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Runpod Serverless

San Francisco, California, United States

Plans and prices

Serverless GPU endpoint (141GB H200)

Free

H200 · 141 GB

Serverless GPU endpoint (16GB class)

Free

A4000, A4500, RTX 4000, RTX 2000 · 16 GB

Serverless GPU endpoint (24GB class)

Free

L4, A5000, 3090, MIG 24GB · 24 GB

Serverless GPU endpoint (32GB 5090)

Free

5090 · 32 GB

Serverless GPU endpoint (32GB RTX PRO 4500 Blackwell)

Free

RTX PRO 4500 Blackwell · 32 GB

Serverless GPU endpoint (48GB A6000/A40)

Free

A6000, A40 · 48 GB

Serverless GPU endpoint (48GB L40/L40S/6000 Ada/MIG)

Free

L40, L40S, 6000 Ada, MIG 48GB · 48 GB

Serverless GPU endpoint (80GB A100)

Free

A100 · 80 GB

Serverless GPU endpoint (80GB H100)

Free

H100 · 80 GB

Every plan, in full

On the record

Autoscaling
The product overview says endpoints scale from zero to hundreds of workers with request demand, and flex workers scale to zero when idle.runpod.io · read 9 Oct 2026
Billing
Serverless compute is billed per second from worker start to full stop, rounded up to the nearest second.runpod.io · read 9 Oct 2026
Cold starts
FlashBoot is described as providing sub-200ms cold starts, while pre-warmed workers can provide zero cold starts.runpod.io · read 9 Oct 2026
Compliance scope
Runpod says compliance coverage can vary by workload, region, provider, and deployment model, and directs customers to confirm specific requirements with its team.runpod.io · read 9 Oct 2026
Containers and runtimes
Users can bring a Docker container and configure runtimes for languages and frameworks including Python, Node.js, Go, Rust, C++, PyTorch, TensorFlow, JAX, and ONNX.runpod.io · read 9 Oct 2026
Deployments
Workers can be deployed from GitHub, with code and Dockerfile built into a container image and deployed to an endpoint.docs.runpod.io · read 9 Oct 2026
Integrations
The documentation describes OpenAI-compatible endpoint integration with tools including n8n, CrewAI, and LangChain.docs.runpod.io · read 9 Oct 2026
Intended workloads
The product page identifies inference, fine-tuning, agent systems, rendering, and simulations as workloads for Serverless.runpod.io · read 9 Oct 2026
Purpose
Runpod Serverless runs containerized AI inference workloads behind API endpoints and scales workers based on demand.runpod.io · read 9 Oct 2026
Regions
The product page says users can deploy in global regions to place workloads closer to users.runpod.io · read 9 Oct 2026
SDKs
Runpod documents Serverless SDKs for Python, JavaScript, and Go.docs.runpod.io · read 9 Oct 2026
Security and compliance
Runpod states that it completed SOC 2 Type II and SOC 3 examinations, maintains HIPAA and GDPR programs, and holds an ISO/IEC 27001:2022 certification for its information security management system.runpod.io · read 9 Oct 2026
Storage
The product page describes persistent network storage that scales with workloads.runpod.io · read 9 Oct 2026
Support
Runpod documentation directs users to submit a support request, check the service status page, or join its Discord community.docs.runpod.io · read 9 Oct 2026

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