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Pods or Serverless? A decision framework for persistent inference APIs [October 29, 2026](/blog/serverless-gpu-inference) Benchmarks on one A40 show when a Serverless endpoint costs less than an always-on Pod, and what latency, storage and debugging trade-offs come with the switch. What’s new Run the work that can wait as Batch Inference and keep real-time capacity for live traffic. Solution Go from experiment to production without replatforming. Pods, Serverless, and Clusters, all in one account. Case Studies 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](https://www.runpod.io/product/serverless) (autoscaling GPU endpoints that scale to zero when idle), [Pods](https://www.runpod.io/product/cloud-gpus) (GPU instances for persistent compute and development – available as Reserved (guaranteed) or Spot (interruptible, lower price), and [Clusters](https://www.runpod.io/product/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](https://www.runpod.io/pricing) AI IaaS is on-demand, cloud-based access to GPUs, networking, and storage; you [rent](https://www.runpod.io/pricing) 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](https://www.runpod.io/product/serverless) endpoints for fast inference tool calls, persistent [Pods](https://www.runpod.io/product/cloud-gpus) for stateful agents that need to stay running, and [network volumes](https://docs.runpod.io/storage/network-volumes) for shared memory and model weights across workers. The [Runpod skills package](https://docs.runpod.io/get-started/agent-skills) lets Claude Code, Cursor, and other coding agents deploy and manage Runpod resources directly. For inference at scale, Runpod [Serverless](https://www.runpod.io/product/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](https://www.runpod.io/product/clusters) support 200+ simultaneous GPUs with InfiniBand. For teams with compliance requirements, [Secure Cloud](https://www.runpod.io/product/cloud-gpus) 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](https://www.runpod.io/legal/compliance) for current certification details.