Livepeer BYOC + Ollama: LLM Freedom for AI Agents

“Claude code is so insanely expensive. It hurts to spend $150/mo actively developing.”
— @1saarim, January 2026

“4M input tokens / 15k output tokens in 15 mins usage. The API is expensive.”
— @mathew_noel, September 2025

“With claude code banning outside usage everyone will quickly realize how expensive their workflows are if they actually pay for tokens.”
— @lazy_coll, January 2026

This is the conversation happening right now on X. Developers building AI agents are bleeding money — and starting to question whether centralized LLM APIs are sustainable.

I built something to fix that.

The Problem with Centralized LLM APIs

Commercial LLM APIs are convenient. Drop in an API key, call POST /v1/chat/completions, and you’re running. Until you’re not.

The problems show up fast when you’re building autonomous agents:

1. Rate limits kill autonomy. An agent that can only think 60 times per minute isn’t autonomous — it’s hobbled.

2. Privacy is a myth. Your prompts, your users’ data, your system instructions — all of it flows through someone else’s servers. Read the ToS. They’re watching.

3. Costs scale unpredictably. Token-based pricing means your costs balloon exactly when your agent is doing useful work.

4. Single points of failure. One provider goes down, your entire fleet of agents goes dark.

For the OpenClaw ecosystem — where AI agents run continuously, handle sensitive personal data, and need to think fast — this is untenable.

The Real Numbers

Let’s talk actual costs. From developers on X this week:

One developer, @tista, shared his Claude Max experience: “Today I spent about $100 of tokens and that’s because I forgot to switch to Sonnet, I let Claude Code use Opus first.”

The pattern is clear: token-based pricing punishes agents that think.

And it’s not just cost. @agentic_austin spent a week wrestling with OpenClaw on a DigitalOcean VPS. His takeaway? Multi-agent sessions “bogged down my server and broke my config several times.”

The infrastructure isn’t designed for continuous, autonomous operation. It’s designed for chatbots with humans in the loop.

Why Agent Builders Are Looking for Alternatives

The AI agent community is at an inflection point.

A year ago, the only option was centralized APIs. You ate the costs, accepted the surveillance, and hoped you didn’t get rate-limited at the wrong moment.

Now the cracks are showing:

• Claude banning “outside usage” — the terms can change anytime
• Hosted solutions with no escape hatch — one developer complained about being “stuck in recurring payment until Savio checks his email”
• Google disabling accounts — @willkriski got disabled for using OAuth via a third-party tool

The message is clear: if you don’t control the infrastructure, you don’t control your agent.

Decentralized compute isn’t just about cost. It’s about sovereignty.

Enter BYOC: Bring Your Own Container

Livepeer has built something powerful for builders: BYOC (Bring Your Own Container).

BYOC lets developers create custom containers and run them on Livepeer’s decentralized GPU network. It’s not just about video transcoding anymore — you can deploy any containerized compute workload.

The network handles:

• Job routing — requests flow through Gateways to available Orchestrators
• Payment — ETH-based, per-compute-unit pricing on Arbitrum
• Discovery — Orchestrators advertise capabilities, Gateways find them

You build the container. The network runs it on decentralized GPUs. No single point of failure. No corporate surveillance. Just compute.

What I Built: Expanding the Ollama LLM Runner with BYOC

In my previous post, I showed how to run LLM inference on Livepeer using an Ollama-based GPU runner. That opened the door for Orchestrators to accept LLM jobs on 8GB+ GPUs.

This project takes it further: a full BYOC implementation that exposes a standard OpenAI Chat Completions API.

Your code doesn’t change. You point your OpenAI SDK at a new base URL. Behind the scenes:

Your Agent (OpenAI SDK)
  → Livepeer Gateway (BYOC routing)
    → Decentralized GPU Node (Ollama)
      → LLM inference (Llama 3.1, Mistral, etc.)

The result: drop-in OpenAI compatibility, running on decentralized infrastructure.

The Architecture

Client
→ OpenAI Proxy (translates to BYOC headers)
→ Livepeer Gateway (routes to available Orchestrator)
→ BYOC Runner (forwards to Ollama)
→ Ollama (runs the actual LLM)

Two lightweight Go services handle the translation:

1. OpenAI Proxy — Accepts standard POST /v1/chat/completions requests, wraps them in Livepeer’s BYOC header format, and forwards to the Gateway.

2. BYOC Runner — The actual BYOC container that receives requests from the Gateway and forwards them to Ollama with full streaming (SSE) support.

Both are designed for byte-for-byte streaming passthrough. When you request "stream": true, tokens flow back in real-time, exactly like calling OpenAI directly.

Why This Matters for OpenClaw Users

Here’s the pitch: OpenClaw users can tap into Livepeer’s LLM infrastructure for a decentralized approach — without the hassle of centralized providers.

It uses an OpenAI-compliant endpoint. If your agent already works with OpenAI, it works with this. Change the base URL, pick a model, done.

And models? You’ve got options. Anything available on Ollama’s model library is fair game:

• Llama 3.1 (8B, 70B, 405B)
• Mistral and Mixtral
• Phi-3 and Phi-4
• Qwen, Gemma, DeepSeek
• Dozens more, constantly updated

OpenClaw agents are different from chatbots. They:

• Run continuously (heartbeats, cron jobs, monitoring)
• Handle personal data (emails, calendars, messages)
• Make decisions autonomously
• Need to think fast

Every one of those requirements breaks on centralized LLM APIs.

The Real Comparison

The Code

The entire project is open source:

👉 github.com/Cloud-SPE/livepeer-byoc-openapi-ollama

Quick Start

# Clone the repo
git clone https://github.com/Cloud-SPE/livepeer-byoc-openapi-ollama.git
cd livepeer-byoc-openapi-ollama

# Configure your Ollama upstream
# Edit docker-compose.yml: UPSTREAM_URL=http://your-ollama:11434/v1/chat/completions

# Start everything
docker compose up --build

Once running, test it:

# Non-streaming
curl -sS http://localhost:8090/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.1:8b",
    "stream": false,
    "messages": [{"role":"user","content":"Hello from Livepeer!"}]
  }'

# Streaming (SSE)
curl -N http://localhost:8090/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.1:8b",
    "stream": true,
    "messages": [{"role":"user","content":"Count to 10."}]
  }'

Using with OpenAI SDK

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'http://localhost:8090/v1',
  apiKey: 'not-needed' // Livepeer handles auth differently
});

const response = await client.chat.completions.create({
  model: 'llama3.1:8b',
  messages: [{ role: 'user', content: 'What is Livepeer?' }],
  stream: true
});

for await (const chunk of response) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

What’s Next

This is a foundation. The pieces that make it production-ready:

1. Multi-model routing — Different models for different tasks. Use a small model for quick classification, a large one for complex reasoning.

2. Failover — If one Orchestrator goes down, automatically route to another.

3. Cost optimization — Bid on compute across the network. Let the market find the best price.

4. OpenClaw integration — Native support for Livepeer BYOC as an LLM backend. Point your agent at the network, not a single endpoint.

The goal: make decentralized LLM inference the default for autonomous agents.

No rate limits. No surveillance. No single points of failure. Just compute, when you need it, at a fair price.

The Choice

You can keep paying $150/mo to rent someone else’s compute. Keep hoping your prompts aren’t training their next model. Keep praying the ToS doesn’t change overnight.

Or you can run your own infrastructure on a decentralized network.

BYOC isn’t for everyone. It requires Docker knowledge, a willingness to self-host, and comfort with a newer ecosystem.

But if you’re building agents that need to think continuously, handle sensitive data, and operate autonomously — the math is simple.

Own your compute. Own your data. Own your agent.

Get Involved

If you’re running Livepeer infrastructure and want to offer LLM workloads, check out my previous post on enabling 8GB+ GPUs for LLM inference.

If you’re building AI agents and want to escape the centralized API trap, try the BYOC setup. It’s a few Docker containers and you’re running.

Questions? Find me on the Livepeer Discord — @mike_zoop in the #orchestrating channel.

Or hit me up on Twitter: @mikezupper

The future of AI isn’t APIs controlled by a handful of companies. It’s decentralized compute, open models, and infrastructure you actually own. Livepeer BYOC is a step toward that future.