Rekognition-compatible

High-accuracy face recognition API.

Index, search, compare, and detect faces at scale, billed per photo processed — with confidence scores you can threshold on.

Already using AWS Rekognition? Keep your boto3 code — change two lines and ship.

Start free — no card Try the live demo

Pay only for what you use · billed per photo, never per face · no minimums.

Why teams switch

PRICED PER PHOTO

$0.00062 a photo, batch

38% under AWS Rekognition's first US tier ($0.0010). real-time $0.00093. run the numbers yourself

GDPR READY

A real DPA, and deletion that actually works

Article 28 processor terms, Article 15/17 subject requests, never trained on your data

Run your own selfie against the live demo below.

don't take our word for it

See it recognize faces — with the JSON.

Search a gallery from one selfie, verify two faces 1:1, or detect and quality-gate. Every tab shows the actual Rekognition-shaped response, so you can see the engine is real and the API is compatible.

Query selfie

4 matches · 41ms

Collection (8 photos)

0.95

0.41

0.96

0.38

0.44

0.92

0.36

0.97

API responseRekognition-compatible

// SearchFacesByImage response
{
  "SearchedFaceConfidence": 99.81,
  "FaceMatches": [
    { "Similarity": 98.1, "Face": { "FaceId": "b3f1…a7" } },
    { "Similarity": 97.4, "Face": { "FaceId": "c2a4…b9" } },
    { "Similarity": 96.7, "Face": { "FaceId": "7e51…62" } },
    { "Similarity": 97.2, "Face": { "FaceId": "9a04…e2" } }
  ]
}

Faces shown are AI-generated — these people don't exist. The matched shots are the same generated identity with real appearance changes (glasses, clean-shaven, aging, different lighting and settings). Scores are representative. Measured accuracy, with methods and limits, is on the benchmarks page.

a customer, in numbers

How sellphotos.online cut their face recognition bill from $3,500 to $1,550 a month

A marketplace where event and sports photographers upload their shots and guests buy the ones they appear in.

~56%

reduction in monthly face recognition spend

Better

match accuracy on event and crowd photos

2 days

from first API call to full cutover

Previously on AWS Rekognition. Same volume of photos, same number of searches.

Read the case study

watch the explainer

SightRadar in 6 minutes.

What it does, how you build with it, how your data is handled, and what it costs.

why teams pick SightRadar

Two reasons it sticks.

Scores you can threshold

Calibrated similarity scores you set your own threshold on, and every search match labelled auto (safe to reveal) or review (confirm first). Measured results, methods and limits are on the benchmarks page.

Rekognition-compatible API

Every Rekognition face operation has a SightRadar equivalent. On Python, the official shim keeps your boto3 IndexFaces / SearchFacesByImage / CompareFaces calls and Rekognition-shaped responses with a two-line change. Other languages call the REST API.

migrate without a rewrite

Keep your code. Change two lines.

SightRadar mirrors AWS Rekognition's face operations. On Python, the official sightradar-rekognition-shim keeps Rekognition's method names, arguments and response shapes: swap the import and the constructor, and your existing calls keep working. From any other language, call the REST API.

Migration guide →SightRadar vs AWS Rekognition →

Supported operations

CreateCollection
IndexFaces
SearchFacesByImage
SearchFaces
CompareFaces
DetectFaces
ListFaces / DeleteFaces
ListCollections / DeleteCollection

# pip install sightradar-rekognition-shim

# was: import boto3
from sightradar_rekognition_shim import client

# was: rek = boto3.client("rekognition")
rek = client(api_key="frs_...")

# your existing calls, unchanged:
rek.search_faces_by_image(
    CollectionId="guests",
    Image={"Bytes": selfie},
    FaceMatchThreshold=80,
)

four moves, one clean API

Everything you need to see faces.

Index

Detect every face in a photo and store its embedding in your collection.

Search

Find every photo a person appears in from a single selfie.

Compare

Similarity between two faces — verify identity 1:1.

Detect

Locate and quality-gate faces without storing anything.

not on Rekognition? start here

Build it the way you already work.

Official SDKs

Typed clients for Python and Node / TypeScript: pip install sightradar, or npm install sightradar. Any other language calls the REST API.

SDK guides →

Let your AI agent set it up

Give the docs link to Claude Code, Codex, Cursor or Copilot. Every docs page has a Markdown version written for agents to read.

Agent setup →

MCP server

39 tools for Claude, ChatGPT, Cursor and VS Code. Ask in plain language to index photos or find a person's photos.

MCP server →

Send images your way

A link on your CDN or in any cloud storage, a file upload, or raw bytes. JPEG, PNG, WebP, iPhone HEIC, TIFF, GIF and BMP, up to 30 MB each.

Image inputs →

honestly, it's three steps

Set it once. Then just call it.

Grab a key

Index your faces

POST photos to a collection. We detect, embed, and store every face.

Search & verify

Send a selfie or compare two faces. Same calls you already know.

handle faces responsibly

Sensitive data, treated that way.

We never store your photos. An indexed face is kept only as a mathematical representation, in vector form, inside a collection you can delete at any time.

You control retention

Detect and compare process images in memory and don't persist them. Indexed faces live in collections you can delete any time, via API or console.

Isolated & encrypted

Each account's face vectors are namespaced and never cross-searchable. Encrypted in transit, scoped API keys, per-tenant isolation.

Never trained on your data

We process your data only to return your results. We do not pool it or use it to train general-purpose models, and we never sell it.

Built for consented use

You're the data controller. Our Terms require a lawful basis and consent — and prohibit unlawful surveillance or covert tracking.

Need it inside your own cloud?

With bring your own cloud we deploy our stack on your infrastructure: AWS, Google Cloud, Azure, or any other cloud. You pay a fixed monthly licence fee with no per-photo charges, and your data stays in your cloud.

Bring your own cloud

Read the full Privacy Policy and Terms.

and yes — it costs less

Billed per photo, not per face.

Pay only for what you use, billed per photo processed (never per face) — no surprise bills. Control-plane calls are free.

Real-time — $0.00093 per photo processed
Batch — $0.00062 per photo processed
Search by face-id — $0.00062 · storage $6 / M / mo

For scale: a 3,000-photo wedding gallery, with 120 guest searches and a month of storage, costs about $2.03.

See full pricing & calculator

Batch indexing

$0.00062

per photo processed

Pay only for what you use — no subscription, no minimums, no surprise bills. See the calculator for a side-by-side with AWS Rekognition.

Start with pay-as-you-go

Ship face search this week.

Free to start, pay only for what you use. Your first match in minutes — no rewrite.

Get your API key — free

Read the quickstart · Need it in your own cloud?