Labellerr

Labellerr is a data labeling and annotation platform for teams preparing training data for AI and machine-learning projects. Projects can use images, videos, PDFs, text, and audio, with annotation options including bounding boxes, polygons, polylines, keypoints, dots, classification, segmentation, and object detection. Its automated tools include prompt-based and model-assisted labeling and active learning; Labellerr says it natively supports Meta’s SAM, SAM 2, and SAM 3. A Python SDK supports dataset and workflow management in ML pipelines. Teams can export annotations in formats such as CSV, JSON, COCO, Pascal VOC, YOLO, and custom formats. Remote storage options include AWS S3, Google Cloud Storage, and Azure Blob. Integrations with GCP Vertex AI, AWS SageMaker, and Azure ML are add-ons for applicable plans. Labellerr lists Auth0 authentication, TLS 1.2 or higher in transit, and AES-256 encryption. The free Researcher Plan includes 2,500 data credits, one seat, one workspace, and up to 10 projects. Pro costs $9,999 a year and includes 100,000 credits and up to 200 seats; unused credits do not carry forward.

Labellerr covers multiple data types, annotation approaches, and export formats, with a free plan for smaller research projects. Teams should weigh the annual Pro cost and credit rollover limit against their expected workload.

Labellerr suits machine-learning teams building computer-vision AI, including teams in automotive, medical imaging, and manufacturing. The free Researcher Plan is also aimed at students and researchers.

Labellerr is a data annotation platform for AI and machine-learning teams, particularly those building computer-vision datasets. It offers a broad mix of media, labeling methods, and deployment options, but the gap between its free allowance and annual Pro price makes it a better fit for sustained team workloads than occasional projects.

Overview

Founded in 2018 by Tensor Matics Inc, Labellerr supports projects involving images, video, PDFs, text, and audio. Teams can work through the web platform, use its API, or deploy it on customer cloud infrastructure or hardware. That flexibility is useful for organizations with established infrastructure requirements; smaller groups may find the free plan more practical than the paid tier.

Key features

Annotation options include bounding boxes, polygons, polylines, keypoints, dots, classification, segmentation, and object detection. Image formats include JPG, JPEG, PNG, BMP, and TIFF. This selection covers common computer-vision tasks, while the stated format support applies specifically to images.

Prompt-based labeling, model-assisted labeling, and active learning provide ways to reduce repetitive manual work. Labellerr also says it natively supports Meta’s SAM, SAM 2, and SAM 3 for annotation, a useful fit for teams incorporating those models into computer-vision workflows. A Python SDK supports programmatic dataset and workflow management in ML pipelines.

Exports include CSV, JSON, COCO, Pascal VOC, YOLO, and custom formats, giving teams several ways to carry annotations into other workflows. Collaboration and API access are supported. Remote storage options include AWS S3, Google Cloud Storage, and Azure Blob; GCP Vertex AI, AWS SageMaker, and Azure ML integrations are add-ons for applicable plans, so teams should account for them separately.

Labellerr says it uses Auth0 authentication, TLS 1.2 or higher for data in transit, and AES-256 encryption. Enterprise plans offer private-cloud and on-premise hosting as an add-on. Those options address infrastructure needs, but they are not included as standard Enterprise hosting.

Pricing

The Researcher Plan is free and includes 2,500 data credits, one seat, one workspace, and up to 10 projects. It is aimed at students and researchers; its single-seat and credit limits make it a constrained choice for a working team.

The Pro Plan costs $9,999 annually and includes 100,000 data credits, up to 200 seats, and unlimited projects. Extra credits and seats are available. The larger quota and team capacity suit ongoing annotation programs, but the annual commitment is a substantial step up from the free plan. Unused data credits do not roll over into the next payment cycle, so teams should estimate their usage rather than assume they can bank a surplus.

Enterprise has custom pricing and includes unlimited data credits and seats, multiple workspaces, SSO, and an enterprise SLA and support. Private-cloud and on-premise hosting are add-ons. Enterprise plans include 24/7 email, chat, and call support; Researcher and Pro include business-hours email support. Enterprise is the natural tier for organizations that need larger-scale access or an SLA, while teams that do not need those provisions can avoid a custom engagement.

Platforms

Labellerr is available on web and through an API, with self-hosted deployment also supported. The SDK and deployment choices make it relevant to teams integrating annotation into existing ML infrastructure rather than relying only on a standalone web workflow.

Who it's for

Labellerr is best suited to machine-learning teams preparing computer-vision data, including groups in automotive, medical imaging, and manufacturing. Its broader media support and variety of annotation methods can also serve projects beyond still images. Students and researchers can start with the free plan, but teams with regular workloads should compare expected credit use and annual cost before choosing Pro.

Pros and cons

• Pros: Supports five data types and a wide set of annotation methods, giving teams room to handle varied training-data projects in one platform.
• Pros: Multiple export formats, API access, and a Python SDK help teams connect annotation work to ML pipelines.
• Pros: Web, API, and self-hosted options, plus named cloud-storage support, accommodate different infrastructure setups.
• Cons: The free plan is limited to one seat, one workspace, 10 projects, and 2,500 credits, which is restrictive for collaborative or high-volume work.
• Cons: Pro requires $9,999 annually, and unused credits expire at the end of the payment cycle, raising the stakes of usage planning.
• Cons: Cloud ML integrations and private-cloud or on-premise hosting are add-ons, so some enterprise needs may increase the cost beyond the plan price.

Alternatives

Roboflow is another freemium option, with web, API, self-hosted, Android, and iOS platforms. Its free tier includes 10 credits a month, described as enough to train about 30 models or run 80,000 inferences; consider it when those stated allowances and mobile access better match the workflow.

CVAT offers free options, including a Community plan for personal use and small teams under an MIT license. It is worth considering when an open-source community edition and small-team use are the priority.

Segments.ai has a free academic license for non-commercial research, subject to a license request. Students and researchers whose work fits those terms may prefer it.

Annota AI has a free tier for one member, two datasets, 4 GB of storage, and €4 in monthly credits. It may suit a solo user whose project fits those limits.

Draw Attention offers a free edition limited to one interactive image, with a paid plan for website use.

Label Studio is a freemium alternative with API, desktop, web, and self-hosted platforms.

Supervisely has a free Community plan with 5 GB of storage, 10,000 files, and two members, which may suit a small team that can work within those caps.

Ultralytics Platform offers a free plan with 100 GB of storage, 10 GB per upload, 100 models, and three concurrent cloud training jobs, among other limits.

For a broader comparison of tools in this category, see Image Annotation Software.

Verdict

Choose Labellerr if your ML team needs varied media support, multiple annotation approaches, and exports or integrations that fit an existing pipeline. Its broad workflow options and self-hosted deployments are the strongest reasons to consider it. Look elsewhere if a small allowance is enough or if $9,999 per year and non-rolling credits do not fit your expected workload.