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.
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 to production with Roboflow.
Why are companies choosing Roboflow?
The turnkey solution for vision AI. Create datasets, train models, and deploy to production.
AnnotateAI-accelerated labeling and dataset optimization
TrainComplete model training toolset and platform
BuildVisual application builder and template
DeployManaged infrastructure, cloud and edge support
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.
Build vision AI applications with ease
Use a low-code open source platform to simplify building and deploying vision AI applications.
Machine learning expertise, always
Work with account-dedicated AI specialists and Field Engineers to accelerate development and optimize performance.
Datasets and models for every industry
Access thousands of datasets and pre-trained models to kickstart your computer vision projects.
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.
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Our team will help you start solving business problems on the first call.
Ask us about:
- Solution architecting
- Live demonstration
- Pricing and specifications
- Feasibility assessment
Over 16,000 organizations build with Roboflow.
- Rivian
- Pella
- Chobani
- USG Corporation
- BNSF Railway
- American Woodmark
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Compare Amazon SageMaker Ground Truth and Roboflow Annotate
Amazon SageMaker Ground Truth is the data labeling service inside SageMaker AI. It covers image classification, bounding boxes, semantic segmentation, label verification, video frame detection and tracking, 3D point clouds, and text, with jobs run by your own private workforce, an AWS Marketplace vendor, or, until now, the Mechanical Turk crowd. That last option is going away.
Amazon Mechanical Turk closes permanently on September 30, 2026, and Amazon's closure FAQ states the Mechanical Turk worker type will no longer be available for Ground Truth labeling jobs or Augmented AI review workflows as of that date.
Ground Truth itself is closed to new customers, Ground Truth Plus reached end of support on June 30, 2026, and AWS says it does not plan to introduce new features.
Teams that need labeling to keep moving are choosing Roboflow. Roboflow imports Ground Truth output manifests directly, adds foundation-model auto labeling so a crowd is no longer the bottleneck, and carries the labeled data straight into training and deployment. If you are on a deadline, read our guide to migrating from SageMaker Ground Truth and Mechanical Turk to Roboflow.
Amazon SageMaker Ground Truth vs Roboflow Annotate: feature by feature
Both tools cover image classification, bounding boxes, and segmentation. They differ in who does the labeling, how automation starts, and whether the labeled data flows into training and deployment in the same place. Ground Truth is closed to new customers and loses its Mechanical Turk workforce on September 30, 2026.
| Feature | Roboflow Annotate | Amazon SageMaker Ground Truth |
|---|---|---|
| Availability | Open, self-serve sign-up | Closed to new customers; existing customers only, no new features planned |
| Supported labeling tasks | Object detection, segmentation, classification, keypoint, OCR, multimodal | Image classification (single and multi-label), bounding box, semantic segmentation, label verification; text; 3D point cloud detection, tracking, and segmentation |
| Video labeling | Yes | Yes, video classification, frame object detection, and frame object tracking |
| Running environment | Web browser, no install | SageMaker AI console; requires an AWS account, S3 buckets, IAM roles, and input manifests |
| Free tier | Yes | Pay per labeled object plus workforce cost and any automated-labeling compute |
| Open source | Closed source platform; open-source supervision and inference libraries | No |
| Hosted labeling environment | Yes | Yes, worker portal hosted by AWS |
| Labeling workforce | Your own team, with foundation models drafting the first pass | Private workforce or Marketplace vendor; the Mechanical Turk worker type is removed on September 30, 2026 |
| AI auto labeling | Yes, Auto Label with GPT-6 Astra and Gemini from a text prompt on day one | Automated 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 models | Yes, Smart Polygon powered by Meta AI's Segment Anything | No |
| Label assist with custom models | Yes, any model trained or uploaded to Roboflow | Partial, active learning trains a model on your job's labels; no bring-your-own model in the built-in flow |
| Dataset management | Yes, versioning, train/valid/test splits, health check, preprocessing | Basic, input and output manifests in S3; no versioning or splits |
| Semantic dataset search | Yes | No |
| Dataset analytics | Yes | No |
| Labeling analytics | Yes | Limited, job status and worker metrics in CloudWatch |
| Labeling history | Yes | Partial, adjustment and verification jobs chained from prior output |
| Image augmentation | Yes | No |
| Export formats | 40+ formats, including direct import of Ground Truth manifests | Augmented output manifest (JSON Lines) in S3 |
| Role-based access control | Yes | Yes, AWS IAM |
| SSO | Yes | Yes, Amazon Cognito or OIDC for private workforces |
| Model training | Yes | Separate service, SageMaker training jobs from the manifest |
| Model deployment | Yes | Separate service, SageMaker endpoints |
| Application builder | Yes, Roboflow Workflows | No |
| Purchase mechanism | Online and sales | AWS account billing (existing customers only) |
Roboflow
End-to-end computer vision platform (annotate, train, deploy)
Amazon SageMaker Ground Truth
Data labeling service inside SageMaker AI, closed to new customers
Scale & Ecosystem
Roboflow
- 2,000,000+ developers on the platform
- 1,000,000+ public datasets on Roboflow Universe
- 100,000+ pre-trained models hosted publicly
- 25,000+ organizations building with the platform, including more than half of the Fortune 100 (publicly cited: Rivian, Chobani, USG, Pella)
- Self-serve sign-up; teams can begin annotating, training, and deploying without a services contract
Amazon SageMaker Ground Truth
- Launched at re:Invent 2018 as the labeling service inside SageMaker; AWS docs now state it is no longer open to new customers and no new features are planned
- Ground Truth Plus, the managed labeling offering, reached end of support on June 30, 2026
- Amazon Mechanical Turk, the public workforce of 500,000+ contractors, closes permanently on September 30, 2026, and the Mechanical Turk worker type is removed from Ground Truth and Augmented AI on that date
- Remaining workforce options: a private workforce you manage, or a vendor contracted through AWS Marketplace
- No public catalog of community-contributed labeled datasets or CV models comparable to Roboflow Universe
Product Capabilities (Computer Vision)
Roboflow
- End-to-end platform: dataset management, annotation, model training, hosted inference, and deployment
- Auto Label uses foundation models (GPT-6 Astra, Gemini) to label images for classes you define in a text prompt; label 1,000 images in under ten minutes
- Label Assist pre-annotates new images with your own trained models, and Smart Polygon creates one-click polygon annotations powered by Meta AI's Segment Anything
- Native support for object detection, instance and semantic segmentation, image classification, keypoint detection, OCR, depth estimation, and multimodal tasks
- Direct import of SageMaker Ground Truth output manifests, so labels paid for on Ground Truth carry over without rework
- Roboflow Workflows for chaining detection, classification, OCR, and business logic into a single pipeline
- Active model registry covering RF-DETR, YOLO (v8 / v11 / v26), Detectron2, and other current architectures
Amazon SageMaker Ground Truth
- Built-in image task types: single and multi-label classification, bounding box, semantic segmentation, and label verification; plus text, video frame, and 3D point cloud task types with adjustment jobs
- No keypoint, OCR, or instance-segmentation polygon task type among the built-ins; custom tasks require your own HTML template and Lambda pre- and post-processing functions
- Automated data labeling (active learning) requires a minimum of 1,250 objects, is recommended at 5,000+, is limited to image classification, bounding box, semantic segmentation, and text classification, and runs managed ml.p3.2xlarge training instances
- Output is an augmented manifest (JSON Lines) written to S3; training happens in separate SageMaker training jobs and deployment in separate SageMaker endpoints
- No new features planned; AWS limits ongoing investment to security and availability
Developer Experience
Roboflow
- Open-source supervision library - model-agnostic utilities for detections, tracking, annotation, and evaluation
- Open-source inference server - self-hostable HTTP inference for custom and foundation models
- Native integrations with Hugging Face Transformers, MMDetection, and the broader open-source CV stack
- Python SDK plus REST/HTTP APIs; standardized I/O lets teams swap model weights without changing application code
Amazon SageMaker Ground Truth
- Jobs created through the SageMaker AI console or the CreateLabelingJob API via the AWS SDKs and CLI
- Every job needs an S3 input manifest, an IAM execution role, a label category configuration file, and a workforce ARN before the first image can be labeled
- Custom task types require writing an HTML worker template plus pre-annotation and post-annotation Lambda functions
- No open-source CV utility library or self-hostable inference server comparable to Roboflow's tooling
Enterprise & Security
Roboflow
- SOC 2 Type II compliant
- HIPAA-compliant infrastructure with BAAs available
- PCI DSS (SAQ A and AOC) compliance
- Deployment options: managed cloud, customer VPC, on-premises, and fully air-gapped / offline (Docker-based) for firewalled environments
- Training and inference can run on customer-owned bare metal or in the customer's own cloud account, including AWS
Amazon SageMaker Ground Truth
- Inherits SageMaker AI and AWS compliance programs (SOC, ISO, HIPAA eligibility, PCI DSS, FedRAMP); no Ground Truth-specific compliance statement is published
- Data stays in the customer's S3 buckets; private workforces authenticate through Amazon Cognito or an OIDC identity provider
- Pricing is per labeled object plus workforce cost; Mechanical Turk balances are refunded within 30 days of closure and transaction history stays available until January 28, 2027
- Service is in maintenance mode: closed to new customers, no new features, security and availability updates only
Sources: roboflow.com, docs.roboflow.com, security.roboflow.com, universe.roboflow.com, github.com/roboflow, docs.aws.amazon.com, aws.amazon.com, mturk.com/help. Figures reflect publicly available information as of September 2026.