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

4.3 (4)

Reviewed by Daniel Nikulshyn·Updated July 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

Pricing

Model: Free
Category: WEB 3
Rating: 4.3 / 5 (4)

Use cases

Private Data Analysis

Research institutions and organizations can utilize BasedAI for confidential data analysis and modeling without compromising sensitive information.

Decentralized Content Generation

Content creators can tap into BasedAI's decentralized infrastructure for generating content while preserving intellectual property and maintaining user anonymity.

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

Sep 25, 2025

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

Sep 1, 2025

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

Jul 24, 2025

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

Jun 8, 2025

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.

Q&A

Are there any barriers to getting started as a node operator or user?

Participation relies on on‑chain token incentives, which can add complexity to onboarding for users unfamiliar with blockchain economics, and the smaller ecosystem may require additional effort to select and configure suitable models.

Asked by George Papadakis · Aug 30, 2025

What integration options does BasedAI provide for developers?

BasedAI offers a privacy‑preserving API that lets developers deploy or access encrypted model endpoints, enabling straightforward integration into existing applications while keeping prompts and outputs hidden from compute nodes.

Asked by Jana Krejčí · Aug 27, 2025

How does the homomorphic encryption affect inference speed compared to traditional APIs?

Because fully homomorphic encryption (FHE) must encrypt and decrypt data before and after computation, latency is significantly higher than standard, non‑encrypted inference, making real‑time responsiveness a limitation for some use cases.

Asked by Wolfgang Krause · Aug 21, 2025

What types of applications are best suited for BasedAI's privacy-preserving inference?

BasedAI excels in scenarios that require confidentiality, censorship resistance, or data sovereignty, such as enterprise document analysis, sensitive chat applications, and workflows in regulated industries where prompt and output privacy is critical.

Asked by Julia Steiner · Aug 15, 2025