Zama builds FHE libraries. H33 runs FHE in production at 2.17 million operations per second. Four FHE engines, ZK-STARK proofs, and Dilithium signatures — delivered as a single REST API call. No parameter tuning. No cryptography PhD required.
How an open-source FHE library compares to a production FHE platform with integrated ZK and post-quantum cryptography.
| Feature | Zama | H33 |
|---|---|---|
| FHE Scheme | TFHE (single scheme) | BFV, CKKS, BFV-32, FHE-IQ — 4 engines |
| Production Readiness | Library — you build the infrastructure | REST API — production-ready with monitoring |
| Throughput | Varies by implementation | 2.17M ops/sec sustained (Graviton4, 96 workers) |
| Biometric Matching | Manual implementation required | 939µs per batch (32 users/ciphertext) |
| ZK Proof Integration | Not included | ZK-STARKs with SHA3-256 — built in |
| Post-Quantum Signatures | Not included | ML-DSA (Dilithium) + ML-KEM (Kyber) — built in |
| Pricing | Open source (BSD) — self-hosted costs | $0.033 per operation — volume discounts available |
| Deployment Model | Self-hosted library (Rust / Python) | Managed REST API — one call, full stack |
Zama offers TFHE — a single FHE scheme optimized for boolean circuits. H33 provides BFV for batched integer arithmetic, CKKS for approximate floating-point computation, BFV-32 for lightweight operations, and FHE-IQ for adaptive workloads. Choose the right engine for your use case without switching libraries.
Zama gives you building blocks. H33 gives you a production service. No parameter tuning, no noise budget management, no key rotation infrastructure to build. Send a request, get cryptographically verified results. Deployed and monitored on AWS Graviton4 with sub-millisecond FHE batch latency.
Zama provides FHE computation only. H33 combines FHE biometric matching, ZK-STARK zero-knowledge proofs, and Dilithium/Kyber post-quantum signatures into a single API call. Every authentication is encrypted, proven, and quantum-safe — with no additional integrations required.
H33 processes 2.17 million operations per second sustained on production hardware. Each operation includes FHE computation (939µs per 32-user batch), a ZK-STARK proof, and a Dilithium digital signature — approximately 38.5 microseconds per individual authentication. Benchmarked, reproducible, and battle-tested.
FHE with a library vs. FHE with an API — the developer experience difference.
# Zama: manage parameters, keys, circuits from concrete import fhe @fhe.compiler({"x": "encrypted"}) def match_biometric(x): return (x - template) ** 2 circuit = match_biometric.compile(inputset) circuit.keys.generate() encrypted = circuit.encrypt(biometric_data) result = circuit.run(encrypted) decrypted = circuit.decrypt(result) # You manage: parameters, noise, keys, # deployment, scaling, monitoring...
// H33: one call, full cryptographic stack const result = await h33.authenticate({ biometric: capturedTemplate, securityLevel: 'h33-128', mode: 'standard' }); // result.verified → true / false // result.attestation → Dilithium-signed proof // result.zkProof → ZK-STARK verification // result.fheEngine → 'BFV' (auto-selected) // // FHE batch: ~939µs (32 users) // ZK proof + PQ attestation included // No parameters, no keys, no circuits