Zama builds FHE libraries. H33 runs FHE in production at 2.21 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 Engines | 1 (TFHE) — single scheme for all workloads | 4 (BFV-128, BFV-256, CKKS, FHE-IQ auto-router) — automatic engine selection in <500ns |
| Per-Operation Latency | 124ms (64-bit add) | 35.25µs per auth (3,200x faster) |
| Throughput | 189K bootstraps/sec (8x H100 GPU) | 2.21M auth/sec (CPU only, Graviton4) |
| ZKP Verify Latency | 123–467ms | 0.059µs (2,000,000x faster) |
| GPU Required | Yes — H100 coprocessors for performance | No — CPU-only (ARM Graviton4) |
| Hardware Cost | ~$15K/mo per GPU coprocessor | ~$2/hr spot instance |
| Post-Quantum Signatures | None | Dilithium + Kyber + FALCON + SPHINCS+ |
| ZKP Security | Not PQ-secure | SHA3-256 STARK — post-quantum secure |
| Side-Channel Protection | “Not yet implemented” | AI agent (1.14µs real-time detection) |
| Total Products | 5 | 38 |
| Pricing Model | Patent license for commercial use, contact sales | Self-service API, $0.001/auth at scale, BotShield free tier |
| Compliance | None | SOC 2 + HIPAA + ISO 27001 pending + HATS |
| Deployment Model | Self-hosted library (Rust / Python) | Managed REST API — one call, full stack |
| Python FHE Compiler | Concrete (TFHE only) | H33-Compile (4 engines + FHE-IQ auto-routing) |
Zama's TFHE takes 124ms for a 64-bit add. H33 completes a full biometric authentication — FHE match, ZK proof, and Dilithium signature — in 35.25µs. That is 3,200x faster per operation, on CPU only, with no GPU infrastructure required.
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 — no signatures, no ZKP, no side-channel protection. H33 combines lattice-based FHE, SHA3-256 STARKs, Dilithium + Kyber + FALCON + SPHINCS+ signatures, and a real-time AI side-channel agent into a single API call. Every layer is post-quantum secure.
Zama requires 8x H100 GPUs (~$15K/month) to reach 189K bootstraps/sec. H33 achieves 2.21M auth/sec on a single Graviton4 spot instance at ~$2/hr. That is 11x higher throughput at a fraction of the cost — with no GPU procurement, no CUDA dependencies, no driver updates.
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: ~937µs (32 users) // ZK proof + PQ attestation included // No parameters, no keys, no circuits
Head-to-head benchmarks on production workloads. All H33 numbers from Graviton4 c8g.metal-48xl (96 vCPUs, CPU only).
| Metric | Zama | H33 |
|---|---|---|
| Per-Operation Latency | 124ms (64-bit add) | 35.25µs per auth (3,200x faster) |
| Throughput | 189K bootstraps/sec (8x H100 GPU) | 2.21M auth/sec (CPU only) |
| ZKP Verify | 123–467ms | 0.059µs (2M x faster) |
| FHE Batch (32 users) | N/A (single-user) | 937µs |
| Dilithium Attest | N/A (no PQ sigs) | 189µs (1 per batch) |
| GPU Required | Yes (H100 coprocessors) | No (ARM Graviton4 CPU) |
| Hardware Cost | ~$15K/mo per coprocessor | ~$2/hr spot instance |
| Benchmark Variance | Not published | ±0.71% (120s sustained) |
Quantum computers will break RSA, ECDSA, and classical ZKPs. Only H33 secures every cryptographic layer against that threat.
| Layer | Zama | H33 |
|---|---|---|
| FHE | ✓ TFHE (lattice-based) | ✓ BFV + CKKS (lattice-based) |
| Digital Signatures | ✗ None | ✓ Dilithium + Kyber + FALCON + SPHINCS+ |
| Zero-Knowledge Proofs | ✗ Not PQ-secure | ✓ SHA3-256 STARK (PQ-secure) |
| Side-Channel Protection | ✗ “Not yet implemented” | ✓ AI agent (1.14µs real-time) |
| Key Exchange | ✗ Not included | ✓ ML-KEM (Kyber) hybrid |
Both platforms offer encrypted biometric processing — but the production readiness gap is significant.
Both platforms address encrypted computation on blockchain — with very different approaches and throughput.
Running machine learning models on encrypted inputs without decryption.
@h33.compile, get an FHE circuit with 4-engine auto-routing via FHE-IQZama focuses on FHE tooling. H33 is a full-stack production platform.
| Dimension | Zama | H33 |
|---|---|---|
| Total Products | 5 (TFHE-rs, Concrete, Concrete ML, fhEVM, fhEVM coprocessor) | 38 products (FHE engines, ZK verifiers, PQ signatures, biometrics, blockchain, detection, storage, video, search, and more) |
| Pricing Model | Patent license required for commercial use; contact sales for pricing | Self-service API; $0.001/auth at scale; BotShield free tier |
| Blockchain Pricing | $0.005–$1.00 per on-chain FHE operation | Credit-based pricing with volume discounts |
| Free Tier | Open-source libraries (BSD), commercial license required for production | 1,000 free operations/month, no credit card |
| Compliance | None | SOC 2 + HIPAA + ISO 27001 pending + HATS certified |