H33 vs Zama Concrete: FHE Compilers Compared
Why Rust-native production FHE with attestation outperforms a Python FHE compiler
Zama Concrete represents an important thesis: FHE complexity can be hidden behind a Python compiler. Write a function, annotate inputs as encrypted, and Concrete generates the circuit. For accessibility, it is a genuine contribution. But accessibility and production readiness are different properties.
H33 is a Rust-native production pipeline with BFV, CKKS, and TFHE, STARK proofs, and three-family PQ signatures. It processes 2,293,766 authentications per second at 38 microseconds each on Graviton4. Single binary, REST API, zero serialization between stages.
Python Overhead
Concrete compiles to Rust/C for FHE operations -- the hot loop is not Python. But orchestration is: parameter selection, circuit optimization, key management, encoding, decoding. For production throughput, the GIL, memory management, and Python-to-native serialization become significant. H33 runs everything in Rust. No interpreter, no GIL, no serialization between layers. 192 workers run in parallel without contention.
Scheme Coverage
Concrete is TFHE-only. BFV is dramatically faster for integer arithmetic (batch polynomials vs individual bits). CKKS provides native floating-point for ML inference. H33's FHE-IQ routes each sub-computation to its optimal scheme. Concrete users needing BFV or CKKS lose compilation convenience.
Attestation
Concrete provides computation without verification, signing, or attestation. For regulated industries, results without provenance are useless. H33 includes STARK proofs, three-family PQ signatures (ML-DSA-65, FALCON-512, SLH-DSA-SHA2-128f), and 74-byte H33-74 distillation -- integral to the pipeline, not an add-on.
Production Readiness
Concrete targets development: Python interface, Jupyter support, automatic parameters. H33 targets production: Rust binary, REST API, Docker, ARM64 optimization, built-in metrics. Concrete needs a Python environment and custom API wrappers. H33 needs a binary and environment variables.
Performance
H33: 2,293,766 auth/sec sustained, 38 microseconds each, BFV with 4,096 SIMD slots plus STARK plus three-family signing. FHE stage: 943 microseconds per 32-user batch. TFHE fundamentally operates at lower throughput than BFV for workloads BFV handles.
When Each Is Right
Concrete excels for data scientists exploring FHE, rapid prototyping in Jupyter, and TFHE-specific workloads. H33 is for production throughput, multi-scheme FHE, integrated attestation, and post-quantum security. The gap between accessible and deployable is the gap between research and production.
Contact support@h33.ai to evaluate which fits your requirements.