Top AI Data Annotation Platforms In 2027

Meta Description: Compare the top AI data annotation platforms in 2027, from Labelbox and Encord to CVAT and Label Studio, by data types, automation, deployment and pricing.

Training data is still the part of a machine learning project that decides whether a model ships. Foundation models are widely available, but the labeled images, point clouds, documents, audio and preference judgments that teach a model your task are not. The tooling a team uses to create and manage that data shapes how fast labels get produced, how consistent they are, and how easily problems can be traced back to the source. A good AI data annotation platform combines annotation editors with model-assisted labeling, review workflows, dataset curation and an API that fits into the rest of the ML stack.

Teams usually face three options. They can build internal labeling tools, which gives full control but pulls engineers away from model work. They can buy a platform and run it with their own annotators or domain experts. Or they can outsource the labeling itself to a managed workforce. Many teams mix the last two, keeping sensitive or expert work in house while sending volume work to a vendor. If you want to outsource the labeling itself, see our list of data annotation and labeling companies, which covers service providers rather than software.

This list is for ML engineers, data operations leads, and computer vision and NLP teams choosing the software layer. We reviewed vendor websites, documentation and pricing pages for each platform and selected ten that cover commercial SaaS, open source and self-hosted options across image, video, LiDAR, text, documents, audio and LLM evaluation work.

AI Data Annotation Platforms At A Glance

Platform	Headquarters	Best For	Key Features
Labelbox	San Francisco, USA	Enterprises combining tooling with expert human data	Catalog, Annotate and Model modules, Foundry model-assisted labeling, Alignerr workforce
Encord	London, UK and San Francisco, USA	Multimodal and physical AI data pipelines	SAM 2 segmentation and tracking, DICOM and LiDAR support, RLHF and rubric evaluation
SuperAnnotate	San Francisco, USA	LLM evaluation and custom annotation interfaces	Drag and drop UI builder, LLM judges, arena comparisons, multi-stage workflows
V7 Darwin	London, UK	Medical imaging and video annotation	SAM 2 auto-annotate, video object tracking, conditional workflow logic
CVAT	Palo Alto, USA	Open source computer vision labeling	MIT licensed Community edition, SAM 2 and SAM 3 tools, 3D point clouds
Label Studio (HumanSignal)	San Francisco, USA	Flexible open source labeling across data types	Apache 2.0 license, ML backend for pre-labeling, LLM as judge evaluation
Kili Technology	Paris, France	Expert-in-the-loop labeling and evaluation	Consensus and honeypot QA, SAM segmentation, Kubernetes self-hosting
Roboflow	Des Moines, USA	Computer vision teams going from labels to deployment	Smart Select with SAM, Label Assist, Auto Label, edge inference
Supervisely	Tallinn, Estonia	Self-hosted computer vision platforms with custom apps	App ecosystem, SAM 2 Smart Tool, LiDAR and DICOM toolboxes
Segments.ai	Belgium	Multi-sensor robotics and autonomous vehicle data	3D cuboids projected to 2D, cuboid propagation, SAM-based segmentation

Best AI Data Annotation Platforms In 2027

1. Labelbox — Catalog, Annotate And Model Modules With The Alignerr Expert Network

Labelbox was founded in 2018 by Manu Sharma and Brian Rieger and is headquartered in San Francisco. The company now describes itself as a data factory for generative AI, pairing its labeling software with on-demand expert labeling through Alignerr, a skill-tested expert community that Labelbox says spans more than 200 domains. For teams that want to run labeling themselves, the core platform has three parts. Catalog connects data from more than 25 cloud sources and supports vector and traditional search for exploration and curation. Annotate provides more than ten built-in editors covering images, video, text, documents, audio, geospatial data, conversational text, HTML, LLM human preference, prompt and response generation, and multimodal chat evaluation. Model lets teams import predictions, run error analysis and compare training experiments.

Foundry brings foundation models into the workflow, including model-assisted labeling where an LLM generates annotations against a defined ontology. Projects include a configurable workflow tab for multi-step review, along with consensus, benchmarks, issues and a performance dashboard. On security, Labelbox states it maintains SOC 2 Type II certification and HIPAA compliance, encrypts data with AES-256 at rest and TLS 1.2 or higher in transit, and lets customers keep assets in their own cloud storage using signed URLs or delegated access. Pricing runs across Free, Starter and Enterprise tiers, with model-assisted labeling and custom workflows starting at Starter and fully managed human data services in Enterprise. Labelbox suits larger organizations doing LLM post-training or multimodal evaluation that want software and an expert workforce from one vendor.

2. Encord — SAM 2 Video Tracking Across DICOM, LiDAR And Audio In One Workflow

Encord went through Y Combinator’s Winter 2021 batch and has dual headquarters in London and San Francisco. It was founded by Eric Landau and Ulrik Stig Hansen and now positions itself as the data infrastructure layer for physical AI and enterprise teams. The platform covers annotation, data indexing and curation, post-training alignment and cloud integrations, and Encord describes it as API and SDK first. Supported modalities include images, video, DICOM and NIfTI, 3D and LiDAR, documents and text, HTML, audio and ECG, with sensor fusion handled in the same workflow.

SAM 2 is built into the annotation tools for detecting, segmenting and classifying objects in images, and it can segment and track objects across video frames. Video is annotated natively rather than split into frames. Teams can also plug in LLMs or their own models to automate labeling pipelines. Workflows support role-based task assignment, multi-stage review and dashboards for project progress and labeler performance, and ontologies allow nested classifications. On the curation side, Encord offers embedding-based search and model-in-the-loop curation for finding edge cases. For generative AI work, it supports RLHF, rubric-based evaluation and pairwise comparison.

Encord lists HIPAA compliance, SOC 2 certification and GDPR compliance, and its pitch is that data stays in the customer’s cloud. Plans are Starter, Team and Enterprise, with VPC and on-prem deployment offered at the enterprise level. It is a strong fit for medical imaging, robotics and autonomous systems teams handling mixed sensor data.

3. SuperAnnotate — Drag And Drop Interface Builder For LLM And Agent Evaluation

SuperAnnotate is a San Francisco company founded by brothers Tigran Petrosyan, the CEO, and Vahan Petrosyan, the CTO, building on segmentation algorithms Vahan developed during his PhD research. The platform started as labeling software and has expanded into dataset management, evaluation and fine-tuning support. Its central idea is one multimodal editor for RLHF preference data, supervised fine-tuning, images, video, audio, LiDAR and custom modalities.

The standout feature is the custom interface builder. Teams compose annotation screens from components such as rich text, rating scales, bounding boxes, audio players and code diffs, add conditional logic, or describe the interface in plain language and let an agentic assistant generate the layout. For automation, models pre-label items, LLM judges grade outputs against rubrics, and low-confidence items are routed to human experts. Workflows support consensus, review layers and disagreement handling, with triggers that fire on platform actions. A model-agnostic connection layer supports GPT, Claude, Gemini, Llama, Mistral, Qwen and custom fine-tunes, and blind side-by-side arena comparisons update Elo ratings as votes come in.

SuperAnnotate states it holds a SOC 2 Type II report and ISO 27001:2022 certification, with encryption in transit and at rest. Pricing runs across Starter, Pro and Enterprise tiers, all quote-based, with image, video, text and audio editors in every tier and SSO from Pro upward. It fits teams building evaluation and post-training pipelines for LLMs and agents that need interfaces tailored to each task.

4. V7 Darwin — SAM 2 Auto-Annotate With Native DICOM And Microscopy Support

V7 was founded in London in 2018 by Alberto Rizzoli and Simon Edwardsson, and the company is headquartered in London. Its product line now has two halves: V7 Go, an LLM-based platform for document-heavy workflows launched in 2024, and V7 Darwin, the annotation platform covered here. Darwin handles images, video, DICOM and NIfTI, microscopy, PDFs, architectural drawings and RLHF tasks, which makes it one of the few tools on this list with a long track record in medical and life sciences imaging.

AI-assisted labeling uses V7 Auto-Annotate and SAM 2 for segmentation, and an auto-track feature follows objects across selected time ranges in video. Teams can connect external or out-of-the-box models to pre-label data or flag quality issues. The workflow builder supports multi-stage review with conditional logic and automations that route data to specific stages or team members, along with consensus stages and blind tests that compare human and model performance. Annotations export in formats compatible with frameworks such as TensorFlow and PyTorch, and the platform offers an SDK, API and storage integrations.

V7 states it is GDPR, HIPAA, SOC 2 Type II and ISO 27001 compliant, and customers can use private cloud storage with AWS, Google Cloud and Azure. Pricing is custom: an annual quote combines a base platform fee, user licenses and volume-based data processing, with demos available before purchase. Darwin suits healthcare, life sciences and video-heavy teams that want polished tooling and configurable QA routing.

5. CVAT — MIT Licensed Open Source Labeling With SAM 2 And SAM 3

CVAT began inside Intel in 2017, when engineers Nikita Manovich and Andrey Zhavoronkov started building it, and its first public release landed on GitHub in 2018. In July 2022 it spun out from Intel as an independent company, CVAT.ai Corporation, which is listed in Palo Alto, California. The Community edition remains open source under the MIT License and is one of the most widely used self-hosted labeling tools, with roughly 17,000 GitHub stars.

CVAT covers images, video, audio and 3D point clouds, with boxes, polygons, masks, keypoints and other shapes. AI-assisted labeling supports SAM 2, SAM 3, Ultralytics, Hugging Face and custom models, and automatic annotation can run through serverless functions built on Nuclio. Quality control includes review, consensus comparison, and Ground Truth and Honeypot checks, plus Grafana-based analytics. It imports and exports more than 20 formats including COCO, YOLO and Pascal VOC, connects to S3, Azure and Google Cloud storage, and offers a REST API, Python SDK and CLI.

There are three ways to run it: CVAT Online, a managed cloud with Free, Solo and Team plans; CVAT Enterprise, self-hosted with SSO, RBAC and support; and the free Community edition. Note that SAM tools in CVAT Online require a paid plan. The company also sells managed labeling services. CVAT is the default choice for computer vision teams that want an open source foundation with a commercial upgrade path.

6. Label Studio — Apache 2.0 Open Source Tool With An ML Backend For Pre-Labeling

Label Studio is developed by HumanSignal, the San Francisco company formerly known as Heartex, founded in 2019. HumanSignal calls Label Studio the most popular open source data labeling tool, and the Community edition is released under the Apache 2.0 license. It installs through Docker, Docker Compose, pip and other routes, which makes it easy to stand up on a laptop or inside a private network.

Label Studio’s strength is breadth. Configurable templates cover images, audio, text, HTML, time series, video and PDFs, and the Enterprise product adds support for sensor outputs and 3D data. The ML backend lets teams connect their own models for pre-labeling, online learning during annotation and active learning, so annotators focus on the hardest examples. Enterprise adds automated pre-labeling with custom LLMs, agreement workflows for resolving annotations from multiple experts, LLM as judge evaluations, plugins with hooks for data transformation, and rubric-based scoring of model versions. An API, Python SDK and webhooks handle project creation and prediction streaming, with connectors for Azure, Databricks, Google Cloud and AWS S3.

HumanSignal offers three tiers. Community is open source and self-hosted. Starter Cloud is a hosted plan for small teams with a cap on users. Enterprise runs in the cloud or on-prem and adds SAML or LDAP single sign-on, SCIM, role-based access, activity logs, SOC 2 and HIPAA compliance and an uptime SLA. Label Studio suits NLP and multimodal teams that want one flexible open source tool they can customize and later move to a supported enterprise version.

7. Kili Technology — Consensus And Honeypot QA With Self-Hosting On Kubernetes

Kili Technology was founded in Paris in 2018 by François-Xavier Leduc and Édouard d’Archimbaud. The company positions its platform around expert-in-the-loop AI evaluation, post-training and data annotation, with the argument that domain specialists catch model errors that automated benchmarks miss. Supported data types include images, video, text, OCR and document layout, geospatial imagery, audio for diarization and transcription, and LLM fine-tuning and evaluation tasks.

Quality management is where Kili is most detailed. Consensus has several labelers work on the same asset and scores their agreement, with configurable coverage. Honeypot assets with trusted reference labels measure how closely each labeler matches known-correct work. Multiple review levels can be stacked with their own sampling rates, and step separation is enabled by default so one person cannot both label and review the same asset. Quality Insights breaks results down by labeler, class and job. For automation, Kili supports importing model-generated pre-annotations, active learning with queue prioritization, and the Segment Anything Model for interactive semantic segmentation on images. Developers use a Python SDK, GraphQL API and CLI.

Kili displays HIPAA, ISO 27001 and SOC 2 certifications. Deployment options include Kili’s hosted cloud with a choice of regions, cloud with customer-owned storage, and self-hosting on the customer’s own Kubernetes as an Enterprise add-on. Pricing runs from a Free Trial with asset limits to Grow and Enterprise plans on custom terms. Kili is a good match for European enterprises and regulated industries where audit trails and measurable label quality matter.

8. Roboflow — Smart Select, Label Assist And Auto Label Feeding Edge Deployment

Roboflow was founded in 2019 by Brad Dwyer and Joseph Nelson and is headquartered in Des Moines, Iowa. It covers more of the computer vision lifecycle than most tools here, taking teams from labeling through training to inference on images, video and streams in the cloud or at the edge. It also maintains open source projects including supervision, Inference and RF-DETR.

Roboflow Annotate offers three levels of automation. Smart Select creates polygon annotations in one click using Meta’s Segment Anything model. Label Assist uses a team’s own trained models to pre-annotate new images. Auto Label uses foundation models to label large batches at once. Supported tasks include object detection, instance segmentation, keypoint detection, classification and multimodal annotation for fine-tuning vision-language models. Augmentation, dataset search and analytics support curation, and team workflows cover job assignment, review, approval and role-based access. Teams can bring their own labelers, outsourced vendors or Roboflow’s labeling services.

Pricing is credit-based, with a Free Tier, a Core plan and custom Enterprise pricing, and self-hosted inference is free on every plan. SOC 2 is listed for Core and Enterprise, while SSO, audit logs, US or EU data sovereignty and a HIPAA add-on with a BAA come with Enterprise. On-prem installation and air-gapped deployment are available as enterprise services. Roboflow suits product teams and manufacturers who want annotation tightly linked to model training and deployment.

9. Supervisely — Hundreds Of Ecosystem Apps With Self-Hosted Kubernetes Installs

Supervisely is registered as Supervisely OÜ in Tallinn, Estonia. Its founders, Denis Drozdov, Maxim Kolomeichenko and Yuri Borisov, ran a consulting business from 2013 before releasing the platform in August 2017, based on internal tools they had built to speed up their own machine learning work. Supervisely is designed as a platform rather than a single application, and its ecosystem includes hundreds of apps for labeling, quality assurance, import and export, training and inference that run inside Docker containers.

Data types include images, multiview and multispectral images, video, 3D point clouds and point cloud episodes, and DICOM and NRRD volumes, with dedicated toolboxes for each. The image toolbox offers bounding boxes, polygons, brush, mask pen, keypoint graphs and a Smart Tool based on SAM 2, ClickSEG and other models. Teams can apply a deployed model during annotation, batch pre-label an entire project, and train and serve models such as YOLO directly from the platform by connecting their own GPU machines through agents. A developer portal, SDK and AppEngine support custom apps and model integration.

Supervisely has three tiers: a free Community plan with storage and team limits, a Pro plan with a 30-day trial, and Enterprise. Only Enterprise covers on-prem deployment, private SaaS, Kubernetes or AWS EKS installs and offline secure environments, along with remote storage such as S3. Security features include encryption, SSO through LDAP, Active Directory and OAuth2, and role-based access, and the company describes itself as SOC 2 aligned and GDPR compliant. It fits computer vision teams that want a self-hosted environment they can extend with their own tools.

10. Segments.ai — Multi-Sensor Labeling That Projects 3D Cuboids Onto 2D Images

Segments.ai is a Belgian company founded in 2020 by Otto Debals and Bert De Brabandere, and a Y Combinator Winter 2021 alumnus. In October 2025 Uber announced it had acquired Segments.ai, and the team joined Uber AI Solutions to continue building annotation tooling for robotics and autonomous systems. The platform is still sold under the Segments.ai by Uber brand with a free trial.

Segments.ai focuses on multi-sensor data for robotics and automotive. Teams label point clouds and camera images in a single interface, and a 3D cuboid can be projected onto every 2D image where the object appears. Point cloud tools include cuboids, segmentation, keypoints and polylines, plus batch mode, merged point clouds and tiling for large scans. ML-assisted features include automated cuboid propagation, Superpixel 2.0, Autosegment and hover-and-click segmentation built on the Segment Anything Model. Teams can upload predictions from their own models as pre-labels and correct them, and a Python SDK and API support export to PyTorch, TensorFlow and Hugging Face.

Plans are Core, Fusion and Enterprise, each on a custom quote tied to annual labeling hours. Core includes unlimited seats, cloud bucket integrations, active learning and QA tools. Fusion adds sensor fusion interfaces, webhooks and access to a labeling workforce. Enterprise adds SSO, MFA and custom security arrangements. Free academic licenses are available. The site lists ISO 27001 and TISAX, which matters for automotive suppliers. Segments.ai is the specialist pick for AV, robotics and drone teams.

How To Choose An AI Data Annotation Platform

Does The Platform Support Your Data Types And Annotation Tools?

Start with the data you have today and the data you expect next year. A platform built for 2D images may handle video only as extracted frames, and many tools treat LiDAR, DICOM or long documents as an afterthought. Check for native editors for each modality, the specific shapes and task types you need (polygons, cuboids, keypoints, relations, transcription, pairwise preference), and whether one project can combine sensors or must be split. Pilot with your own hardest files, not the vendor’s sample dataset.

How Much Automation And Model-Assisted Labeling Do You Need?

Most platforms now include Segment Anything style click-to-segment tools, but the depth varies. Look at whether you can plug in your own models for pre-labeling, whether active learning can prioritize the most informative samples, and whether video tracking propagates labels across frames. For LLM work, check support for LLM judges, rubric scoring and routing low-confidence items to humans.

Will You Bring Your Own Workforce Or Use A Managed One?

Some teams have in-house annotators or domain experts such as radiologists or lawyers. Others need a vendor to supply people. Labelbox, Encord, CVAT, Roboflow and HumanSignal offer services alongside software, while others are mainly tooling. Either way, check the quality controls: consensus scoring, honeypots or ground truth sets, review sampling and per-labeler metrics. These matter more than raw labeling speed at scale.

What Deployment And Security Options Does Your Data Require?

Regulated data often cannot leave your environment. Confirm whether the platform can read directly from your own cloud buckets, run in a VPC or on-prem, or work in an air-gapped network, and which plan unlocks each option. Ask for current SOC 2 reports, ISO 27001 certificates or HIPAA business associate agreements rather than relying on badges. Open source options such as CVAT Community and Label Studio Community give full control over infrastructure, but your team then owns patching, scaling and access management.

How Do Integrations And Pricing Fit Your Stack?

A labeling platform sits between storage, training and evaluation, so the API and SDK matter as much as the editor. Check native connectors for S3, Google Cloud Storage, Azure and data platforms such as Databricks, plus webhooks and export formats like COCO and YOLO. Pricing models vary widely: per seat, per labeling hour, credit-based usage or annual enterprise quotes. Model your expected volume and team size against each vendor’s structure, and confirm which features, such as SSO or AI-assisted tools, are held back for higher tiers.

Conclusion

The AI data annotation platforms on this list cover very different needs. Labelbox, Encord and SuperAnnotate lead for enterprise multimodal work and LLM evaluation, with Labelbox and SuperAnnotate leaning hardest into post-training and expert data. V7 Darwin and Encord are strong for medical imaging and video, Segments.ai is built for multi-sensor robotics and automotive data, and Roboflow connects labeling to deployment for computer vision products. Kili stands out for measurable quality and European deployment options, while CVAT, Label Studio and Supervisely give teams open source or self-hosted foundations they can control.

The best way to decide is a short paid or trial pilot with two or three shortlisted tools, using your own data, your own reviewers and a clear quality metric. Measure throughput, label accuracy against a ground truth set, and how easily results flow into your training pipeline.