Labelbox Vs Encord: Comparing Two Data Annotation Platforms

Meta Description: Labelbox vs Encord compared: data types, AI-assisted labeling, curation, RLHF, workforce options, security, deployment and published pricing tiers side by side.

Labelbox and Encord are two of the platforms AI teams shortlist most often when they outgrow spreadsheets, open source labeling tools and one-off vendor contracts. Both offer hosted labeling, curation and review, both sell managed labeling services, and both have moved into model evaluation and post-training data. The overlap is real, but their centers of gravity differ.

Labelbox has leaned hard into human data for frontier model builders, with a large expert network, reinforcement learning environments and agent products sitting next to its labeling platform. Encord now describes itself as a multimodal data layer for physical AI, with deep tooling for video, LiDAR, sensor fusion and medical imaging, plus flexible deployment options for regulated buyers.

If you want a wider field first, see our ranking of the top AI data annotation platforms and our list of top data annotation and labeling companies.

What Labelbox Is

Labelbox was founded in 2018 and is headquartered in San Francisco, according to its company page. TechCrunch’s 2019 coverage named Manu Sharma (CEO), Dan Rasmuson (CTO) and Brian Rieger (COO) as the founding team. Its about page lists investors including Kleiner Perkins, Andreessen Horowitz, SoftBank Vision Fund and Databricks. Its 2022 Series D of $110 million was led by SoftBank Vision Fund 2. In October 2026, Dealroom reported that Labelbox raised more than $100 million in a Series E combining equity and a credit facility, led by Infinitum Partners.

The core software platform covers Catalog (data search and curation), Annotate (labeling with more than 10 built-in editors), Model (predictions and error analysis) and Foundry (foundation models used for pre-labeling and preference data). Around that platform, Labelbox now sells Alignerr, its expert network, Horizon (RL environments and evals for post-training), Terra (robotics data) and Recursion (managed AI agents for enterprises). The homepage states that over 90% of leading US AI labs post-train and evaluate on Labelbox, and names Meta, Google Cloud and Intuitive Surgical among its customers.

What Encord Is

Encord was founded in London by Ulrik Stig Hansen and Eric Landau, who now serve as co-CEOs, and went through Y Combinator’s Winter 2021 batch. Its Y Combinator profile lists offices in London, San Francisco and New York and a team of about 150. In February 2026, Encord raised a $60 million Series C led by Wellington Management, bringing total funding to $110 million according to Pulse 2.0.

Encord’s platform combines annotation and labeling, data indexing and curation, post-training alignment, data agents and cloud integrations, and the company also sells a Data-as-a-Service offering for collection, annotation and evaluation. It says it is used by more than 300 AI teams, and names customers such as Woven by Toyota, AXA, UiPath, Zipline and Toyota Research Institute.

Labelbox vs Encord At A Glance

Factor	Labelbox	Encord
Founded and HQ	2018, San Francisco	London, Y Combinator Winter 2021 batch
Latest reported round	Series E of $100M+ (October 2026)	$60M Series C (February 2026)
Positioning	Human data and evals for frontier labs and enterprise AI	Multimodal data layer for physical AI
Core data types	Images, video, text, documents, geospatial, audio, conversational text, HTML, LLM preference	Images, video, 3D and LiDAR, documents and text, audio, DICOM and NIfTI, sensor fusion
AI-assisted labeling	Model-assisted labeling, Foundry, SAM 2 Auto-Segment in the image editor	SAM 3 for labeling, SAM 2 for tracking, foundation model pre-labeling, configurable agents
Workforce	Alignerr expert network, self-serve and fully managed services	Trained managed annotators, hybrid setups with in-house teams
SDK	Python SDK	Python SDK
Certifications listed	SOC 2 Type II, HIPAA program, GDPR, CCPA, NIST 800-171, PCI DSS	SOC 2, HIPAA, GDPR
Deployment	Cloud, with delegated access to customer storage	US or EU cloud, private VPC, on-prem, Private Cloud Integration
Published tiers	Free, Starter, Enterprise	Starter, Team, Enterprise

Data Types And Editors Compared

Labelbox Data Types

Labelbox’s documentation lists editors for images, video, text, documents, geospatial data and audio, along with conversational text, HTML, LLM human preference, prompt and response generation, and multimodal chat evaluation. That last group reflects how much of Labelbox’s work now involves generative AI rather than classic computer vision. Medical imaging is less mature: the medical imagery product page announces native 3D DICOM support with linked axial, sagittal and coronal views, but invites users to sign up for early access, so buyers with heavy radiology workloads should confirm availability for their account.

Labelbox has also built a separate robotics data product, Terra, which its homepage describes as covering egocentric and teleoperated demonstrations captured from seven synchronized viewpoints. That is a data service, and the Labelbox documentation we reviewed does not list a LiDAR editor.

Encord Data Types

Encord’s site lists image, video, 3D and LiDAR, documents and text, audio, and DICOM and NIfTI, and it supports physical AI inputs such as RGB, depth, LiDAR, radar, force and torque, and synchronized sensor streams. Annotation tools include bitmask brushes, polygons, polylines, keypoints, rotatable boxes and classifications, plus video interpolation and object tracking. For teams labeling robotics footage, driving data or volumetric medical scans, this breadth of native formats is Encord’s clearest technical advantage.

AI-Assisted Labeling Compared

Labelbox

Labelbox supports model-assisted labeling, where teams import their own model predictions as pre-labels for humans to correct. Foundry lets teams configure foundation models, including LLMs, to generate annotations directly inside a project. In August 2024, Labelbox upgraded its Auto-Segment tool in the image editor to Meta’s Segment Anything 2.0. The Starter tier on the pricing page lists model-assisted labeling with foundation models as an included feature.

Encord

Encord’s documentation says it uses SAM 3 for labeling objects and SAM 2 for tracking them. Segmentation works on single images, image groups, image sequences and video, and tracking works on video, image groups, image sequences and DICOM. Users can label all instances of a class at once and track multiple objects forward or backward across frames. Encord’s solution docs also mention pre-labeling with models such as GPT-4o, Gemini and LLaMA 3.2, imported YOLO or custom predictions, and configurable agents that run pre-labeling before human review.

For video and multi-frame data, Encord’s tracking tools go further than anything Labelbox documents for its own video editor. For text, chat and LLM preference tasks, Labelbox has the deeper set of purpose-built editors.

Curation And Data Management Compared

Labelbox Catalog

Catalog connects to more than 25 data sources and offers vector and traditional search, natural language search, data exploration and bulk classification. Natural language search is listed even on the Free tier. The Model component adds prediction imports and error analysis.

Encord Curation

Encord’s curation tools include embedding plots, natural language and similarity search, metadata filtering and automatic checks for duplicates, outliers, class imbalance and likely label errors. Saved subsets called Collections can be sent to annotation or exported. Encord describes a loop of finding model failure modes, re-annotating the relevant data and retraining, which suits teams hunting for rare edge cases in large video or sensor archives.

Model Evaluation And RLHF Compared

Labelbox

Evaluation and post-training are now central to Labelbox’s business. Its editors cover LLM human preference, prompt and response generation and multimodal chat evaluation, Foundry can generate preference data for RLHF, and Horizon provides RL environments and evals for reasoning, tool use and computer use. Teams building or fine-tuning large language models will find more ready-made workflows here. Our list of top RLHF and LLM training data companies covers other specialists in this area.

Encord

Encord’s post-training alignment product generates preference data for RLHF, DPO and model evaluation, with rubric-based evaluation, side-by-side comparison of model outputs and consensus workflow nodes. Its managed evaluation services cover model output review, red-teaming, preference ranking and benchmarking. Model evaluation appears on the Team tier of its pricing page.

Workforce Options Compared

Labelbox And Alignerr

Labelbox’s labeling services run through Alignerr, its expert network. The Labelbox homepage describes Alignerr as more than 2.6 million skill-tested experts across more than 200 domains who write rubrics, preferences and graded assessments, and the platform overview says services cover RLHF, SFT and multimodal LLM evaluation in more than 30 languages. In early 2026 Labelbox acquired Upcraft, an agentic sales automation startup, to help scale the network. Customers can also bring their own team or their own vendor into Annotate.

Encord’s Managed Teams

Encord takes a narrower approach. It supplies trained annotators matched to domains such as medical imaging, robotics, VLA captioning, ADAS, financial services, multilingual audio and behavioral video, and builds guidelines with the customer’s subject matter experts before scaling. In-house teams can work in the same workspace, including hybrid setups for teams moving off an existing BPO. It also offers data collection through in-field operators, teleoperation facilities and lab environments.

Integrations, SDK And API Compared

Both platforms are Python-first. Labelbox’s SDK installs with pip install labelbox and supports Python 3.9 through 3.13, and its docs describe IAM delegated access for AWS S3, Google Cloud Storage and Azure Blob Storage. The pricing page mentions integrations with custom model endpoints such as Vertex AI and Databricks, and the docs describe connecting AI assistants to Labelbox through MCP.

Encord’s SDK installs with pip install encord from PyPI. Its documentation lists integrations with Amazon S3 (including Multi-Region Access Points), Google Cloud Storage, Azure Blob Storage and OTC. Encord describes itself as API and SDK-first with zero data migration, meaning data can stay in the customer’s own buckets.

Security And Compliance Compared

Labelbox

Labelbox’s security page states that it maintains SOC 2 Type II certification and a HIPAA compliance program, and describes GDPR measures. The homepage also lists CCPA, NIST 800-171 and PCI DSS. Data is encrypted at rest with AES-256 and in transit with TLS 1.2 or higher, and customers can use delegated access or signed URLs so assets stay in their own cloud under their own access policies.

Encord

Encord’s security page reports a completed SOC 2 audit, HIPAA compliance and GDPR compliance, monitored through Vanta, and a public trust center. Data is encrypted at rest with AES-256 and in transit with TLS. With Private Cloud Integration, Encord requests a temporary signed URL from the customer’s storage provider and loads data into the browser without storing it on Encord’s platform.

Deployment Options Compared

This is one of the clearest differences. Labelbox’s security page describes a cloud service hosted on Google Cloud, with most customers keeping their assets in their own cloud through delegated access. It does not list on-premises or VPC deployment.

Encord offers US or EU cloud deployment for data residency, private VPC, on-premises installation and Private Cloud Integration. Its pricing page lists VPC and on-prem deployments under the Enterprise tier. For defense, healthcare or European buyers with strict residency or air-gap requirements, that range matters.

Pricing Compared

Neither vendor publishes a per-seat software price. Both use tiered plans with custom enterprise quotes.

Tier	Labelbox (per its pricing page)	Encord (per its pricing page)
Entry	Free: up to 30 users, 50 projects, 25 ontologies, 1 workspace, community support	Starter: image and video annotation, complex ontologies, customizable workflows, self-serve support
Middle	Starter: unlimited users, projects and ontologies, custom workflows, performance dashboard, model-assisted labeling with foundation models	Team: adds data agents, performance analytics, model evaluation and onboarding support
Top	Enterprise: custom pricing and volume discounts, fully managed services, quality SLA, 3 workspaces	Enterprise: multiple workspaces, SSO, enterprise SLA, VPC and on-prem deployments

Labelbox’s pricing page also lists labeling service rates of $8 per hour and starting at $10 per hour, and offers free access to qualified educators and students for non-commercial research. Encord’s pricing page shows “Get started” for Starter and Team and “Contact sales” for Enterprise, with no dollar figures. In practice, both companies quote managed labeling and enterprise contracts based on volume, data type and quality requirements. For broader budgeting context, see our guide to AI development cost.

Which One Fits Your Team?

Choose Labelbox When

Labelbox fits teams whose main bottleneck is expert human judgment on text, code, chat and multimodal model outputs. If you are post-training or evaluating LLMs, need preference data, rubrics or graded assessments from domain experts, and want a large network you can scale up quickly, Labelbox’s combination of Alignerr, Foundry and Horizon is built for that. Its Free tier also lets small teams start without a sales call.

Choose Encord When

Encord fits teams working with video, LiDAR, radar, synchronized sensor streams or DICOM and NIfTI volumes, especially in robotics, autonomous systems, drones and healthcare. Its SAM-based segmentation and tracking, native 3D and medical formats, and edge-case curation suit physical AI pipelines. It is also the stronger option when deployment is a hard requirement, since it offers EU residency, private VPC and on-prem installation.

When To Use Both Or Neither

Some larger organizations run a vision or sensor pipeline on one platform and source LLM evaluation data elsewhere, so using both is not unusual. If your needs are narrow, such as a single image classification project or text labeling on a tight budget, an open source tool or a smaller platform may be enough, and a managed services vendor may be a better match than either.

Frequently Asked Questions

Is Labelbox Or Encord Better For Computer Vision?

Both handle images and video well. Encord documents more depth for video tracking, LiDAR, sensor fusion and medical formats, while Labelbox offers SAM 2 Auto-Segment in its image editor.

Does Either Platform Have A Free Plan?

Labelbox’s pricing page lists a Free tier for up to 30 users and 50 projects. Encord’s pricing page lists a Starter tier but does not state a price or describe it as free.

Can I Keep My Data In My Own Cloud?

Yes, on both. Labelbox supports delegated access for AWS, Google Cloud and Azure storage, and Encord offers Private Cloud Integration with signed URLs as well as VPC and on-prem deployment.

Which Platform Offers Expert Labelers For LLM Work?

Labelbox’s Alignerr network is built specifically for expert RLHF, SFT and evaluation work. Encord also provides managed annotators and evaluation services, with an emphasis on multimodal and physical AI domains.

Are Both Platforms HIPAA Compliant?

Labelbox states that it maintains a HIPAA compliance program, and Encord states that it is HIPAA compliant. Healthcare buyers should still request a BAA and review each vendor’s trust documentation.

Conclusion

Labelbox vs Encord is less a question of which platform is better and more a question of what kind of data sits at the center of your roadmap. Labelbox has built its recent growth around expert human data, evaluation and post-training for language and multimodal models, backed by a very large expert network. Encord has built around multimodal and physical AI data, with strong video, 3D, sensor and medical tooling and more deployment flexibility.

Run a pilot on your own data before committing: test the editors on your hardest examples, measure throughput and review effort, and ask each vendor to quote software and services at your expected volume.