Best Face Recognition APIs in 2026: 7 Options Ranked for Photo Search and Face Matching

Ranked on price, sign-up access, rate limits and migration effort, with the source for every number

Disclosure: I work on SightRadar, and it is first in this list. That is a vendor's ranking, so I state the criteria up front, give a source for every number, and say where each other option is the better choice.

Most "best face recognition API" lists rank on brand size, or on features copied from each vendor's docs. Neither tells you what happens when you build with one. The things that decide a project are plainer:

1. Can you sign up and call it today? Some APIs need an approval form before they will identify anyone.
2. What does a real workload cost? Per image, per transaction and per month are three different bills.
3. What are the default rate limits? A backfill of 200,000 photos at 5 requests a second takes eleven hours.
4. How much code do you write? Especially if you already have AWS Rekognition code.
5. Does it do recognition at all? Detection (where is a face) is not recognition (whose face is it).

This ranking is for one job: building face search or face matching into a product. Think "find my photos with a selfie", duplicate-account checks, or matching a selfie to a stored face. If you need on-device AR tracking, document KYC or data kept on your own servers, a different option wins, and I say which one below.

Competitor prices and limits were read from each vendor's own pages on 2026-10-31. SightRadar's were read on 2026-11-13. All prices are US list prices.

The ranking at a glance

Google Cloud Vision is not ranked because it detects faces but does not recognise them. More on that at the end.

1. SightRadar: best for photo search and for teams leaving Rekognition

SightRadar is a face recognition API with the same collection model as Rekognition: create a collection, index photos, search with a selfie, compare two faces, detect faces. It does faces only.

Why it is first for this job:

• Price per photo, not per face. $0.00062 per photo for batch indexing, $0.00093 real-time, $0.00062 for a search by stored face ID. A photo with eight faces costs the same as a photo with one. Storage is $6 per million faces a month. (Pricing page)
• Self-serve. Sign up, create a key, call it. No approval form and no cloud account to set up first.
• Usable default limits. 50 requests per second per key. Batch jobs take up to 1,000 photo URLs in one call, answer by webhook, and do not count against the rate limit.
• Rekognition-shaped responses. The REST API returns fields such as BoundingBox ratios in the same shape. For Python, a shim package keeps your boto3 method names:

# before import boto3 rek = boto3.client("rekognition", region_name="us-east-1") # after from sightradar_rekognition_shim import client rek = client(api_key="frs_...") # unchanged rek.index_faces(CollectionId="event-2026", Image={"URL": "https://cdn.example.com/a.jpg"}, ExternalImageId="a") res = rek.search_faces_by_image(CollectionId="event-2026", Image={"Bytes": open("selfie.jpg", "rb").read()}, FaceMatchThreshold=90) 

• A dashboard that manages the whole thing. Collections, batches, usage and billing are screens, not API calls. Details and screenshots are below.
• Prepaid credits. No subscription and no minimum. When the balance runs out the API returns 402, and every billable response carries an X-Credits-Remaining header.

The dashboard. This is the part that is easiest to show, so here it is. The screenshots use sample data.

• Collections: every collection with its photo, face and selfie counts, filters by status, search by ID, a check of the live count against the vector store, and delete.
• Playground: run detect, index, search, selfie, search by ID and compare against your own collection in the browser, with boxes and match scores drawn on the image. These are real, billable calls.
• Batches: progress for each batch job, and a retry for the photos that failed.
• Usage: spend per operation and per day, storage shown separately, CSV export.
• Billing: a runway estimate (days left at your recent spend), auto-recharge, and email alerts when the balance runs low.
• Webhooks, keys and team: signed webhook endpoints, API keys with last-used times, and teammate invites.

For comparison, AWS's own guide says the Rekognition console manages Custom Labels and Custom Moderation, and "only provides demos of other Rekognition features" (AWS docs). So on Rekognition you list, inspect and delete face collections through the API or CLI. I have not compared every other vendor's console screen by screen, so check the ones on your shortlist.

What it costs on a real workload. An event photo business runs 40 events a month at 2,500 photos each. Guests make 2,400 selfie searches, and 300,000 faces stay stored.

SightRadar 100,000 batch photos x $0.00062 = $62.00 2,400 searches x $0.00093 = $2.23 300,000 faces x $6 / million = $1.80 total = $66.03 Amazon Rekognition 102,400 images x $0.0010 = $102.40 300,000 faces x $0.00001 = $3.00 total = $105.40 

You can price your own volumes, on four vendors at once, with the cost calculator.

Where it loses:

• Faces only. No labels, OCR, moderation, celebrity recognition, video or liveness.
• 1:1 compares cost more than on Rekognition. A compare is billed as two photos ($0.00186). AWS bills a CompareFaces call as one image ($0.0010).
• Very high volume. Above roughly 5 million images a month, Rekognition's lower tiers ($0.0006, then $0.0004) drop below the batch rate.
• The shim is Python only. Other languages call the REST API.
• Accuracy is published for event photos. The benchmarks page gives methods and limits. For KYC or access control, test on your own data first.

Pick it if you are building photo search, deduplication or selfie matching, or you have Rekognition face code you want to keep.

2. Amazon Rekognition: best if you are already on AWS, and at very large volume

Rekognition is the default for a reason. It is mature, well documented, and its face collections hold very large galleries. It also does labels, text, moderation and video, which nothing else here does in one API.

Pricing (AWS pricing page, us-east-1): $0.0010 per image for the first million a month, then $0.0008, $0.0006 and $0.0004 at higher tiers. Face storage is $0.00001 per face a month ($10 per million). Some regions cost more. Mumbai's first tier is $0.00125.

What to watch:

• Default quotas. IndexFaces, SearchFaces and SearchFacesByImage default to 5 transactions per second per account in most regions, and 50 only in us-east-1, us-west-2 and eu-west-1 (service quotas). You can ask for more, but a first backfill often runs into this.
• Input formats. JPEG and PNG only, 5 MB as raw bytes. iPhone HEIC uploads need converting first.
• You need an AWS account with IAM set up, which is real work if the rest of your stack lives elsewhere.

Pick it if you are on AWS already, you need labels or video as well as faces, your workload is mostly 1:1 compares, or you process more than about 5 million images a month.

3. Azure Face: best for approved enterprise customers

Microsoft's Face API has detection, verification (1:1), identification (1:N) and "find similar". On features it is the closest match to Rekognition.

The catch is access. Identification and verification are Limited Access features. You fill in a registration form, and Microsoft decides. In its words, access is "subject to Microsoft's sole discretion". Detection works without the form.

Pricing (Azure Face pricing): $1 per 1,000 transactions for the first million, falling to $0.40 at high volume. Storage is $0.01 per 1,000 faces a month. The free tier gives 30,000 transactions a month at 20 a minute, without identification or verification.

Azure bills per transaction, not per photo. A selfie search is usually a Detect call plus an Identify call, so two transactions. Indexing a photo with three faces can be four. Count your real calls before comparing its price with a per-photo one.

Pick it if you are a Microsoft enterprise customer, your use case is approved, and your compliance team wants Azure.

4. Luxand.cloud: best for small, steady workloads on a fixed bill

Luxand has a hosted API for recognition, verification and detection, with extras like age and gender estimates. It is quick to start with and has examples in many languages.

Pricing is a monthly plan with a request allowance, a rate limit and a cap on stored faces (Luxand pricing):

• Start: $9 a month, 1,500 requests, 20 a minute, 500 stored faces
• Basic: $39 a month, 10,000 requests, 100 a minute, 2,500 faces
• Pro: $99 a month, 200,000 requests, 150 a minute, 6,000 faces
• Business: $249 a month, 500,000 requests, 250 a minute, 18,000 faces

For photo search, the storage cap matters more than the request count. One 3,000-photo wedding produces about 9,000 face entries, which is more than the Pro plan stores.

Pick it if you have something like an office attendance app with a few hundred people and you want the same bill every month.

5. Face++: best for a wide attribute catalogue

Face++ (Megvii) is one of the oldest face platforms. Its catalogue is the widest here: detection, compare, search, dense landmarks, attributes, beauty scores and body analysis. The free tier has tight QPS limits, and paid throughput is sold as concurrency packages that you size in advance.

Check before choosing: Megvii has been on the US Entity List since 2019. Many US and EU procurement teams will ask about that, so raise it with yours early.

Pick it if you need its attribute and landmark range, and your compliance team is comfortable with the vendor.

6. Kairos: best when the real job is identity verification

Kairos began as a face recognition API and now focuses on identity verification: document checks, selfie-to-ID matching and fraud signals. Its site offers 100 free API calls at sign-up.

Pick it if you are building KYC onboarding with documents. For photo search it is not the natural fit.

7. CompreFace: best self-hosted option

CompreFace by Exadel is open source under Apache 2.0. It runs with Docker Compose and gives you a REST API for recognition, verification and detection, plus a web UI for managing subjects.

What "free" costs:

• A server to run it on, with a GPU if you need speed at volume
• Upgrades, backups, monitoring and security patches
• Scaling the vector search yourself as collections grow

Pick it if face data must stay on your own hardware and you have someone to run it.

Not ranked: Google Cloud Vision, and on-device SDKs

Google Cloud Vision appears in most lists, but for faces it does detection only. Google's face detection docs say "specific individual Facial Recognition is not supported". You get bounding boxes, landmarks and emotion likelihoods. You do not get collections or matching. It is a good choice for blurring or cropping faces.

On-device SDKs are a different product. If you need live camera tracking for AR effects or virtual try-on, a cloud API is the wrong shape, because every frame would be a network round trip. Look at mobile SDKs that run on the phone instead. None of the seven APIs above is built for that.

Which one should you pick?

• Photo search, selfie matching or deduplication in a product: SightRadar, or Rekognition if you are on AWS and above about 5 million images a month.
• Mostly 1:1 compares: Rekognition is the cheapest per compare.
• Faces plus labels, OCR or video in one API: Rekognition.
• Microsoft enterprise with approval: Azure Face.
• A few hundred people, fixed monthly bill: Luxand.cloud.
• KYC with documents: Kairos, or a dedicated identity-verification vendor.
• Data must stay on your servers: CompreFace.
• Only need to find faces, not identify them: Google Cloud Vision.

Two things to do before you commit

• Test on your own photos. Public benchmark scores say little about your lighting, cameras and crowd sizes. Take a few hundred real samples with known answers and run them through your top two options.
• Plan for re-indexing. Face vectors and thresholds do not transfer between engines. If you ever switch, you index again from the original photos and re-tune the threshold, so keep those photos.

FAQ

What is the best face recognition API in 2026?
It depends on the job. For photo search and face matching in a product, I rank SightRadar first on price per photo, self-serve access and default rate limits. Rekognition is the better choice on AWS at very large volume or for 1:1 compares, and Azure Face for approved Microsoft enterprise customers.

Which face recognition API is cheapest?
At list prices under a million images a month, SightRadar's batch rate ($0.00062 per photo) is 38% below Rekognition's first US tier ($0.0010). For 1:1 compares Rekognition is cheaper, because it bills one image per compare. Self-hosting CompreFace has no per-call price, but you pay for servers and the time to run them.

What is the difference between face detection and face recognition?
Detection finds where faces are in an image. Recognition says whose face it is, by matching it against stored faces. Google Cloud Vision does detection only. The seven ranked options all do recognition.

Can I switch from AWS Rekognition without rewriting my code?
In Python, SightRadar's shim keeps the boto3 method names, arguments and response shapes, so the change is the import and the constructor. In other languages you call the REST API. With any other vendor it is a rewrite. In every case you re-index your photos, because face vectors do not transfer.

I work on SightRadar. If a number for any vendor here is out of date, tell me in the comments and I will correct it.

This article was written with AI.