If you searched “Venice AI”, you’re usually looking for two things:
A less restricted AI chat experience (fewer “I can’t help with that” walls)
Stronger privacy defaults than mainstream chatbots
This tutorial does two jobs:
Explain what Venice AI is (and what it isn’t).
Show you two concrete paths:
Use Venice (web app + API).
Or run your own models on Zo Computer so your AI work is durable (files + automation) and under your control.
What is Venice AI?
Venice AI positions itself as “private” and “uncensored” for AI conversations and generation (text and images).^1
For developers, Venice also offers an OpenAI-compatible API (same basic interface shape as OpenAI’s API, but with Venice’s base URL and additional Venice-specific parameters).^2
When Venice AI is a good fit
Use Venice AI if you want:
A “single app” experience for chatting and generating media, without wiring up a stack
An OpenAI-compatible API endpoint to swap into existing tooling (SDKs, proxies, agents)^2
A model menu where you can select an “uncensored” model explicitly (instead of guessing what guardrails you’ll hit)^2
When you should self-host instead
Even if Venice is “private” by design, there are cases where you still want full custody:
You want your AI workflow to be persistent (projects, scripts, outputs, logs)—not just chats.
You want reproducibility (the same prompt + the same files yields the same output next week).
You want to automate jobs (scheduled runs, monitoring, transforms) on infrastructure you control.
That’s where Zo Computer fits: it’s a real server with a filesystem and automation, so you can run models locally and treat your data as first-class.
How to use Venice AI (fast path)
Open Venice and start a chat.
Pick the model you want.
If you want web-backed answers, use a model/setting that enables web search.
Venice’s public entry point is here:^1
https://venice.ai/character-chat/public
How to use the Venice API (practical developer path)
Step 1: Create an API key
Venice’s API docs walk through generating an API key and storing it as an environment variable.^2
Step 2: Call the API with an OpenAI-compatible client
Venice’s API uses this base URL:^3
https://api.venice.ai/api/v1
From their quickstart, the idea is:
Use an OpenAI client
Swap
base_urlto VeniceProvide your Venice API key
Step 3: Use Venice-specific parameters when you need them
Venice exposes extra request options (for example enabling web search or web scraping) via venice_parameters.^3
Self-hosted alternative on Zo (recommended if you care about custody)
If what you really want is “AI that I control and can automate”, don’t stop at swapping chat apps. Put the work on a machine that can:
store state in files
run code repeatedly
stay up when your laptop sleeps
Option A: Run a local LLM on Zo with Ollama
Ollama is the simplest on-ramp for running a local model behind an HTTP API.
Follow this tutorial (verified link):
https://www.zo.computer/tutorials/how-to-run-a-local-llm-on-zo-computer-ollama^4
If you want the broader “local LLM on a server” version:
https://www.zo.computer/tutorials/how-to-run-a-local-llm-on-your-server^5
Option B: Keep the “Venice API” convenience, but run the workflow on Zo
A common pattern is:
Use Zo as your durable workspace (files + scripts + automation).
Use Venice as one of several model backends.
This avoids the biggest practical pain of chat-only tools: losing context, losing files, and not being able to schedule work.
If you want to automate recurring work on Zo (monitoring, digests, change detection), start here:
https://docs.zocomputer.com/agents^6
A concrete workflow you can copy
If you’re deciding between Venice and self-hosting, try this simple test:
Pick one recurring task (for example: “summarize new GitHub issues weekly” or “monitor a status page”).
Do it once in Venice.
Then do it on Zo, storing the inputs/outputs in files and scheduling it.
If the Zo version feels obviously better, you’re not looking for “another chatbot”—you’re looking for an AI runtime.