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.
Amazon Rekognition and Roboflow: Unlock powerful vision
Use Amazon Rekognition and Roboflow for custom vision solutions that drive growth.
Why are companies choosing Roboflow?
The turnkey solution for vision AI used by over 1 million engineers to 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
Seamless AI, Zero Lock-In
Seamless integration with any tool, any workflow - no platform lock-in, ever.
One Platform, Full Adoption
Easy tools for every team: domain experts, ML engineers, developers, and business users.
Your Vision, Our Commitment
We help you tackle business challenges and fill any gaps in skills or resources.
Build vision AI applications with ease
Use a low-code open source platform to simplify building and deploying vision AI applications.
Edge deployment
Cloud and edge deployments (NVIDIA Jetson, Raspberry Pi, or mobile devices) for real-time, on-premise inference.
Devices for every scenario
Software, compute, cameras: all the tools you need to deploy vision AI.
Power your vision with Roboflow and Amazon Rekognition—accurate, scalable, seamless
Refine Your Dataset
Easily import labeled data from Amazon Rekognition Computer Vision into Roboflow.
Train Anywhere, Deploy Anywhere
Bring your Amazon Rekognition-trained models into Roboflow for more flexibility.
Improve performance
Close the loop by sending data from your running models back to Roboflow.
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.
Speak with an AI expert
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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Commpare Roboflow and Amazon Rekognition
When it comes to comparing Roboflow and Amazon Rekognition, Rekognition offers pre-trained APIs for tasks such asobject detection, facial analysis, and text recognition. Engineering teams that need custom models and full control over their computer vision workflows often turn to Roboflow. Roboflow enables you to label your own data, train models tailored to your exact requirements, and deploy them anywhere - whether in the cloud, on the edge, or on-premises. Unlike black-box APIs, Roboflow gives you visibility into your model’s performance and the ability to iterate quickly. Teams choose Roboflow because it supports end-to-end development, data privacy, and long-term ownership of their vision systems.
Roboflow
End-to-end, CV-specific platform (annotate, train, deploy)
Amazon Rekognition
AWS pre-trained vision APIs + Rekognition Custom Labels
Scale & Ecosystem
Roboflow
- 1,000,000+ 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: GE Vernova, Rivian, Chobani, USG, Pella)
- Cloud-agnostic: runs in managed cloud, on AWS, GCP, or Azure customer accounts, on-premises, or air-gapped
Amazon Rekognition
- Delivered as part of Amazon Web Services; scale is the broader AWS platform footprint
- No publicly disclosed developer or organization count specific to the Rekognition service
- No public catalog of community-contributed datasets or pre-trained models comparable to Roboflow Universe
- Use of the service requires an AWS account, IAM roles, S3 storage, and AWS billing; not available outside the AWS ecosystem
Product Capabilities (Computer Vision)
Roboflow
- End-to-end platform: dataset management, annotation, model training, hosted inference, and deployment
- Native custom-training support for object detection, instance and semantic segmentation, image classification, keypoint detection, OCR, depth estimation, and multimodal tasks
- Built-in foundation models including CLIP and SAM
- 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; no per-image or per-dataset label caps
Amazon Rekognition
- Pre-trained APIs: DetectLabels, DetectFaces, CompareFaces, DetectText, DetectModerationLabels, and Face Liveness
- Rekognition Custom Labels supports image classification and object/bounding-box detection only; instance and semantic segmentation, keypoint detection, depth estimation, and multimodal training are not supported as custom-training tasks
- Per AWS documentation, Custom Labels is "not designed for analyzing faces, detecting text, or finding unsafe image content" — those tasks must be served by the pre-trained APIs and cannot be fine-tuned to customer data
- Hard quotas on Custom Labels include a maximum of 50 labels per image, 50 bounding boxes per image, 250 unique labels per dataset, 4096×4096 pixel maximum image dimension, 15 MB max file size, and PNG/JPEG only
- As of April 30, 2026, Rekognition Streaming Events and Batch Image Content Moderation features entered maintenance mode (no new feature development)
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 Rekognition
- Access via the AWS SDK family (boto3 for Python, plus official SDKs for Java, .NET, Go, JavaScript, Ruby, PHP) and the AWS CLI
- REST API surface is anchored to AWS authentication (IAM / SigV4), AWS regions, and S3-backed inputs
- Trained Custom Labels models are served as AWS-managed inference endpoints billed in inference units; self-hostable inference of customer-trained models is not part of the service
- No comparable open-source CV utility library or self-hostable inference server in mainstream community use; the developer surface is tied to the AWS cloud
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
- Independent of any hyperscale cloud provider; data does not need to traverse a single vendor's infrastructure
Amazon Rekognition
- Inherits the AWS compliance portfolio: SOC 1 / 2 / 3, ISO 27001 / 27017 / 27018, PCI DSS Level 1, HIPAA (BAA-eligible), and FedRAMP (Commercial and GovCloud)
- Deployment is the AWS managed cloud (Commercial or GovCloud regions); customer-VPC, on-premises, and fully air-gapped self-serve deployments are not offered for Rekognition
- Data residency is determined by the AWS region selected; outside-AWS deployment is not supported
- Vendor concentration: workloads, identity, billing, storage, and data plane all sit within a single hyperscale provider
Sources: roboflow.com, docs.roboflow.com, security.roboflow.com, universe.roboflow.com, github.com/roboflow, aws.amazon.com/rekognition, docs.aws.amazon.com/rekognition, aws.amazon.com/compliance, aws.amazon.com/about-aws/whats-new. Figures reflect publicly available information as of May 2026.