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What’s new
The State of AI Compute shows what teams actually deploy: agents are 8% of users but drive 24% of revenue, a new model version can take over its family's workloads in about a month, and lower precision is becoming a production strategy for large models.
Lost on how to connect to your pod with SSH? Start here.
Solution
Go from experiment to production without replatforming. Pods, Serverless, and Clusters, all in one account.
Case Studies
"We didn't seriously evaluate other alternatives before committing to Runpod. We found their approach interesting from the beginning, particularly the ease of integration, and we haven't had a reason to switch since."
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"AWS is very reliable and has everything we need, but it is way more expensive than Runpod. With Runpod, we can choose the right card for the job."
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"Runpod is easily one of the most scalable tools that we use in our infrastructure."
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"You guys have genuinely changed the scope of what we've been able to attempt."
"All of these projects, the renders for AMD, the Coca-Cola builds, that has to do with scalability. If we can't scale, we can't deliver. Runpod makes that possible."
"Runpod has changed the way we ship because we no longer have to wonder if we have access to GPUs. We've saved probably 90% on our infrastructure bill, mainly because we can use bursty compute whenever we need it."
"Runpod has allowed the team to focus more on the features that are core to our product and that are within our skill set, rather than spending time focusing on infrastructure, which can sometimes be a bit of a distraction.”
"Runpod helped us scale the part of our platform that drives creation. That’s what fuels the rest, image generation, sharing, remixing. It starts with training."
"Runpod allowed us to reliably handle scaling from zero to over 1,000 requests per second in our live application."
"Runpod has allowed us to focus entirely on growth and product development without us having to worry about the GPU infrastructure at all."
"We could stop worrying about infrastructure and go back to building. That’s the real win.”
“The main value proposition for us was the flexibility Runpod offered. We were able to scale up effortlessly to meet the demand at launch.”
"After migration, we were able to cut down our server costs from thousands of dollars per day to only hundreds."
Runpod’s scalable GPU infrastructure gave us the flexibility we needed to match customer traffic and model complexity, without overpaying for idle resources.
Run critical workloads with confidence, backed by industry-leading reliability.
Independently audited SOC 2 Type II compliance for end-to-end data protection.
Adapt instantly to demand with infrastructure that grows with you.
FAQs
Serverless, simplified. Clear answers on running your code without the fuss.
Runpod offers three primary infrastructure products: Serverless (autoscaling GPU endpoints that scale to zero when idle), Pods (GPU instances for persistent compute and development – available as Reserved (guaranteed) or Spot (interruptible, lower price), and Clusters (multi-GPU distributed compute for training and large-batch inference). All run on the same GPU catalog (H100 80GB HBM3, H100 NVL, A100, L40S, and more) accessible on-demand with no contracts or minimum commitments. See pricing
AI IaaS is on-demand, cloud-based access to GPUs, networking, and storage; you rent the capacity you need by the hour or second instead of buying and operating the hardware yourself. The tradeoff is the same one cloud computing has always offered: building your own data center gives you full control and, at very high sustained utilization, a lower per-hour cost, but it requires upfront capital, lead time to provision, and an ops team to keep it running. AI IaaS platforms like Runpod let you start serving traffic in minutes, scale to zero when there's no demand, and avoid being locked into hardware that ages out as new GPU generations ship.
AI agent infrastructure is the compute, storage, and networking layer that AI agents depend on to run, call tools, store memory, and scale. Runpod supports it across three layers: Serverless endpoints for fast inference tool calls, persistent Pods for stateful agents that need to stay running, and network volumes for shared memory and model weights across workers. The Runpod skills package lets Claude Code, Cursor, and other coding agents deploy and manage Runpod resources directly.
For inference at scale, Runpod Serverless gives you autoscaling GPU endpoints with sub-200ms cold starts (powered by FlashBoot) and a built-in job queue across 31 global regions. For training and fine-tuning at scale, Clusters support 200+ simultaneous GPUs with InfiniBand. For teams with compliance requirements, Secure Cloud provides network-isolated environments. Most teams use all three depending on the stage of their workload: Serverless for production inference, Pods for development and agent hosting, Clusters for training runs.
Yes. Runpod is SOC 2 Type II certified and HIPAA and GDPR compliant, with SOC 2 reports, BAAs, and DPAs available for security review through the Runpod Trust Center. Secure Cloud adds network isolation for workloads with stricter compliance needs. Coverage can vary by region and deployment model, so check requirements for your workload. Enterprise agreements add a 99.99% uptime SLA, dedicated capacity, and tailored terms. See runpod.io/legal/compliance for current certification details.
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