How to Build a Verifiable AI Inference API on Ethereum

Frequently Asked Questions

01.What is a verifiable AI inference API?

A verifiable AI inference API is a hosted endpoint that returns a machine learning prediction plus a cryptographic proof, usually a zero-knowledge proof, confirming the output came from a specific model and specific input without needing anyone to re-run or trust the server. Anyone can check the proof against a verifier contract on Ethereum in milliseconds.

02.How long does it take to build a verifiable AI inference API?

A working MVP with one production model, a proving service, and an on-chain verifier typically takes 8 to 10 weeks with an experienced Web3 and ML engineering team. Adding a second model architecture, audit remediation, or a custom identity registry usually extends the timeline by 3 to 5 weeks.

03.What does it cost to run zkML proof generation in production?

Proof generation on GPU-backed cloud infrastructure typically runs from a few cents to about half a dollar per inference depending on model complexity, plus on-chain verification gas of roughly 200,000 to 500,000 gas units per call depending on the proof system. Total build cost for an MVP generally lands between 40,000 and 80,000 dollars.

04.Do I need a technical team to launch a verifiable AI inference API?

You need access to ML engineers who can export models to ONNX and Solidity engineers who can integrate a verifier contract, but you do not need in-house zero-knowledge cryptography expertise. Frameworks such as EZKL abstract the circuit generation, which is why most operators use an experienced build partner instead of hiring a dedicated ZK team.

05.Is verifiable AI inference required by regulation?

No single regulation mandates zkML specifically, but frameworks including the EU AI Act and GDPR Article 22 already require traceability, logging, and contestability for automated decisions in credit, insurance, and hiring. Verifiable inference is one engineering path to meeting those obligations with a cryptographic audit trail instead of a manual one.