BasedAI

Decentralized AI network combining homomorphic encryption with large language models for private inference.

4.3 (4)

Reviewed by Daniel Nikulshyn·Updated May 2026

Overview

BasedAI is a decentralized network designed to run large language models with privacy guarantees built in. By integrating fully homomorphic encryption (FHE) techniques with LLM inference, it aims to let users query AI models without exposing prompts or outputs to the nodes performing the computation. The network distributes workloads across independent operators, with incentives coordinated on-chain. Developers can deploy or access privacy-preserving model endpoints, while node operators contribute compute in exchange for rewards. This setup targets use cases where confidentiality, censorship resistance, or data sovereignty matter, such as enterprise document analysis, sensitive chat applications, and regulated industries.

Key features

Homomorphic encryption for prompts and outputs
Decentralized LLM inference network
On-chain incentives for node operators
Privacy-preserving API access
Distributed compute across independent nodes
Support for confidential AI applications

Pros & Cons

Pros

End-to-end private inference via homomorphic encryption
Decentralized infrastructure reduces single points of failure
Open participation for node operators
Suited for sensitive or regulated data workflows

Cons

FHE adds significant latency versus standard inference
Smaller ecosystem than centralized AI providers
Token-based economics may complicate onboarding
Model selection more limited than mainstream APIs

Reviews

4.3

Average from 4 ratings.

Ahmed Saleh

Solid for our team

We rolled this out across the team last quarter and suited for sensitive or regulated data workflows. Homomorphic encryption for prompts and outputs fits neatly into how we already work, and decentralized LLM inference network removed a step we used to do by hand. FHE adds significant latency versus standard inference, which is the main caveat, but it has held up under daily use.

Tomáš Novák

Does the job

Pretty happy overall. On-chain incentives for node operators just works and open participation for node operators. Smaller ecosystem than centralized AI providers can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Fatima Zahra

Years in this space

I've evaluated a lot of these over the years. What stands out here is support for confidential AI applications — handled better than most — and end-to-end private inference via homomorphic encryption. Worth the time if this is your use case.

Joanna Kowalski

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on on-chain incentives for node operators, and end-to-end private inference via homomorphic encryption caught me off guard. FHE adds significant latency versus standard inference is why this isn't a perfect score, still, I'd recommend giving it a real trial.