Hot starts, batch inference, and what's next for Runpod Serverless. Webinar June 25.

Infrastructure for AI agents running on GPU inference

GPU endpoints built for agents that scale instantly, eliminate idle costs, and handle high-throughput workloads.

Free account. No credit card required. Start running in minutes.

AI agents infrastructure illustration

Agents don't work like normal apps. They loop, branch, call tools, spawn sub-agents, and make decisions mid-run. Traditional cloud functions weren't designed for any of that. GPU endpoints that scale on demand, start instantly, and cost nothing when idle, that's what agent workloads need.

How it Actually Works

GPU endpoint workflow illustration

Your agent calls a GPU endpoint

Run LLM inference, image generation, and embeddings. Deploy as a Serverless endpoint.

Research infrastructure feature illustration

The endpoint scales with the task

One request or ten thousand. Workers spin up when needed and scale back to zero when done.

Runpod feature workflow illustration

You only pay while it runs

Per-second billing with no idle costs. You only pay for active compute, with no reserved capacity required.

What people are deploying

LLM backends

Image + video

Embeddings + RAG

Tool execution

Multi-agent systems

Fine-tuned models

Works with the stack you already have

  • LangChain / LangGraph
  • LlamaIndex
  • Mastra
  • AutoGPT, CrewAI, custom loops
  • OpenAI-compatible API
  • Vercel AI SDK
  • MCP (Model Context Protocol)
  • Any HTTP client
AI agent framework stack diagram

Want your agent to provision its own GPU compute? Flash lets you deploy any Python function to Runpod Serverless with a decorator. The agent calls it. The GPU runs. You pay for the seconds.

Built for Production AI

vLLM-optimized LLM serving

Any HuggingFace model, deployed in minutes. PagedAttention and continuous batching for high-throughput inference.

Sub-200ms cold starts with FlashBoot

Pre-warmed worker pools eliminate initialization latency. Your users don't wait for infrastructure.

Global regions, low-latency routing

31 regions. Deploy closer to your users. Route traffic intelligently across your worker pool.

Bring your own container

Python, Node.js, Go, Rust, C++. PyTorch, TensorFlow, JAX, ONNX. Or your own custom runtime. No rewrites, no lock-in.

GitHub-native deployment

Push to GitHub, auto-release to your endpoint. Roll back instantly. Zero downtime on updates.

Persistent model storage

Network volumes keep your models loaded and ready. No re-pulling from HuggingFace on every cold start.

Builders are already running here

"Runpod is the only place I can deploy high-end GPU models instantly. No sales calls. No rate limits."
Amjad Masad
7B+
requests served across Runpod Serverless
500k
developers using the platform

Pay per second.
Nothing when idle.

Simple pricing for agent workloads. Start small. Scale when you need to.

Free account. No credit card required.

Flex workers start at fractions of a cent per second and cost nothing between requests. Active workers eliminate cold starts with up to 30% discount. Pick the tradeoff that fits your workload.

Cheapest GPU

$X/sec

Entry point for lightweight workloads

Most common GPU

$Y/sec

Popular for production agent workloads

Start in minutes.

From sign-up to working endpoint in a few steps.

Runpod feature workflow illustration

1. Create a free account

No credit card required.
Get started in minutes.

GPU endpoint workflow illustration

2. Deploy an endpoint

Pick a model or bring your own container. Running in minutes.

Agent API call workflow illustration

3. Call it from your agent

Use HTTP, an OpenAI-compatible
API, or Flash.

Build what’s next.

Build, train, and scale AI workloads on Runpod with cloud GPUs, Serverless, and Clusters.