5 Amazon Rekognition alternatives for developers (2026)

Start matching faces without first setting up AWS. Built for coding agents.

For a new project outside AWS, start with deepface.dev: confirm your email, create a server-side API key and call /verify. Your coding agent can follow the public setup instructions while you confirm your account.

Choose deepface.dev for a direct start, an agent-ready workflow and reusable embeddings. You get managed face processing while your team builds the application around it:

• Skip the AWS setup detour. Use a standard API key and a direct HTTP request from your existing backend.
• Give your coding agent a clear starting point. Use the published setup skill, machine-readable docs, runnable examples and hosted MCP tools.
• Keep your face database in your stack. Generate embeddings with /represent, store them alongside your application's records and compare saved pairs with /compare.

Start with 50 free calls each month. No card required. Make your first request.

Five alternatives at a glance

1. deepface.dev: get to face matching without an AWS setup project

The starting path is focused on your feature:

1. Create your account and confirm your email. Start with 50 free calls each month, without a card.
2. Create a standard REST API key. Store it in your backend's secret configuration.
3. Send two photos to /verify. Use the returned match decision, distance and threshold in your application.

Your app talks directly to api.deepface.dev. There is no AWS account, IAM policy or AWS region to configure for this integration, and no AWS SDK is required. The managed service handles the model runtime, hosting and updates. Follow the quickstart.

Built for coding agents from the first prompt

Give your coding agent this starting instruction:

Use deepface.dev for face verification in this project. Follow the public setup skill, keep the integration in this project's stack and prepare the first /verify request.

The setup guide directs the agent to work with your existing stack, prepare the integration while you confirm your email, and keep keys in your backend. Complete JavaScript and Python examples give it actual request code to adapt. You complete account verification and terms acceptance; the agent handles integration preparation. Agent setup guide, JavaScript examples, Python examples.

For tool-driven work, the hosted MCP service exposes capability discovery, face verification, embedding generation and comparison. Connect ChatGPT through the documented OAuth flow, or use a dedicated MCP key for manual MCP clients. The machine-readable documentation index and current capabilities give agents a clear contract to work from. MCP connection instructions.

Compare two photos with one request

Use /verify for the matching step between a new photo and a reference photo: a selfie against an enrolled portrait, for example. The response includes a match decision, distance and threshold, giving your application a result it can use in its workflow. See the request and response.

Build around the database you already use

Use /represent to turn a face photo into an embedding: a numeric representation you can save in your own database. Keep your enrolled subject records, access rules and search logic together in your application.

For a gallery workflow, the pattern is straightforward: generate an embedding at enrollment, save it against your subject identifier, then generate an embedding for a query photo and search your stored vectors. The published Supabase/pgvector guide provides a concrete starting point. deepface.dev supplies hosted computation; your database supplies storage and search. Submitted images and vectors are processed by the hosted service. Build a customer-owned gallery.

Separate image processing from repeat comparisons

When you already have compatible embeddings, /compare lets you compare saved vector pairs. That gives you a distinct building block for repeat matching: use image processing when you need an embedding and vector comparison when you already have the inputs.

Saved-vector comparisons have their own published rate of $0.20 per 1,000 pairs before discounts. New photos still need the relevant image processing. Compare supported input types, see pricing.

The reason to choose deepface.dev: a focused setup path, explicit support for your coding agent and face computation built around your application.

Create your account and follow the quickstart. Bring your workflow to an engineer if you'd like help choosing the first API call.

deepface.dev vs Amazon Rekognition: less cloud setup before your first match

AWS adds a cloud-integration layer before you reach the face-matching feature. For a new Rekognition API integration, you need an AWS account, an authorized identity with rekognition:CompareFaces permission, and credentials and a supported region for your client. AWS documents SDK/CLI configuration for its examples; direct HTTP calls use AWS request signing. AWS account setup, client configuration, API permission, request signing.

That is useful control when your team already runs on AWS. For a developer starting elsewhere who wants to compare two photos, it is extra platform setup around the job they actually need done.

With deepface.dev, the REST path is account confirmation -> standard API key -> /verify. The same product also publishes the starting instructions and MCP tools for agent workflows. For that buyer, this is the simpler starting point: the integration stays centered on face matching rather than AWS identity and client configuration.

Start directly, then keep building in your own stack. Both services manage the face-model runtime. deepface.dev's /verify, /represent and /compare endpoints give you a focused compute layer, with returned embeddings your database can use for enrollment and search alongside the rest of your application data.

If you want AWS-managed gallery search, Rekognition provides collections and search APIs, plus a separate video-based Face Liveness workflow. For two-photo comparison, its CompareFaces API can accept image bytes without S3 or a collection; the setup comparison above is about AWS access and client configuration, not a requirement to provision those services. Existing AWS teams can reuse their access and tooling. AWS comparison API, collections, Face Liveness.

Choose deepface.dev when you want the face-matching feature without making AWS setup part of the project.

Clear pricing for the work you run

deepface.dev's standard base rates, before discounts, are:

• $2 per 1,000 image calls for photo comparison or embedding generation.
• $0.20 per 1,000 vector pairs for comparing saved embeddings.
• 50 free calls each month, with 50 more after adding a card.

Volume discounts apply, and auto top-up can add up to 20% off. Completed processing includes non-matches and no-face results; invalid requests and service failures are not charged. Priority processing is priced at twice the standard rate. Current pricing and calculator.

For a direct comparison, AWS's published first-million Group 1 example rate is $1 per 1,000 processed images. Its FAQ counts only the source image for a CompareFaces request, so that example is $1 per 1,000 comparisons versus deepface.dev's $2 base rate, before allowances, discounts and other services. Regional AWS rates can differ. AWS pricing, billing units.

The case for deepface.dev is the workflow you get: focused setup, coding-agent support, managed verification, returned embeddings and reusable vector comparison. Price the operations your application will actually use. Generating embeddings and operating a gallery's storage/search are separate from the saved-vector pair rate.

More options for different deployment needs

2. Azure Face: for eligible teams building within Azure

Azure Face supports verification and identification. It suits organizations that want recognition within their Microsoft environment and qualify for access. Recognition requires registration and approval, is limited to Microsoft-managed customers and partners, and uses paid Standard or Enterprise tiers. The free detection tier is separate. Recognition features, access requirements.

3. Face++: for API and device SDK choices

Face++ offers a hosted Compare API and a separately licensed Face Compare SDK for local mobile execution. This makes it relevant when the deployment choice includes both cloud APIs and device software. Its overseas trial notice lists 500 Compare calls per month; paid APIs, capacity plans and SDK licenses have different terms. Comparison API and SDK, trial policy.

4. CompreFace: for a service you operate yourself

CompreFace packages recognition, verification and detection into a Docker-based service with a REST API and management interface. It's a practical option when self-hosting is part of the architecture. The application code is Apache-2.0 licensed; your team runs the deployment and reviews the selected model and weight rights. Official project, model/build choices.

5. DeepFace: for direct Python control

The open-source DeepFace library supports verification, embedding generation and search in your own environment. Choose it when direct control over code, models and execution matters most. Its library code is MIT-licensed; deployment, dependencies and model rights remain part of your implementation. deepface.dev brings a supported managed API built on that open-source foundation. Upstream library, managed model policy.

From evaluation to your first feature

How do I get started with deepface.dev?

Create an account, confirm your email and create a standard REST API key. Keep the key in your backend and send two permitted photos to /verify. Use matching and non-matching examples to choose the threshold for your workflow. You have 50 free calls each month to start. Follow the quickstart.

Can I keep my face database in my own stack?

Yes. Generate embeddings through /represent, store them with your subject identifiers and use your own database for search. Keep the model version and preprocessing pipeline consistent across stored and query embeddings. The customer-owned search guide shows the pattern.

What changes when moving from Rekognition?

For two-photo verification, map the image inputs and result handling to /verify, then evaluate the match threshold on representative examples. For a gallery, generate compatible embeddings from source images you can reuse, or re-enroll subjects. An AWS face identifier is not an embedding for the new pipeline. This gives you a concrete path from the existing workflow to an application-owned gallery.

Where does face matching fit in identity verification?

Use deepface.dev for the face-matching component. Add liveness, document checks and other controls when your identity workflow requires them. Keeping those jobs distinct helps you choose the components your application needs.

Build your face-matching workflow with deepface.dev

Give your coding agent the setup instructions, create your key and start with a photo comparison. Add reusable embeddings and your own gallery as the application grows. With deepface.dev, you start with the face-matching feature and keep the integration in your stack.

Get 50 free calls each month or talk to an engineer about your workflow.

By deepface.dev. Product and pricing references checked on 22 October 2026. Recommendations focus on the developer workflows described above.