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Label Studio

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Spec score 9.1 #1 of 28 in Image Annotation Software

What it is

Label Studio is an open-source data labeling tool for text, images, audio, video, time series, and multimodal data. It provides a configurable labeling interface, project templates, data management, and annotation import and export. Custom machine-learning backends can provide predictions, while Enterprise adds automated active-learning loops. Its integrations directory spans cloud storage, foundation-model APIs, computer vision, speech and audio, machine-learning frameworks, and infrastructure. Listed storage options include Amazon S3, Google Cloud Storage, Azure Blob Storage, Databricks, Redis, and local storage. APIs, a Python SDK, and webhooks support programmatic management and integration. Deployment options include on-premises or cloud installation using pip, Docker, Ubuntu, source, or Anaconda. Security documentation describes TLS for application-component interactions, HTTPS, and secured cookies. Enterprise is described as SOC 2 and HIPAA compliant and can be deployed in the cloud or on premises, with SSO and SCIM 2.0 support. Community Edition is free. Starter Cloud costs $99 a month, with each additional user at $49 a month; Enterprise pricing is custom.

Spec sheet

What it does
Label Studio is an open-source platform for data labeling, AI evaluation and human-in-the-loop workflows across many data types.Read 15 Oct 2026 · labelstud.io
Purpose
Label Studio is an open source data labeling tool for labeling multiple data types and connecting machine learning models for predictions or active learning.Read 29 Sep 2026 · labelstud.io
Deployment
Label Studio can be installed on premises or in the cloud using pip, Docker, Ubuntu, source, or Anaconda.Read 29 Sep 2026 · labelstud.io
Integrations
The integrations directory lists cloud storage, foundation model APIs, computer vision, speech and audio, ML frameworks, and infrastructure integrations.Read 29 Sep 2026 · labelstud.io
Security
The security documentation says application component interactions use TLS and the web app enforces HTTPS and secured cookies.Read 29 Sep 2026 · labelstud.io
Support
Community users are directed to Slack and GitHub issues, Starter Cloud includes a dedicated support portal, and Enterprise offers priority support with response time SLAs.Read 29 Sep 2026 · humansignal.com
License
The public Label Studio repository is released under the Apache-2.0 license.Read 15 Oct 2026 · github.com
Founded
HumanSignal is listed as founded in 2019.Read 15 Oct 2026 · linkedin.com
Maker
The company says HumanSignal was founded in 2019 and that Label Studio originated from its founding team’s work.Read 29 Sep 2026 · humansignal.com
APIs
The platform provides APIs, a Python SDK, and webhooks for programmatic management and integration.Read 29 Sep 2026 · labelstud.io
Audio features
Audio workflows include transcription, waveform or spectrogram annotation, speaker diarization and emotion recognition.Read 15 Oct 2026 · labelstud.io
Community scale
HumanSignal reports more than 250,000 Label Studio users, millions of labeled samples and more than 20,000 GitHub stars.Read 15 Oct 2026 · humansignal.com
Company headquarters and size
LinkedIn lists HumanSignal headquarters in San Francisco, California and company size of 51–200 employees.Read 15 Oct 2026 · linkedin.com
Company legal name
Crunchbase identifies the legal entity as HumanSignal, Inc., also known as Heartex and Label Studio.Read 15 Oct 2026 · crunchbase.com
Data handling
Input media can remain in external storage and be delivered through presigned URLs, while project settings and annotations are stored in the internal database or configured external stores.Read 15 Oct 2026 · labelstud.io
Data types
The editions support labeling text, images, audio, video, time series data, and multimodal data.Read 29 Sep 2026 · labelstud.io
Documentation depth
The documentation includes installation, Kubernetes and air-gapped deployment, storage, API, Python SDK, webhooks, frontend extension and troubleshooting sections.Read 15 Oct 2026 · labelstud.io
Enterprise compliance
The Enterprise offering is described as SOC 2 and HIPAA compliant and is available in cloud or on-prem deployments.Read 29 Sep 2026 · humansignal.com
Enterprise identity
Enterprise supports single sign-on with SAML or LDAP, and the security page also describes SCIM 2.0 support.Read 29 Sep 2026 · humansignal.com
Free trial
The Enterprise product is available through a free trial, and the pricing page offers a Starter free trial.Read 29 Sep 2026 · humansignal.com
Funding
HumanSignal says it has raised $30 million from Redpoint Ventures, Unusual Ventures, Bow Capital and Swift Ventures.Read 15 Oct 2026 · humansignal.com
GetApp review sentiment
GetApp reports that 100% of its two Label Studio Enterprise reviews are positive.Read 15 Oct 2026 · getapp.com
Image classification
The platform supports putting images into categories for image-classification projects.Read 15 Oct 2026 · labelstud.io
Labeling tools
The platform provides a configurable labeling interface, project templates, data management, and annotation import and export.Read 29 Sep 2026 · labelstud.io
Limit: documentation and SSO
A G2 reviewer asked for more documentation and reported challenges connecting some SSO providers.Read 15 Oct 2026 · g2.com
Limit: pricing and throughput
A G2 reviewer described pricing as high and cited a community-edition limit of one request per second plus a 5,000 prediction quota.Read 15 Oct 2026 · g2.com
Machine-learning integration
Models can supply predictions for pre-labeling and support continuous active learning.Read 15 Oct 2026 · labelstud.io
ML workflows
Custom ML backends can provide predictions, and Enterprise adds automated active learning loops.Read 29 Sep 2026 · labelstud.io
Multimodal work
Multimodal configurations can combine dialogue processing, OCR and time-series references to video or audio.Read 15 Oct 2026 · labelstud.io
NLP tasks
Built-in examples include named-entity recognition, question answering and sentiment analysis.Read 15 Oct 2026 · labelstud.io
Notable news
HumanSignal announced that Label Studio Enterprise became available in the AWS Marketplace on December 9, 2025.Read 15 Oct 2026 · humansignal.com
Object detection
Object-detection projects can use boxes, polygons, circles and keypoints.Read 15 Oct 2026 · labelstud.io
Object tracking
Video workflows can track multiple objects frame by frame.Read 15 Oct 2026 · labelstud.io
OCR and documents
Label Studio supports PDF and image OCR annotation for complex documents.Read 15 Oct 2026 · labelstud.io
Praise: integrated ML
A verified G2 reviewer praised the integrated machine-learning predictions and confidence dashboard.Read 15 Oct 2026 · g2.com
Praise: time savings and security
Another verified G2 reviewer said it reduces dataset-preparation time and provides good security.Read 15 Oct 2026 · g2.com
Public API
The REST API imports labeling data, exports annotations, connects machine-learning backends and synchronizes cloud-storage tasks.Read 15 Oct 2026 · labelstud.io
Python SDK
The Python SDK is installed with pip and provides classes for project management, task import, annotation handling, filtering and asynchronous operations.Read 15 Oct 2026 · labelstud.io
Rating on G2
3.5 out of 5 from 1 reviewRead 15 Oct 2026 · g2.com
Rating on GetApp
4.0 out of 5 from 2 reviewsRead 15 Oct 2026 · getapp.com
Rating-site feature coverage
GetApp lists access controls, AI or machine learning, annotations, API, collaboration, data import/export, data security, image and document labeling, integrations and quality control among the product features.Read 15 Oct 2026 · getapp.com
Security controls
Official security guidance describes TLS, HTTPS, secured cookies, role-restricted token API access, SSRF protection and encrypted PostgreSQL connections.Read 15 Oct 2026 · labelstud.io
Self-hosting
Community Edition can be installed with pip, Homebrew, Git or Docker and run locally or on infrastructure managed by the user.Read 15 Oct 2026 · labelstud.io
Semantic segmentation
Semantic-segmentation projects partition images into multiple labeled segments.Read 15 Oct 2026 · labelstud.io
Storage
Listed storage integrations include Amazon S3, Google Cloud Storage, Azure Blob Storage, Databricks, Redis, and local storage.Read 29 Sep 2026 · labelstud.io
Storage integrations
Documentation lists AWS S3, Google Cloud, Microsoft Azure, Databricks, Redis and local storage connections.Read 15 Oct 2026 · labelstud.io
Time series
Time-series projects support classification, segmentation and event recognition.Read 15 Oct 2026 · labelstud.io
Webhook limitation
Failed webhook deliveries are not retried, and connections time out after one second unless configured otherwise.Read 15 Oct 2026 · labelstud.io
Webhooks
Webhooks cover task, annotation and project events and can trigger training, active learning or dataset-version actions.Read 15 Oct 2026 · labelstud.io

Plans and prices

Community Edition Free open-source edition · self-hosted deployment Read 15 Oct 2026 · humansignal.com
Starter Cloud $99 a month $99 per month; $49 per month for each additional user up to 12 users · hosted service · support portal · RBAC · quality workflows · automated task distribution Read 15 Oct 2026 · humansignal.com
Enterprise Priced on request Custom pricing cloud or on-premises · SSO with SAML or LDAP · enhanced quality workflows and analytics · SOC2 and HIPAA compliant cloud or on-premises Read 15 Oct 2026 · humansignal.com

Every Label Studio plan and price →

Strong points and limits

Strong points

  • Supports labeling across six data types
  • Custom ML backends can provide predictions
  • APIs, Python SDK, and webhooks support integrations
  • Cloud or on-premises deployment options
  • Community Edition is free

Limits

  • Starter Cloud costs $99 a month
  • Each additional Starter Cloud user costs $49 a month
  • Enterprise pricing is custom and not listed

Who it suits

Label Studio suits teams labeling datasets across multiple media types, especially those connecting custom model predictions or programmatic workflows. Organizations needing Enterprise compliance, identity, or deployment options can consider its Enterprise offering.

Label Studio in depth

Read the full write-up

Overview

Label Studio is an open-source data-labeling platform for teams preparing varied data for machine learning. It suits projects that need configurable annotation across several media types and control over deployment; the trade-off is that advanced workflow, identity, and compliance needs point toward Enterprise.

Its broad format coverage and integration options make it a flexible foundation for annotation work, but teams should weigh the modest Starter Cloud seat ceiling against their collaboration needs and consider whether they need Enterprise features such as active-learning loops or SSO.

Key features

Annotation across data types

Label Studio supports text, images, audio, video, time series, and multimodal data. A configurable interface, project templates, data management, and annotation import and export give teams room to shape workflows rather than use a single fixed labeling setup. For image work, annotation options include rectangles, ellipses, keypoints, polygons, brush masks, and image classification.

The breadth is useful for teams handling mixed datasets under one platform. It is less compelling if the requirement is a narrow, purpose-built image workflow and no broader data coverage is needed.

Machine-learning workflows and integrations

Custom machine-learning backends can supply predictions to assist labeling; Enterprise adds automated active-learning loops. That makes the Community and Starter options suitable for prediction-assisted work, while teams seeking a more automated loop need to budget for Enterprise.

The integrations directory spans cloud storage, foundation-model APIs, computer vision, speech and audio, machine-learning frameworks, and infrastructure. Storage connections include Amazon S3, Google Cloud Storage, Azure Blob Storage, Databricks, Redis, and local storage. APIs, a Python SDK, and webhooks support programmatic management and integration. Export formats include JSON, JSON_MIN, CSV, TSV, COCO, Pascal VOC XML, YOLO, PNG, and NumPy, a useful range for moving annotated data into different workflows.

Security and deployment

Label Studio can be installed on premises or in the cloud using pip, Docker, Ubuntu, source, or Anaconda. The security documentation describes TLS for application-component interactions, plus HTTPS and secured cookies for the web app. Enterprise is described as SOC 2 and HIPAA compliant, and supports SAML or LDAP single sign-on and SCIM 2.0. Those are meaningful options for organizations with governance and identity requirements; smaller teams can avoid that level of deployment commitment.

Pricing

Label Studio uses a freemium model, with a free plan and a free trial. Community Edition costs nothing and is open source, with community support through Slack and GitHub issues. It is the natural starting point for individuals and teams comfortable relying on community channels; it does not include the hosted service or dedicated support offered on paid plans.

Starter Cloud costs $99 a month, billed $99 per month, with each additional user priced at $49 per month. It includes hosted service, up to 12 users, and a support portal. The 12-user cap makes the plan a fit for smaller teams that want managed hosting and a support route, but larger groups should compare its additional-user charges with Enterprise.

Enterprise has custom pricing and is available in cloud or on-prem deployments. It adds SSO with SAML or LDAP and highest-priority support with response-time SLAs; Enterprise also brings automated active-learning loops and is described as SOC 2 and HIPAA compliant. This is the plan for organizations needing those controls and commitments, not a default upgrade for teams that can work with the community edition or Starter Cloud.

Platforms

Label Studio supports API, Linux, macOS, self-hosted, web, and Windows. The range of installation methods and cloud or on-prem deployment makes it adaptable to different operating environments, though teams choosing self-hosting take on the deployment choice rather than using Starter Cloud's hosted service.

Who it's for

Label Studio is best suited to data and machine-learning teams that label multiple data types, need model predictions or storage integrations, and value deployment flexibility. Community Edition fits users who can work with open-source software and community support. Starter Cloud suits teams of up to 12 users that prefer a hosted service and support portal. Enterprise is the better fit when active-learning loops, SSO, compliance commitments, on-prem deployment, or response-time SLAs are central requirements.

It is less suited to teams that need the Enterprise controls but cannot justify custom pricing, or to groups that want managed hosting beyond Starter Cloud's 12-user allowance without moving to Enterprise.

Pros and cons

Pro: Support for six broad data categories and multimodal data lets teams bring varied annotation work into one platform.

Pro: Multiple export formats, APIs, a Python SDK, webhooks, and integrations with common storage options give technical teams several routes into existing workflows.

Pro: Community Edition is open source and costs nothing, while deployments can run on premises or in the cloud.

Con: Automated active-learning loops are reserved for Enterprise, so prediction assistance alone may not satisfy teams seeking automated iteration.

Con: Starter Cloud is capped at 12 users and charges $49 per month for each additional user, which can make growth more expensive.

Con: Enterprise pricing is custom, making it harder to assess the cost of its compliance, identity, and support commitments before engaging.

Alternatives

For image-focused shortlists, browse Image Annotation Software or AI Image Annotation Tools; for broader labeling comparisons, see Data Labeling Software and AI Data Labeling Tools. Teams concentrating on video can compare options in Video Annotation Software.

Roboflow is a freemium alternative with web, API, Android, iOS, and self-hosted platforms; its Free Tier includes 10 credits a month, described as enough for about 30 model trainings or 80,000 inferences. Consider it when those credit-based model and inference allowances better match the work.

Supervisely offers a free Community plan with 5 GB storage, 10,000 files, and two members, while its Pro plan starts at €199 a month. It may suit a small team that can work within those Community caps or wants a paid plan with a stated starting price.

CVAT has a free, MIT-licensed Community plan for personal use and small teams, as well as a CVAT Online Free plan limited to one member, one project, three tasks, and 1 GB. Choose it when a free, small-scale annotation setup is the priority.

Labelme is a desktop alternative for Linux, macOS, and Windows. Its Starter plan costs $49 as a one-time purchase, includes one year of updates and SAM2 and SAM3 annotation, but has no dataset toolkit; it is worth considering for desktop annotation when that trade-off fits.

Pixano is a free alternative available on API, Linux, macOS, self-hosted, web, and Windows platforms. FiftyOne is also free and spans those same platforms; its Team plan has custom pricing and includes stated user, compute, and deployment allowances. Labellerr offers a free Researcher Plan with 2,500 data credits, one seat, one workspace, and up to 10 projects for students and researchers. Ultralytics Platform is a freemium option whose Free plan includes 100 GB storage, 10 GB per upload, 100 models, three concurrent cloud training jobs, and three cloud deployments.

Verdict

Choose Label Studio if your team needs flexible annotation across data types, model-assisted labeling, and a choice of hosted or self-managed deployment. Its broad integrations and open-source Community Edition are strong reasons to start here; look elsewhere if you need Enterprise-grade controls on a clearly stated budget or a hosted plan without the 12-user ceiling.

Verdict

Label Studio offers broad data-type coverage and flexible deployment, with integrations for storage and machine-learning workflows. Check the edition-specific support, compliance, and pricing details to determine which deployment fits.

Where Specifiction places it

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