The Chips Got Faster. The Stack Didn't.
June 2, 2026
Why faster chips have shifted the bottleneck to AI infrastructure, and what that means for teams running production workloads.
The AI Developer Cloud
Experiment, train, fine-tune, deploy, scale on one AI system
Trusted by over one million developers at the world’s leading AI companies
What’s new
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With MIG, we can partition RTX 6000 Pro cards into isolated 24 GB instances. Here's when it makes sense for your workloads.
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Solution
One platform. Full lifecycle.
Go from experiment to production without replatforming. Pods, Serverless, and Clusters, all in one account.
Case Studies
In production. At scale.
See what our customers are building.
"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.”
Setup process was great—very quick and easy. Runpod had the exact GPUs we needed for inference and the pricing was very fair.
“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.
99.9% Uptime
Run critical workloads with confidence, backed by industry-leading reliability.
Secure by default
Independently audited SOC 2 Type II compliance for end-to-end data protection.
Scale to thousands
of GPUs
Adapt instantly to demand with infrastructure that grows with you.
FAQs
Questions? Answers.
Serverless, simplified. Clear answers on running your code without the fuss.
What GPU infrastructure does Runpod offer for AI workloads?
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 Instant 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
What is AI Infrastructure as a Service (IaaS), and how does it compare to building your own?
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.
What is AI agent infrastructure and how does Runpod support it?
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.
What are the best AI infrastructure solutions for deploying models at scale?
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, Instant 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.
Is Runpod suitable for production AI infrastructure?
Yes. Runpod commits to an SLA with a 99.99% uptime guarantee, and data is hosted across data center partners holding certifications including SOC 2, ISO 27001, and HIPAA, depending on location. Secure Cloud adds network isolation for workloads with stricter compliance needs, and enterprise customers can arrange dedicated capacity and tailored terms through a direct agreement. See runpod.io/compliance for the current SLA and certification details.
