# Flide — AI Coding Tool Review & Pricing

> Flide is an open-source inference engine for Jev-style decision models. It serves the Jev `/v1/systemone` API and uses exact state forking to read a shared state once and answer many questions on it in one pass, returning a probability for every option so agents and pipelines can make thresholded decisions.

Source: https://ai.dosa.dev/tools/flide · Updated Nov 10, 2026 · Category: Hardware & Edge AI · Published by dosa.dev

## At a glance

- **Tool**: Flide
- **Company**: OmniJev
- **Category**: [Hardware & Edge AI](https://ai.dosa.dev/category/hardware-edge)
- **Pricing model**: Open Source
- **Official website**: https://flide.org/

## Key features

- `pip install flide` and `flide serve --model ...` start an API server
- Jev `/v1/systemone` API with choice and yes/no questions and per-option probabilities
- Exact state forking so each answer matches the model asked alone
- Serves Qwen3.5 and Gemma 4 chat models plus OneJev, Open-Jev, pplx-decider, Clef and other decision models
- LoRA adapters and image inputs for supported families
- `flide verify` checks exact forking on your own model and requests

## Pricing

**Model**: Open Source

Flide is free and open-source under the Apache-2.0 license and installs from PyPI. You run it on your own NVIDIA GPU; prebuilt kernels cover A10, A40, RTX 30, RTX 50, RTX PRO 6000, H100 and H200.

## Who it is for

- **Best for**: Teams running Jev-style decision models for agent routing, triage, classification or LLM-as-a-judge workloads who need high throughput on their own GPUs.
- **Not ideal for**: Developers looking for a coding assistant, general chat or text generation serving, or anyone without an NVIDIA GPU.

## Verdict

A fast-moving, specialised serving engine for the Jev decision model ecosystem; its speed claims are vendor benchmarks and it is still pre-1.0.

## Recent updates

Flide 0.1.12, released on November 10, 2026, added GLiDE, Nimble-v4-max and clef-flash models and precompiled kernels for RTX 50 series and RTX PRO 6000 GPUs; the project's Show HN on November 9 reached 124 points.

## Frequently asked questions

### Is Flide free?

Yes - Flide is Open Source. Flide is free and open-source under the Apache-2.0 license and installs from PyPI. You run it on your own NVIDIA GPU; prebuilt kernels cover A10, A40, RTX 30, RTX 50, RTX PRO 6000, H100 and H200.

### Is Flide open source?

Yes - Flide is open source. Flide is free and open-source under the Apache-2.0 license and installs from PyPI. You run it on your own NVIDIA GPU; prebuilt kernels cover A10, A40, RTX 30, RTX 50, RTX PRO 6000, H100 and H200.

### Who is Flide best for?

Teams running Jev-style decision models for agent routing, triage, classification or LLM-as-a-judge workloads who need high throughput on their own GPUs.

### Who is Flide not ideal for?

Developers looking for a coding assistant, general chat or text generation serving, or anyone without an NVIDIA GPU.

### What are the best Flide alternatives?

The closest Flide alternatives on ai.dosa.dev are Copperhead, NVIDIA Personal AI Router (PAIR), WebLLM - all listed under Hardware & Edge AI.

### Who makes Flide?

Flide is developed by OmniJev. It is listed in the Hardware & Edge AI category on ai.dosa.dev.

## Top alternatives

- [Copperhead](https://ai.dosa.dev/tools/copperhead) (Freemium) — Hardware engineers who want to automate design tasks, sync documentation with schematic changes, and maintain consistency in KiCad repositories.
- [NVIDIA Personal AI Router (PAIR)](https://ai.dosa.dev/tools/personal-ai-router) (Open Source) — Multi-agent AI workflows and concurrent inference tasks that benefit from distributing parallel requests across multiple local machines.
- [WebLLM](https://ai.dosa.dev/tools/webllm) (Open Source) — Developers building privacy-first web applications, in-browser AI assistants, or Chrome extensions that require local LLM execution without server-side infrastructure.
- [Magnitude](https://ai.dosa.dev/tools/magnitude) (Free) — Developers and power users running local coding agents on Apple Silicon or NVIDIA hardware who require high-performance, low-latency inference.
- [text-to-cad](https://ai.dosa.dev/tools/text-to-cad) (Open Source) — Rapid prototyping, creating custom brackets or fixtures, robotics development using URDF, and preparing manufacturable files from AI prompts.
- [nanochat](https://ai.dosa.dev/tools/nanochat) (Open Source) — Developers, researchers and students who want to understand or experiment with every stage of LLM training using a readable codebase and a modest GPU budget.
- Full alternatives guide: https://ai.dosa.dev/tools/flide/alternatives