Serverless endpoints are great for testing. But when your AI agents run 24/7, you need a dedicated GPU that never sleeps, never queues, and never shares. Deploy any open model on dedicated infrastructure — from $0.50/hr.
You tested on serverless and it worked. Now your agents need to run all day, every day — with consistent latency, zero cold starts, and no rate limits.
Serverless spins down after inactivity. Your OpenClaw agent sends a request at 3AM and waits 30+ seconds for a cold boot. Dedicated is always warm — always ready.
Serverless shares capacity across all users. When traffic spikes, you get throttled. A dedicated endpoint is yours alone — no queuing, no 429s.
Pay-per-token adds up fast when agents run 24/7. A dedicated GPU at $1.80/hr costs less than $1,320/month — cheaper than serverless after ~2M daily tokens.
| Feature | Serverless (Free) | Serverless (Pro) | Dedicated ✦ |
|---|---|---|---|
| Cold starts | ✗ 10–60s | ~ Reduced | ✓ Zero — always warm |
| Rate limits | ✗ Strict | ~ Higher | ✓ None — your GPU |
| Model choice | ~ Curated list | ~ More models | ✓ Any Hub model |
| Custom containers | ✗ | ✗ | ✓ Full control |
| 24/7 agent use | ✗ Not designed for it | ~ Works, costly | ✓ Built for it |
| VPC / Private Link | ✗ | ✗ | ✓ Available |
| Autoscaling | ✓ Automatic | ✓ Automatic | ✓ Configurable |
| Cost at high volume | ✗ Expensive | ~ Moderate | ✓ Predictable flat rate |
From OpenClaw agents to OpenCode assistants — these are the open models the community runs on dedicated GPUs.
No per-token surprises. Pick your model, pick your GPU, see the flat monthly rate. Compare against what you'd pay on serverless.
Serverless is for demos. Dedicated is for the real stuff — agents that think, code, and orchestrate all day long.
Your OpenClaw agent monitors Slack, orchestrates tools, and responds 24/7. It needs a model that's always warm, always fast, and never rate-limited.
Pair-program with an AI that reads your codebase, writes patches, and debugs in real-time. Dedicated means no waiting for cold starts mid-flow.
You spent weeks fine-tuning Qwen3.5-9B for your domain. Now deploy it on dedicated infrastructure with vLLM for maximum throughput.
Ship AI features to thousands of users with predictable costs, guaranteed uptime, and VPC isolation. No shared infrastructure.
Run custom inference engines, benchmark new architectures, or test novel serving strategies — all on hardware you control.
Index millions of documents with TEI on dedicated CPUs from $0.033/hr. No token counting, no throttling, just throughput.
Choose any model from the 1M+ on the Hugging Face Hub — or upload your own fine-tuned weights.
T4, L4, L40S, A100 — pick the instance that fits your model and budget. AWS, GCP, or Azure.
vLLM, TGI, SGLang, TEI, or bring your own container. Optimized for throughput out of the box.
Get a secure API endpoint. OpenAI-compatible. Plug it into OpenClaw, OpenCode, or any app.
Join thousands of developers running open models on dedicated infrastructure. Predictable costs, zero cold starts, full control.