Running OpenClaw on OpenAI gets expensive fast. Here’s the setup that cuts your API bill dramatically without gutting capability.
If you’ve been running OpenClaw with GPT-4o as your default, you’ve probably noticed the API costs creeping up, especially if you’re running multiple bots, doing heavy image generation, or just using it constantly throughout the day. I was in the same spot until I started using DeepInfra as my primary provider. It’s been one of the most practical changes I’ve made to my setup.
OpenClaw’s 2026.4.27 release made this easier than ever by bundling DeepInfra as a first-class built-in provider. Here’s what it is, why it works so well, and how to get it running in a few minutes.
What Is DeepInfra?
DeepInfra is an OpenAI-compatible inference platform that hosts a massive catalog of open-weight models, Llama, Mistral, SDXL, Whisper, Wan, and dozens more, and charges you per token, per image, or per second of audio/video. No monthly minimums, no seat fees, no surprises.
The OpenAI-compatible part is key. It means any tool that speaks to OpenAI’s API can point at DeepInfra instead with minimal friction. You’re not swapping out an integration, you’re just changing where the requests go.
Cost-wise, the difference is real. GPT-4o input runs around $2.50 per million tokens. A comparable open model on DeepInfra, say, Llama 3.1 70B, runs closer to $0.35–0.59 per million tokens depending on context size. That’s roughly a 4–7x cost reduction for text. Image generation is similarly cheaper than DALL-E 3, and TTS is competitive with OpenAI’s audio endpoints.
Why It Works Great with OpenClaw
OpenClaw’s DeepInfra integration isn’t just a generic “add a custom OpenAI-compatible provider” config. It’s a proper bundled provider, which means a few things:
Drop-in compatibility. The OpenAI-compatible API means OpenClaw can use DeepInfra for everything it already does, chat, tool calls, function calling, streaming, without any special adapter layer.
Dynamic model discovery. OpenClaw auto-discovers the models available on your DeepInfra account. You don’t need to manually maintain a list of model IDs. If DeepInfra adds a new model, OpenClaw picks it up automatically.
Full stack coverage. This is the part that impressed me most. DeepInfra isn’t just a text inference service. With OpenClaw’s integration, you get:
- Text generation: Chat, completions, tool use
- Image generation: SDXL and other diffusion models
- Vision / image understanding: Multimodal models that can analyze images
- Audio understanding: Whisper-based transcription
- Text-to-speech: TTS endpoints
- Text-to-video: Video generation via models like Wan 2.1
That’s a complete AI stack through a single provider at a fraction of the cost. No juggling separate API keys for each capability.
How to Set It Up
Getting DeepInfra working with OpenClaw takes about five minutes.
Step 1: Get your DeepInfra API key
Go to [deepinfra.com](https://deepinfra.com), sign up, and navigate to your account dashboard. Create a new API key. DeepInfra gives you a small free credit when you sign up, which is enough to test everything out before committing.
Step 2: Add the API key to OpenClaw
Open your OpenClaw config:
openclaw config
Find the DeepInfra provider section and paste in your API key. Alternatively, you can set the environment variable:
export DEEPINFRA_API_KEY=your_key_here
If you’re running OpenClaw as a persistent service, add the variable to your shell profile or your service environment file so it persists across restarts.
Step 3: Select DeepInfra as your provider
In OpenClaw’s settings, set DeepInfra as your default provider (or select it per-session). Because of dynamic model discovery, OpenClaw will pull the current model list automatically when you configure it.
Step 4: Pick a model and test
Start a new session, ask something simple, and confirm it routes through DeepInfra. You’ll see model responses that look and behave like you’d expect from a well-tuned open model.
That’s it. No complicated setup, no custom endpoints to configure.
Best Models to Use on DeepInfra with OpenClaw
Here are the ones I’d recommend as starting points:
For everyday chat and reasoning: `meta-llama/Meta-Llama-3.1-70B-Instruct`, This is my daily driver. Strong reasoning, good instruction following, fast enough for real-time use. Use this for anything GPT-4o was handling day-to-day.
For a fast, cheap default: `mistralai/Mistral-7B-Instruct-v0.3`, When you want low latency and minimal cost for simple queries, quick lookups, or high-volume tasks where quality can be slightly lower.
For image generation: `stability-ai/sdxl`, Drop-in for DALL-E 3 tasks within OpenClaw. Solid quality for most use cases. Cheaper per image than OpenAI.
For vision / image analysis: `llava-hf/llava-1.5-13b-hf` or a current multimodal Llama variant, Handles image understanding tasks like describing screenshots, reading receipts, or analyzing charts.
For transcription: `openai/whisper-large-v3`, Yes, you can run Whisper on DeepInfra. Same model, fraction of the cost if you’re doing significant audio transcription volume.
Real Cost Comparison
These are approximate numbers based on publicly listed pricing at the time of writing. Prices change, so treat these as ballpark figures:
| Task | OpenAI | DeepInfra Equivalent | Approx. Savings |
|---|---|---|---|
| 1M input tokens (GPT-4o vs Llama 3.1 70B) | ~$2.50 | ~$0.35–0.59 | ~75–85% |
| 1M output tokens (GPT-4o vs Llama 3.1 70B) | ~$10.00 | ~$0.59–0.89 | ~90%+ |
| Image generation (DALL-E 3 vs SDXL) | ~$0.04/image | ~$0.003–0.008/image | ~80–90% |
| Whisper transcription (per minute) | ~$0.006 | ~$0.001–0.003 | ~50–75% |
If you’re running a personal OpenClaw setup doing, say, 500k tokens a day across chat and tasks, you’re looking at potentially saving $50–100/month compared to GPT-4o pricing. For devs running multiple bots or agents? The savings stack fast.
Limitations to Know
DeepInfra is great, but it’s not a perfect drop-in for every scenario. A few things to keep in mind:
No GPT-4o equivalent (yet). Open models have closed the gap significantly, but for very complex multi-step reasoning or tasks that lean heavily on GPT-4o’s specific capabilities, you may notice a quality difference. For most daily assistant tasks, you won’t.
Latency varies by model. Smaller models are fast. The 70B+ models can be slower than GPT-4o, especially at peak times. If response speed matters more than cost for a specific use case, keep a fast small model as a fallback.
Free tier rate limits. DeepInfra’s free tier has rate limits. If you’re doing burst usage or high-volume testing, you’ll want to add a small credit balance to get access to higher throughput.
Model-specific tool support. Not every model supports function calling or structured output in exactly the same way. The mainstream Llama and Mistral instruct models work well with OpenClaw’s tool use. Smaller or more niche models may have quirks.
Some newer OpenClaw features are OpenAI-first. If you’re on a feature that was just released, double-check whether it’s been validated against DeepInfra’s API. The core capabilities are solid; cutting-edge features may lag slightly.
Final Thoughts
DeepInfra is the right fit if you’re a hobbyist who doesn’t want a surprise $80 API bill, a developer running multiple OpenClaw agents and watching spend carefully, or anyone who needs the full AI stack, text, images, audio, video, without paying OpenAI’s premium on every capability.
For most OpenClaw use cases, Llama 3.1 70B on DeepInfra will handle everything GPT-4o was doing, at a fraction of the cost, with the same OpenClaw features. That’s a genuinely good deal.
The 2026.4.27 release made this a first-class supported setup, not a hack. Set it up once, forget about it, and stop worrying about your API bill.
*Prices and model availability current as of May 2026. Always check deepinfra.com and the OpenClaw provider docs for the latest.*
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About The Author
This is exactly what I was looking for. I’ve been running GPT-4o for everything and my API bill is getting out of hand. Going to test DeepInfra this week. How stable is it compared to OpenAI’s API in your experience?
Happy to help, Marco! DeepInfra has gotten significantly more reliable over the past year — uptime is solid and latency is competitive for most use cases. The main difference vs OpenAI is that you’re usually running open-weight models (Llama, Mistral, etc.) rather than proprietary ones, so for complex reasoning tasks you may notice a gap. That said, for most automation, summarization, and chat workflows the cheaper models do the job fine. Start with Llama 3.1 70B — it’s a great balance of cost and capability. Keep OpenAI as a fallback for the tasks that actually need it.
Love how detailed this is. I set up OpenClaw last month and it’s been a game changer. Didn’t realize I could swap providers this easily though — definitely trying this.
Glad it clicked, Sarah! One tip: once you add DeepInfra as a provider in your OpenClaw config, you can set per-session model overrides so only your heavier tasks route to the pricier models. Heartbeat and routine automation can run on cheap open-source models without you even noticing the difference. Saves a lot month-to-month.