How we make this list: we compare pricing pages, product documentation and public user reviews. Ratings come from G2, Capterra or Trustpilot, with the date we checked them.
Key takeaways
- Scale AI and Appen suit large, long-running programs, while Labelbox and SuperAnnotate suit teams that want to run labeling in-house with platform tools.
- iMerit and Sama suit high-stakes or managed projects where domain expertise and quality control matter more than the lowest per-label price.
- CloudFactory and TELUS Digital suit enterprises that need a managed workforce at scale, including multilingual and document work.
- Most managed pricing is quoted by project, so a pilot with agreed quality measures is the best way to compare vendors fairly.
Quick comparison
| Tool | Best for | Starting price | Free plan |
|---|
 Scale AI | Enterprise and 3D sensor labeling | See website | No |
 Appen | Multilingual data at large scale | See website | No |
 Labelbox | In-house teams using a platform | See website | Yes |
 iMerit | Regulated and expert annotation | See website | No |
 SuperAnnotate | Computer vision teams needing tooling | See website | No |
 CloudFactory | Managed workforce for documents and images | See website | No |
 Sama | Managed labeling with ethical sourcing | See website | No |
 TELUS Digital | Large multilingual AI data programs | See website | No |
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The best data annotation companies
Starting priceSee website
Free planNo
Scale AI is one of the largest annotation providers, with a focus on autonomous vehicles, robotics, mapping, and defense work, alongside generative AI data. Its platform combines automation with human review, and it handles complex sensor data that many smaller vendors cannot manage at scale. For a large team labeling LiDAR sequences or building a long-running training pipeline, that depth is a real advantage.
The weaknesses are price and sensitivity. Enterprise contracts can start at a level that excludes startups, and Meta's large investment in Scale AI in 2025 led some buyers to ask harder questions about data separation. Scale fits best for large enterprises and government-linked teams with complex 3D or multimodal needs, provided they confirm data handling, pricing, and workforce terms in a written agreement.
What we like- Strong tools for LiDAR, 3D, and sensor data
- Model-assisted labeling plus human review
- Enterprise-grade workflow and quality controls
Watch out for- Enterprise pricing puts it out of reach for small teams
- Ownership changes in 2025 raised data sensitivity questions for some buyers
- Custom pricing makes early comparison hard
Starting priceSee website
Free planNo
Appen, founded in 1996, is one of the oldest providers in this field, with more than one million contributors and coverage across many languages and locales. Its strengths are speech and audio annotation, text work, search evaluation, and large multilingual programs that need many annotators quickly. For a global product team, that breadth is hard to match with a single smaller vendor.
The catch is consistency. A large crowd needs strong calibration, gold-standard examples, and sampling checks, or quality will vary across languages. Appen has also faced restructuring in recent years, so buyers should confirm the team and capacity for their specific program. Appen fits best for large, multilingual, or speech-heavy programs where scale matters and where the buyer will set clear quality measures from the start.
What we like- Very large global contributor pool
- Strength in text, speech, search evaluation, and multilingual work
- Long history in AI training data
Watch out for- Recent years brought revenue pressure and restructuring
- Quality can vary by language and task without tight controls
- Pricing is project-based and quoted
#3Labelbox
Best for: In-house teams using a platform4.5/5 on
G2 (48 reviews, checked Oct 2026)
Visit labelbox.com
Starting priceSee website
Free planYes
Labelbox suits teams that want to keep labeling in-house but need better tooling than spreadsheets and shared folders. The platform covers image, video, audio, text, and point cloud annotation, with model-assisted labeling, review workflows, and dataset versioning. That control appeals to engineering teams that already have annotators or domain experts and want to manage the process themselves.
The weakness is total cost. A platform subscription is only part of the spend if you also pay for a managed workforce or for internal reviewer time. Smaller teams may also find that advanced features require a higher tier. Labelbox fits best for engineering-led teams with in-house annotators who want a flexible platform and who will budget separately for labor and review.
What we like- Platform for image, video, audio, text, and point cloud data
- Model-assisted labeling and dataset management
- Managed labeling services available alongside the platform
Watch out for- Workforce costs sit separately from platform fees in some plans
- Enterprise features require a paid plan
- Teams need internal staff to run quality checks
Starting priceSee website
Free planNo
iMerit focuses on managed annotation where accuracy matters more than speed alone. Its teams cover computer vision, healthcare imaging, geospatial data, and other domains where annotators need training and where errors carry real costs. Structured quality controls, including review rounds and agreement checks, help buyers who must defend a dataset to regulators, clinicians, or safety reviewers.
The trade-off is cost and fit. Expert annotation is not the cheapest option, and a very small, short pilot may not get the same attention as a long program. iMerit fits best for enterprises in healthcare, geospatial, or autonomous systems that need managed, expert labeling and that will define quality targets and audit steps before work starts.
What we like- Domain expertise in healthcare, geospatial, and autonomous systems
- Managed workforce with structured quality controls
- Experience with regulated and high-stakes data
Watch out for- Project pricing is quoted, so early budgeting is hard
- Enterprise focus may not suit very small pilots
- Timelines depend on expert availability for niche tasks
#5SuperAnnotate
Best for: Computer vision teams needing tooling4.8/5 on
G2 (382 reviews, checked Oct 2026)
Visit superannotate.com
Starting priceSee website
Free planNo
SuperAnnotate combines an annotation platform with managed services, with particular strength in computer vision. Video frame handling, polygon and instance segmentation, and model-assisted labeling help vision teams move faster than manual drawing alone. Active learning features can also help a team focus human effort on the examples that most improve a model, rather than labeling everything at the same intensity.
The limit is breadth. Teams whose main need is speech transcription, long-form text, or expert language work may find a better fit elsewhere. Pricing for larger plans is quoted, so a small team should confirm what is included before assuming a low entry cost. SuperAnnotate fits best for startups and mid-size vision teams that want strong tooling and the option to add managed labeling later.
What we like- Strong computer vision tooling, including video and segmentation
- Model-assisted labeling and active learning features
- Managed services available for teams without annotators
Watch out for- Platform strength is centered on vision more than speech or text
- Pricing is quoted for larger plans
- Smaller teams may need time to learn advanced features
Starting priceSee website
Free planNo
CloudFactory provides a managed workforce for human-in-the-loop labeling, with a focus on document processing, data extraction, and image tasks for enterprises in finance, insurance, and logistics. The model suits buyers who want to hand over workforce management, training, and quality checks, rather than hiring and managing annotators directly. Scaling up or down with program volume is part of the appeal for long-running work.
The weakness is that managed work still needs good task design. Unclear guidelines, weak examples, or rushed training will produce weak labels no matter how structured the workforce is. Custom pricing also means budgeting requires a scoping discussion. CloudFactory fits best for enterprises that need a managed team for document or image programs and that will invest time in guidelines and pilot review.
What we like- Managed, trained workforce with structured quality processes
- Strength in document processing, data extraction, and image work
- Flexible scaling for long programs
Watch out for- Custom pricing requires a scoping call
- Quality depends on task design and training time
- Less suited to teams that want a self-serve platform alone
#7
Sama
Best for: Managed labeling with ethical sourcing
Visit sama.com
Starting priceSee website
Free planNo
Sama, formerly Samasource, offers managed data annotation and enrichment with an emphasis on ethical sourcing and workforce development. Its teams handle structured annotation across image, text, and other data types for enterprise clients. For buyers who care about how annotators are recruited, trained, and paid, Sama gives a clearer story than a pure crowd marketplace.
The catch is scale and flexibility. Sama is built for enterprise programs, so a very small or one-off task may not be its best fit. Pricing is quoted by project, and timelines depend on scope. Sama fits best for enterprises that want managed labeling at scale and that value ethical sourcing alongside accuracy and delivery reliability.
What we like- Managed labeling with a focus on ethical workforce practices
- Reliable structured annotation and data enrichment
- Experience with enterprise-scale programs
Watch out for- Enterprise focus limits small-project flexibility
- Pricing is project-based and quoted
- Turnaround depends on program scope and workforce capacity
Starting priceSee website
Free planNo
TELUS Digital, which absorbed Lionbridge AI, offers managed AI data services at large scale, with strength in multilingual annotation, speech recognition evaluation, and large structured datasets. Its size suits global programs that need many languages, many annotators, and consistent delivery over months. Evaluation and search-related work add a second use case beyond basic labeling.
The weakness is fit for small teams. Enterprise programs bring process and scale, but they can also bring longer onboarding and less flexibility for a short pilot. Pricing is quoted, so early budgeting needs a scoping call. TELUS Digital fits best for large enterprises running multilingual or speech-heavy AI data programs that need a managed workforce at scale.
What we like- Very large global workforce for multilingual work
- Strength in speech, language, and large structured datasets
- Managed services plus evaluation work
Watch out for- Enterprise scale can mean slower setup for small buyers
- Pricing is quoted by program
- Quality control needs clear guidelines across languages
How to run a pilot that actually compares vendors
Start with a small, representative sample from your own data, not a vendor demo set. Include hard cases, edge cases, and the exact guidelines your team will use in production. Score each vendor on accuracy against gold-standard answers, agreement between annotators, turnaround time, and communication quality. A vendor that is fast but needs heavy rework is not cheaper than a slower vendor that delivers clean labels first time.
Set quality and security terms in writing
Agree on quality measures, sampling rates, review steps, and what happens when quality falls below target. Confirm security controls, such as SOC 2 or ISO 27001 certification, data access rules, and whether data leaves your region. For healthcare or other sensitive data, confirm handling rules and any need for de-identification before data is shared.
Compare total cost, not per-label price alone
Per-label price hides platform fees, workforce management, review time, and rework. Add platform subscription, managed service fees, internal review hours, and the cost of fixing errors. A pilot that tracks all four gives a fairer total than a single quoted rate.
How we chose
We compared company websites, product documentation, service ranges, security certifications, and public reviews, with weight given to firms that handle the data types they claim at real scale. We included platform-led vendors and workforce-led vendors, plus large and mid-size firms, and we left out firms where public information did not support a current, verifiable service. We did not claim hands-on testing, and we treated quoted pricing as quoted, not as a published price list.
Frequently asked questions
What is data annotation?
Data annotation is the process of adding labels to raw data so a machine learning model can learn from it. Examples include drawing boxes around objects in images, transcribing speech, classifying text, and labeling points in 3D sensor data.
Should I choose a platform or a managed service?
Choose a platform if you have in-house annotators and want control over tooling and workflows. Choose a managed service if you want the vendor to recruit, train, and manage annotators and quality checks. Some vendors, such as Labelbox and SuperAnnotate, offer both options.
How much does data annotation cost?
Most managed pricing is quoted by project, based on data type, volume, complexity, and quality requirements. Platform pricing may be subscription-based, with workforce costs separate. Run a pilot to get a total cost that includes review and rework.
How do I check annotation quality?
Use gold-standard examples, measure agreement between annotators, sample completed work for review, and track error types over time. Agree on quality targets and rework rules before a large program starts.
What security controls should I look for?
Look for SOC 2, ISO 27001, and GDPR compliance where relevant, plus clear data access rules, retention terms, and regional handling. For sensitive data, confirm de-identification and access controls in writing.
Can a small team use these companies?
Yes, but fit varies. Labelbox and SuperAnnotate suit smaller in-house teams, while Scale AI, Appen, and TELUS Digital suit larger programs. Start with a small pilot and confirm minimums before signing a long agreement.
Sources we checked (8)
- https://www.scale.com
- https://www.appen.com
- https://www.labelbox.com
- https://www.imerit.net
- https://www.superannotate.com
- https://www.cloudfactory.com
- https://www.sama.com
- https://www.telusdigital.com
Is your product missing or out of date?
We review this list regularly. Vendors can request a listing, send updated pricing or ask about featured placement.
Shihab Mia · Founder, Rankite
Shihab has 5+ years in B2B and SaaS SEO. He writes Rankite's software buying guides from pricing pages, documentation and public user reviews. More about Shihab.