requirement, or no answer.

Frequently asked questions

What should a data labeling RFP include?

Your data types, volumes and sensitivity; the annotation types and a draft ontology; requirements for quality control, model-assisted labeling, workforce management, security, integration and export formats, each marked Must-have or Nice-to-have; whether you're buying software, managed labeling services or both; a pricing table that fits your pricing model; and the rubric you'll score with. Attach a de-identified sample dataset and your labeling guidelines so vendors can price and pilot accurately.

What is the difference between data labeling software and a data labeling service?

Data labeling software is the platform where annotation, review and quality measurement happen; your own team or contractors do the work. A data labeling service, often called managed labeling, supplies the annotators, project management and quality checks, and charges for labeled output or annotator time. Many vendors offer both. If you buy them separately, require the service provider to work in your platform so you keep the labels and quality history when you change providers.

How is data labeling priced?

Software is usually priced per seat, often with different rates for annotators, reviewers and administrators, or by usage such as data volume or compute for model-assisted labeling. Managed services are priced per label, per item or task, or per annotator hour, with rates that rise with complexity and expertise. Ask every vendor to price the same volume assumptions and to define exactly what counts as a billable unit, or the quotes won't be comparable.

How do you measure data labeling quality?

Use three measures together: accuracy against a gold set of items with known answers, inter-annotator agreement on items labeled by more than one person, and the rate at which reviewers reject or correct labels. Set the target and the measurement method in the RFP, and require the platform to report these measures by class and by annotator. For managed services, tie acceptance and rework to the same measures.

How long does a data labeling RFP take?

The example schedule in this template runs about 11 weeks from kickoff to contract award, including three weeks for vendor responses and three weeks for a pilot batch on your own data. Allow longer if an external workforce needs a security review, if you run an RFI first, or if your labeling guidelines aren't written yet.

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