Best Face Recognition & Comparison APIs in 2026

If you are adding identity checks to an app, such as selfie-to-ID verification at signup, face login, employee check-in or duplicate account detection, a face recognition API saves you from training, hosting and tuning your own models.

The market changed in 2025 and 2026: Google Vision still does not recognize faces, a well-known open-source server stopped shipping releases, and two long-standing APIs pivoted or went offline. Here is what developers can actually sign up for today, and the catches you only find in the docs.

Want to see how it handles your own photos? Try the Face Analyzer API on a pair of images.

Detection, comparison, search: which one you need

Vendors use "face recognition" loosely. Pin down the operation first, because not every API offers all three:

• Face detection: finds faces and returns boxes, landmarks and sometimes attributes (age range, emotion, glasses). It does not tell you who the person is.
• Face comparison or verification (1:1): "are these two photos the same person?". The core of selfie-to-ID checks and face login.
• Face search or identification (1:N): enroll known faces in a gallery, then ask "who is this?" for a new photo. Attendance systems and duplicate account detection need this.

The options at a glance

• AI Engine Face Analyzer: detection with attributes, 1:1 comparison, 1:N search in repositories. Plain REST with one key. No liveness check.
• Face++ (Megvii): detect, compare, search in FaceSets. The free key shares a 3 queries-per-second capacity with other users, and failed requests are billed.
• Luxand.cloud: recognition, verification, liveness, emotion and age in every plan. Each plan caps how many faces you can store.
• Google Cloud Vision: detection only. Its docs state that recognizing specific individuals is not supported.
• CompreFace: open source (Apache 2.0), self-hosted with Docker. Last release August 2023, so updates are on you.
• Kairos: now documented as an identity verification product (documents, selfie-to-ID, liveness); the classic gallery API is gone.
• Betaface: public API listed as temporarily down.

Compare two faces (1:1)

import requests

HEADERS = {
    "x-rapidapi-host": "faceanalyzer-ai.p.rapidapi.com",
    "x-rapidapi-key": "YOUR_API_KEY",
}

resp = requests.post(
    "https://faceanalyzer-ai.p.rapidapi.com/compare-faces",
    headers=HEADERS,
    data={
        "source_image_url": "https://example.com/id-photo.jpg",
        "target_image_url": "https://example.com/selfie.jpg",
    },
)
body = resp.json()["body"]
print("Same person" if body["matchedFaces"] else "Different person")

The response lists matchedFaces and unmatchedFaces with bounding boxes and landmarks. You can upload files instead with the source_image and target_image multipart fields.

Search a face among enrolled users (1:N)

API = "https://faceanalyzer-ai.p.rapidapi.com"
REPO = "employees"

# 1. Create a repository once
requests.post(f"{API}/create-facial-repository", headers=HEADERS,
              data={"repository_id": REPO})

# 2. Enroll each person with your own ID
requests.post(f"{API}/save-face-in-repository", headers=HEADERS,
              files={"image": open("alice.jpg", "rb")},
              data={"repository_id": REPO, "external_id": "alice", "max_faces": "1"})

# 3. Identify a new photo
resp = requests.post(f"{API}/search-face-in-repository", headers=HEADERS,
                     files={"image": open("door-camera.jpg", "rb")},
                     data={"repository_id": REPO})
for match in resp.json()["body"]["FaceMatches"]:
    print(match["ExternalImageId"], round(match["Similarity"], 2))

We enrolled two people and searched with a third photo of one of them in a different pose. Only the right person came back, with a similarity of 99.99:

The full comparison has the cost of each API at 10,000 calls a month side by side.

Which one should you choose?

• AI Engine if you want comparison and 1:N search with one key and a few lines of REST, plus face attributes from the same API
• Luxand.cloud if you need liveness bundled with recognition from one vendor
• Face++ if you are fine with shared throughput on the free key and want per-call billing
• CompreFace if images must never leave your infrastructure and you can maintain the stack
• Google Cloud Vision only if detection is all you need

For complete builds, see the guides to selfie-to-ID verification (eKYC) and a face recognition attendance system.

Sources

• Google Cloud Vision face detection docs: detection only, no recognition
• Face++ pricing: per-call prices and free key limits
• Luxand.cloud pricing: plans and included features
• CompreFace on GitHub: licence, features and release history

Read the full guide with the pricing table and the Kairos and Betaface details on ai-engine.net.