Amazon SageMaker Ground Truth and Roboflow

SageMaker Ground Truth is closed to new customers, Roboflow is a complete computer vision platform

Amazon SageMaker Ground Truth vs Roboflow? Mechanical Turk closes September 30, 2026 and Ground Truth is closed to new customers. Over 2 million engineers create datasets, train models, and deploy with Roboflow, and every plan, including the Free tier, comes with self-hosted inference, model weights download, and a private workspace.

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Why are companies choosing Roboflow?

Roboflow is enterprise vision AI infrastructure, used by more than half of the Fortune 100, with SOC 2 Type II, HIPAA, and air-gapped deployment. Create datasets, train models, and deploy to production.

  • End-to-End Vision, One Platform

    From labeling to deployment - build, train, and launch with ease.

  • Built for Builders, Ready for Anything

    Interoperable infrastructure for developers to create custom vision solutions.

  • AI at the Edge, Without Limits

    Deploy to any edge device and power real-time vision AI.

  • Roboflow Annotate labeling cans with AI box prompting

    Fast data labeling

    Label data quickly with a suite of AI-assisted annotation tools to augment human labeling or fully automate your data labeling pipeline.

  • Workflow blocks for notifications and integrations

    Build vision AI applications with ease

    Use a low-code open source platform to simplify building and deploying vision AI applications.

  • A team reviewing a detected pallet in a warehouse aisle

    Machine learning expertise, always

    Work with account-dedicated AI specialists and Field Engineers to accelerate development and optimize performance.

  • Photos from datasets across industries

    Datasets and models for every industry

    Access thousands of datasets and pre-trained models to kickstart your computer vision projects.

  • Logos of OpenAI, Microsoft, Meta, Anthropic, Google and Qwen

    Flexible model licensing

    Choose from a range of licensing options to fit your needs, whether for commercial use, private deployment, or collaboration across teams.

Want to migrate from SageMaker Ground Truth to Roboflow?

  • Import your Ground Truth manifests

    Bring your S3 images and Ground Truth output manifests into Roboflow. Existing boxes carry over, no relabeling needed.

  • Deploy with Roboflow

    No matter where you train your model, use Roboflow's trusted deployment solutions, including in your own AWS account.

  • Automate data labeling

    Use AI labeling in Roboflow to automate 90% of human labeling, no crowd workforce required.

OK I’m ready, how do I use Roboflow?

Roboflow makes it easy to build, train, and deploy custom computer vision models, even if you’re not a machine learning expert.

Start a free Roboflow account

Talk to a Vision AI engineer about replacing SageMaker Ground Truth

Ground Truth is closed to new customers. We'll map your move to Roboflow on the first call.

Ask us about:

  • Direct import of your Ground Truth output manifests, with labels intact
  • A live demo of Auto Label on your own images
  • A labeling workflow for your own team, with AI drafting the first pass
  • Training and deployment in the same platform, on cloud, edge, or air-gapped hardware

Over 80,000 organizations build with Roboflow.

  • Rivian
  • Pella
  • Chobani
  • USG Corporation
  • BNSF Railway
  • American Woodmark
Start where you are

Compare Amazon SageMaker Ground Truth and Roboflow Annotate

Amazon SageMaker Ground Truth is AWS’s data labeling service, but its availability has changed. Ground Truth is closed to new customers, with no new features planned. Ground Truth Plus reached end of support on June 30, 2026, and Mechanical Turk closed on September 30, removing the public workforce option from Ground Truth. Existing customers can continue using private or vendor workforces, but teams that relied on Mechanical Turk need a new labeling process.

Roboflow helps you move forward with the labeling work you’ve already done. Upload your images and Ground Truth output manifests to bring existing bounding boxes into Roboflow, ready to review, edit, and use for training. For new images, Auto Label (opens in a new tab) drafts annotations from a text prompt describing what you want to find. Your team reviews and corrects the results, putting the people who know your products in control of label quality.

The same platform takes those labels through dataset versioning, model training, and deployment. Roboflow Agent helps select models, build Workflows, and connect predictions to business systems. With the Roboflow MCP server and ChatGPT plugin (opens in a new tab), you can also manage datasets, start labeling jobs, train models, and test predictions through a conversation or coding agent. Deploy in Roboflow’s cloud, your own AWS account, or on edge hardware.

For enterprise teams, Roboflow provides controls for managing who labels data, who reviews it, and who can access each project. Enterprise plans include SSO, role-based permissions, annotation history, labeling analytics, and review workflows. Optional labeling services and hands-on engineering support help teams handle large datasets and move applications into production.

You can start for free with private projects, unlimited seats, model weight downloads, and 10 credits each month. Self-hosted inference is free on every plan, so running models locally consumes no Roboflow credits. Follow the Ground Truth migration guide to import your existing images and labels, then test Auto Label on your next batch.

Amazon SageMaker Ground Truth vs Roboflow Annotate: Feature by Feature

FeatureRoboflow AnnotateAmazon SageMaker Ground Truth
AvailabilityOpen, self-serve sign-upClosed to new customers; existing customers only, no new features planned
Supported labeling tasksObject detection, segmentation, classification, keypoint, OCR, multimodalImage classification (single and multi-label), bounding box, semantic segmentation, label verification; text; 3D point cloud detection, tracking, and segmentation
Video labelingYesYes, video classification, frame object detection, and frame object tracking
Running environmentWeb browser, no installSageMaker AI console; requires an AWS account, S3 buckets, IAM roles, and input manifests
Free tierYes, with free self-hosted inference, model weights download, and a private workspacePay per labeled object plus workforce cost and any automated-labeling compute
Open sourceYes, open-source supervision and inference libraries that run alongside the Roboflow platformNo
Hosted labeling environmentYesYes, worker portal hosted by AWS
Labeling workforceYour own team, with foundation models drafting the first passPrivate workforce or Marketplace vendor; the Mechanical Turk worker type is removed on September 30, 2026
AI auto labelingYes, Auto Label from a text prompt on day oneAutomated data labeling needs a minimum of 1,250 objects (5,000 recommended), runs only on four task types, and trains on managed ml.p3 instances
Label assist with foundation modelsYes, Smart Polygon powered by Meta AI's Segment AnythingNo
Label assist with custom modelsYes, any model trained or uploaded to RoboflowPartial, active learning trains a model on your job's labels; no bring-your-own model in the built-in flow
Dataset managementYes, versioning, train/valid/test splits, health check, preprocessingBasic, input and output manifests in S3; no versioning or splits
Semantic dataset searchYesNo
Dataset analyticsYesNo
Labeling analyticsYesLimited, job status and worker metrics in CloudWatch
Labeling historyYesPartial, adjustment and verification jobs chained from prior output
Image augmentationYesNo
Export formats40+ formats, including direct import of Ground Truth manifestsAugmented output manifest (JSON Lines) in S3
Role-based access controlYesYes, AWS IAM
SSOYesYes, Amazon Cognito or OIDC for private workforces
Model trainingYesSeparate service, SageMaker training jobs from the manifest
Model deploymentYesSeparate service, SageMaker endpoints
Application builderYes, Roboflow WorkflowsNo
Model architecturesYes, RF-DETR, YOLO (v8 / v11 / v26), Detectron2, and other current architecturesSeparate service; training runs in separate SageMaker training jobs
Self-hosted inference serverYes, open-source inference server for custom and foundation modelsNo comparable self-hostable inference server
Open-source CV libraryYes, supervision for detections, tracking, annotation, and evaluationNo comparable library
SDKs and APIsYes, Python SDK plus REST/HTTP APIsYes, the CreateLabelingJob API through the AWS SDKs and CLI

Best Amazon SageMaker Ground Truth Alternatives

Roboflow is the top Amazon SageMaker Ground Truth alternative for teams that want labeling, training, and deployment in one platform. Teams comparing Amazon SageMaker Ground Truth also look at: