Software advice
Best AI Data Labeling Platforms in 2026
An AI data labeling platform is software, sometimes paired with a managed workforce, that lets teams annotate images, video, text, audio and other data so machine learning models can be trained, fine-tuned or evaluated. It covers task interfaces, review workflows and export formats. It is not a model host, and it is not a general data warehouse.
Quick answer
CVAT ranks first for open-source control and published seat prices. Label Studio fits teams that want a free edition and a flat hosted plan, and Kili Technology fits buyers who prefer a French vendor. Appen and Scale AI suit managed workforce projects.
A team buying a labeling platform is really choosing between running software and buying finished labels. Some vendors sell a tool you operate with your own annotators, others sell a managed contributor pool, and a few sell both. This ranking covers twelve vendors across those models. It judges what the vendor itself publishes: plan structure, whether a price is visible, data handling and certifications from trust pages, how easy it is to export and leave, and which kind of team fits. Prices were read from vendor pages in October 2026. We did not benchmark annotation speed or label accuracy, and several vendors publish no price at all.
The 12 best AI data labeling platforms in 2026, ranked
There is no score. Vendors can pay for more visibility on this website, for example a higher place in a ranking or more room on a page. Commercial relationships can also affect which products we feature and how prominently. How we rank.
| # | Tool | Best for | Free plan | Entry price |
|---|---|---|---|---|
| 1 | CVAT | Computer vision teams that want an open-source annotation tool they can also self-host. | Yes | $33 per user per month (Team, billed monthly) |
| 2 | Label Studio | Teams labeling mixed data types who want an open-source start and a hosted upgrade path. | Yes | $99 per month (Starter Cloud, up to 12 users) |
| 3 | Kili Technology | Teams that want a labeling platform plus optional managed annotators, from a vendor based in Paris. | Trial | Quoted |
| 4 | Encord | Teams working with video, DICOM or LiDAR who also want data curation and model evaluation. | No | Quoted |
| 5 | SuperAnnotate | Teams that want a customizable editor for images, video, text and audio plus a data operations service. | No | Quoted |
| 6 | Dataloop | Data engineering teams that want annotation, pipelines and model hosting in one platform. | No | Quoted |
| 7 | Toloka | AI labs and enterprise teams that need expert-ranked data and RLHF from a managed contributor pool. | No | Quoted |
| 8 | Labelbox | Frontier labs and large enterprises that want expert labelers and training environments from one vendor. | No | Quoted |
| 9 | Appen | Buyers who want a managed multilingual workforce for speech, text and alignment data. | No | Quoted |
| 10 | Scale AI | Large AI builders that need expert human data, RLHF and evaluation at high volume. | No | Quoted |
| 11 | Roboflow | Computer vision teams that want annotation, training and hosting in one credit-based account | Yes | $0 (Free Tier), or $39 per month for Core |
| 12 | Segments.ai | Robotics and autonomous driving teams labeling 3D point clouds together with camera images | Trial | Core is a custom quote starting at 3,600 labeling hours per year ($2.67 per hour) |
- 1
CVAT
CVAT takes first place because its core is MIT-licensed, so a team can run the same tool on its own servers for nothing or pay for the hosted CVAT Online. The published plan table lists image, video, 3D and audio support, honey pot and ground truth quality checks, and an API. The hosted tiers cap projects and tasks, and the workforce is yours to find.
Strong on
- Community edition is MIT-licensed on GitHub, so exit costs little if you self-host
- Pricing page lists per-seat prices and plan limits for projects, tasks and storage
- Quality tools such as ground truth jobs and honey pots appear on Team plans
Weak on
- Hosted plans cap tasks at 750 for the small team tier, so large backlogs push you to Enterprise
- Labeling workforce and project management are left to you, with no managed annotators in the plan table
Skip it if: Teams that want annotators supplied under one contract or need LLM preference ranking and evaluation workflows. Yearly billing is $23 per user. A free plan covers 1 to 2 members; Enterprise is a self-hosted quote.
- 2
Label Studio
Label Studio ranks second because HumanSignal publishes both an Apache-2.0 community edition and a flat-priced hosted Starter plan, which few rivals do. The configurable labeling interface handles several data types, and the Enterprise tier adds SSO, LLM-as-a-judge and auto-labeling. Larger teams hit the 12-user cap on Starter and must move to a custom quote with sales.
Strong on
- Community Edition is Apache-2.0 on GitHub, with a flat hosted Starter plan beside it
- Configurable labeling interface plus ML backend hooks let teams pre-label with their own models
- Enterprise cloud or on-prem is described as SOC 2 and HIPAA compliant
Weak on
- Single Sign-On, activity logs and SOC 2 reporting are listed only for Enterprise
- Starter is capped at 12 users, so larger teams must talk to sales
Skip it if: Teams that want a managed workforce to do the labeling, since the platform is software first. Each extra user is $49 per month. The Community Edition is open source; Enterprise is quote only.
- 3
Kili Technology
Kili Technology sits third as the only entrant here with a French company behind it and a published trial limit: 100 assets for text, documents and images and 5 for video. The Grow plan adds up to 20 seats, 50,000 assets and an API with a Python SDK. Pricing beyond the trial is custom, and managed labeling is an add-on.
Strong on
- Pricing page states trial limits in seats and assets, so the test scope is clear
- Add-ons include dedicated labeling services and on-premise deployment for Enterprise
- Supports video, image, text, OCR, geospatial and audio modalities
Weak on
- No public per-seat or per-asset price exists beyond the free trial
- Grow caps seats at 20 and assets at 50,000 before a custom Enterprise contract applies
Skip it if: Buyers who need a published price list or a permanently free tier for small projects. Free trial: 2 to 5 seats, 100 text, document or image assets. Grow and Enterprise are custom.
- 4
Encord
Encord ranks fourth because the pricing page splits the product into annotation, Index for data curation and Active for model analytics, each with its own data-volume tiers. Starter, Team and Enterprise plans are listed, with US and EU deployment and VPC or on-premises options. Prices are not published, and several data types, including 3D and LiDAR, are paid add-ons.
Strong on
- Plan table covers annotation, curation and model evaluation in one product
- Offers US and EU deployment, plus private VPC and on-premises options
- Segment Anything Model 2 tracking and data agents appear in the plan comparison
Weak on
- No price is printed for Starter or Team, so budgeting needs a quote or an account
- Geospatial, ECG, 3D and LiDAR data types are listed as add-ons rather than included
Skip it if: Buyers who want a transparent per-seat price list before talking to anyone. Starter and Team plans exist with self-serve sign-up, but prices are not shown on the page. Enterprise is by contact.
- 5
SuperAnnotate
SuperAnnotate lands fifth: its pricing page lists the same multimodal editor on every tier and separates plans by compute hours, SSO, a customer success manager and consulting. Its security page names SOC 2 Type II and ISO 27001:2022. No price appears for any plan, so the first real number comes from a sales conversation, and a team comparing budgets cannot do it from the page.
Strong on
- The editor covers image, video, text and audio on every plan, including Starter
- Security page lists SOC 2 Type II, ISO 27001:2022 and a GDPR privacy program
- Enterprise adds a dedicated solutions engineer and AI data operations consulting
Weak on
- No price is published for any tier, so comparison shopping needs sales contact
- SSO and a dedicated Slack channel are missing from the Starter tier
Skip it if: Small teams that want to see a monthly price and sign up without a call. Starter, Pro and Enterprise tiers are listed without prices. Orchestrate compute hours rise from 1K to 10K by tier.
- 6
Dataloop
Dataloop ranks sixth because its site presents annotation as one module of a wider stack: data management, pipelines, model hosting, a marketplace and human feedback for GenAI validation. The vendor lists GDPR, ISO 27001, ISO 27701 and SOC 2 Type II. Pricing is not public, and the platform is broader than a pure labeling buyer may need.
Strong on
- Pipelines and model hosting sit beside annotation, so one tool covers data to inference
- Platform page cites ISO 27001, ISO 27701, SOC 2 Type II and GDPR
- Marketplace and SDK access are listed in the site navigation
Weak on
- No price or free tier is published, and the pricing URL returned a not-found page
- Scope is wider than labeling, which adds setup effort for teams that only annotate
Skip it if: Teams that only need a simple annotation tool with a public price. No pricing page was available when checked, so only a demo request is offered. Contact the vendor for a quote.
- 7
Toloka
Toloka sits seventh because it sells human data more than software: preference data, instruction tuning, model evaluation and egocentric video collection, with an agent that sets up the pipeline from a plain description. The company is a Dutch B.V. with ISO 27001, ISO 27701 and SOC 2 claims. No price is published, and some countries are restricted.
Strong on
- Covers RLHF, instruction tuning, evaluation and data collection from one platform
- Lists ISO 27001 and ISO 27701 certification plus private or on-premises storage options
- Contracting entity is Toloka AI B.V. in Amsterdam, per its privacy notice
Weak on
- No public price list, and the pricing path on the site returned a not-found page
- A separate Eligibility and Geographic Restrictions document limits where the platform can be used
Skip it if: Teams that want to label their own data in-house with software only. No pricing page was found; the site offers demos and a self-service agreement, with enterprise quotes by contact.
- 8
Labelbox
Labelbox ranks eighth because its current homepage leads with environments for frontier labs and agents for engineering teams, with Alignerr, a network of skill-tested experts, supplying the human judgment. The security page covers AES-256 encryption at rest and TLS in transit. A buyer who wants only an annotation tool will find the message has moved elsewhere.
Strong on
- Alignerr expert network is described as 2.6M+ skill-tested experts across 200+ domains
- Security page documents AES-256 at rest, TLS 1.2+ in transit and Auth0 sign-in
- Treats all customer data submitted as confidential, per its security program page
Weak on
- Homepage now centers on environments and agents, so the classic labeling product is less visible
- No public price list was reachable when checked
Skip it if: Small teams that want a self-serve annotation tool with a visible monthly price. The pricing page did not load when checked. The site points to talking to the team for access.
- 9
Appen
Appen ranks ninth because it is a managed human-data vendor first and a platform second, with its AI Data Platform (ADAP) handling annotation. The homepage lists RLHF, agent trajectories, speech corpora and LiDAR annotation across 500+ locales, and states SOC 2 and ISO 27001 certification. It is ASX-listed and headquartered in Sydney. Pricing is by project quote only.
Strong on
- Covers 500+ locales, which suits multilingual speech and text collection
- States SOC 2 and ISO 27001 certification on its homepage
- Offers off-the-shelf datasets as well as custom collection and annotation
Weak on
- No self-serve plan or published price, so every project begins with a scoping conversation
- Product focus is data services, so a team wanting only tooling pays for more than it needs
Skip it if: Teams that want to run annotation themselves on a low-cost seat license. No public price list exists; products are scoped as custom data projects or via its AI Data Platform.
- 10
Scale AI
Scale AI is tenth here because the buyer profile is narrow: its Data Engine page covers text, image, video and LiDAR annotation, RLHF, red teaming and model evaluation, and the homepage says 25% of its contributors hold advanced degrees. Pricing is not stated, a demo is the entry point, and its privacy policy gives a San Francisco address.
Strong on
- Data Engine page lists annotation across text, images, video and 3D sensor fusion
- Offers RLHF, red teaming and model evaluation alongside labeling
- Homepage says contributors are sourced with a share holding advanced degrees
Weak on
- No pricing or self-serve plan is published, so small projects face a sales process
- Product emphasis has shifted toward applications and frontier data, not a lightweight labeling tool
Skip it if: Small teams or startups that need a cheap annotation tool they can start using today. The site shows Book a Demo and Get Started only, with no prices or plan table.
- 11
Roboflow
Roboflow ranks eleventh because its labeling is one part of a wider computer vision workflow. Label Assist and Smart Polygon speed up image annotation, and the same account trains and hosts models. Credits fund both, so heavy labeling and heavy inference compete for one balance. Review Mode and Labeling Analytics sit in higher tiers. Teams that need video, audio or text annotation at scale should look at the vendors above.
Strong on
- Free Tier and Core both include AI-assisted labeling and a hosted API
- Self-hosted inference on your own hardware is free on every plan
- Security page lists SOC 2 and HIPAA compliance for the platform
Weak on
- New workspaces allow data licensing and model training on private projects until an admin switches it off
- Review Mode and labeling analytics are not part of Core
Skip it if: Teams labeling text, audio or large multi-modal sets, or those needing review stages on a lower tier. Plans run on credits. Free includes 10 a month, Core $39 includes 20, and credits do not roll over.
- 12
Segments.ai
Segments.ai ranks twelfth because it serves a narrower job than the general platforms above it. It labels 3D point clouds, camera images and multi-sensor sequences in one interface, with model-assisted labeling and unlimited seats, and its trust page shows ISO 27001 and TISAX. Pricing is by labeling hours and quoted, and no text, audio or document tools are listed. For perception data it is a focused option, and for anything else it is the wrong fit.
Strong on
- Plans include unlimited seats, projects and API or SDK access
- Point clouds and camera images share one labeling interface
- About page shows ISO 27001 and TISAX certification logos
Weak on
- Every plan is a custom quote tied to annual labeling hours
- Modalities listed are images and 3D sensor data only
Skip it if: Teams labeling text, audio or documents, or buyers wanting a permanent free plan. All three plans are quoted. A 14-day trial and a free academic license are offered, and seats are unlimited.
How these AI data labeling platforms were judged
How do labeling platforms keep label quality high?
Quality control is built from review stages, consensus between annotators, and ground truth checks, and the vendors differ in what they list. CVAT names manual review, ground truth jobs and honey pots on its Team plans. Encord lists consensus workflows and annotator performance dashboards. Kili and SuperAnnotate describe review workflows too. Managed vendors such as Appen and Toloka lean on vetted contributors and expert reviewers instead. We did not measure label accuracy, so use each vendor's trial or pilot to compare error rates on your own sample before signing a contract.
What does a data labeling platform cost?
Only a few vendors print a price. As of October 2026, CVAT Online lists $33 per user monthly or $23 on yearly billing, and Label Studio Starter Cloud costs $99 per month with $49 for each added user up to 12. Kili publishes trial limits only. Encord, SuperAnnotate, Dataloop, Toloka, Labelbox, Appen and Scale AI quote privately. Managed labeling is usually priced per project or per label, which is a different budget line from software seats. Ask for both the platform fee and the per-label rate.
Where does my training data go and who can see it?
Data location and access depend on deployment. Encord offers US and EU deployment plus VPC or on-premises installs. CVAT Enterprise and Label Studio can run on your own servers, and Kili lists on-premise as an Enterprise add-on. SuperAnnotate, Dataloop, Toloka and Appen cite SOC 2 or ISO 27001 on their own pages. Managed workforces add a second risk, because contributors see your raw data. Read the data processing terms, ask how contributors are screened, and whether your data trains anyone's models.
How hard is it to move labels to another platform?
Lock-in grows with workflow customization more than with the labels themselves. Open-source CVAT and Label Studio keep the codebase public, which lowers the risk of a vendor disappearing. Hosted plans can still hold your project setup, and exports vary by tier: CVAT's free plan exports annotations only, while paid plans add images with annotations. Check that you can export in common formats like COCO or JSON, and keep your ontology file outside the tool. Managed vendors lock in people and process, not formats.
Which labeling platform fits my team size and data type?
Fit starts with who does the labeling. A small computer vision team with its own annotators fits CVAT or Label Studio. A team handling video, medical imaging or LiDAR and wanting curation can look at Encord. A buyer who needs EU contracting can shortlist Kili Technology, based in Paris, or Toloka, a Dutch B.V. A lab buying RLHF or evaluation data will find Appen, Scale AI and Toloka built for volume. Start by deciding whether you want software, finished data, or both.
How to choose an AI data labeling platform in three steps
- Decide who will do the labeling. Write down whether your own staff, an outsourced team or a vendor's contributor pool will annotate. If it is your staff, a tool with a visible seat price like CVAT or Label Studio is the cleaner fit. If you want finished data, you are buying a service, and Appen, Toloka or Scale AI quote by project. A mixed model, where a platform vendor also supplies labelers as an add-on, is available from Kili. Confirm this before you ask for demos, because it changes which contract you sign and who is liable for quality.
- Run the same pilot on three vendors. Pick a sample of 200 to 500 items that includes your hard cases, then label it on two or three shortlisted platforms with the same instructions. Compare time per item, reviewer disagreement and how many tool limitations got in the way. Use free trials where they exist, such as Kili's, or the open-source editions of CVAT and Label Studio. A pilot costs days, while a wrong platform costs months, and the vendors without public pricing will usually agree to a short paid test when asked directly.
- Check export, security and exit terms. Before signing, ask for a sample export in the format your training code reads, and confirm you can pull data out without a fee. Read the security page for SOC 2, ISO 27001 and deployment choices, and get written answers on whether your data is retained after the contract ends. For managed vendors, ask how contributors are vetted. Keep ontologies and instructions in your own repository so a switch means rerunning a pilot rather than rebuilding your whole labeling setup.
Frequently asked questions
What is the best AI data labeling platform in 2026?
CVAT ranks first here because its core is MIT-licensed, its hosted plans list a per-seat price and it supports images, video, 3D and audio. Label Studio ranks second for a similar open-source start with a flat hosted plan, and Kili Technology third. The best choice still depends on whether you annotate in-house or buy finished data, so pilot two or three.
Are there free data labeling platforms?
Yes. CVAT Community and Label Studio Community Edition are open source and can be run on your own servers at no license cost. CVAT Online also lists a free plan for 1 to 2 members, and Kili offers a free trial with a 100-asset limit for text, documents and images. Free versions usually limit users, tasks or quality tools.
How much does a data labeling platform cost?
The published prices are low for software: $23 to $33 per user per month for CVAT Online and $99 per month for Label Studio Starter, as of October 2026. Enterprise tools from Encord, SuperAnnotate and Dataloop are quote-based. Managed labeling from Appen, Toloka or Scale AI is priced per project, so total spend depends on data volume and complexity.
What is the difference between a labeling tool and a managed labeling service?
A tool is software your own annotators use. A managed service supplies the annotators too, plus project management and review. CVAT and Label Studio are tool-first, while Appen and Toloka are service-first, and Kili, Labelbox and Scale AI sell combinations. The practical difference is who carries the quality risk and how pricing scales with volume.
Which labeling platforms can be self-hosted?
CVAT Community and Label Studio Community Edition can be installed on your own infrastructure, and CVAT Enterprise is described as self-hosted. Encord lists VPC and on-premises deployments, and Kili lists on-premise deployment as an Enterprise add-on. Toloka mentions private and on-premises storage options. Self-hosting suits strict data residency, at the price of running upgrades yourself.
Is there a European data labeling platform?
Kili Technology SAS is a French company with a Paris address in its privacy policy, and Toloka AI B.V. is registered in Amsterdam. Encord lists both a UK limited company and a US incorporated company in its footer. Whether a European entity fits depends on data location, so confirm where annotation happens and where data is stored.
Can I use a labeling platform to evaluate LLMs and collect RLHF data?
Several can. Toloka lists RLHF, preference data and model evaluation, Appen lists RLHF and red teaming, Scale AI lists RLHF, red teaming and evaluation, and Label Studio Enterprise lists LLM-as-a-judge. Open-source image tools like CVAT focus on computer vision, so check the vendor's workflow templates for text comparison before choosing.
Do labeling vendors train on my data?
The answer sits in each vendor's data terms, and we did not verify a no-training clause for every vendor here. Labelbox's security page calls customer data confidential, and SuperAnnotate cites independent audits. For managed vendors, ask directly whether your data or labels enter any shared dataset, and get the answer in the contract or data processing addendum.