New SDK removes infrastructure complexity for AI developers and agent builders on Runpod

NEWARK, N.J., April 30, 2026 /PRNewswire/ — Runpod, the AI developer cloud, today announced the general availability of Runpod Flash, an open-source Python SDK that removes the infrastructure overhead between writing AI code and running it in production. With Flash, developers go from a local Python function to a live, auto-scaling endpoint in minutes, with no containers to build, no images to manage, and no infrastructure to configure. Flash is available now on PyPI and GitHub under the MIT license.

Why Runpod built Flash

“We’ve built one of the largest serverless inference platforms in the industry, and Flash makes it even faster to get on it.” said Zhen Lu, Runpod CEO and Co-founder. “A local Python function becomes a live, auto-scaling endpoint in minutes, on the same per-second billing and scale-to-zero economics our developers already run on. Flash is what continuous improvement looks like at the pace AI moves.”

“We’re also seeing a shift in how AI applications are built. Agents don’t fit neatly into one container or one endpoint. They need to call different models, route between different compute types, and scale on demand. Flash and Runpod Serverless were designed for exactly that kind of workload.”

“Flash deploys your Python functions serverlessly — no Dockerfile, no registry, no ops overhead.” said Philippe Gilles, Senior Engineering Manager, Nodalview. “Having one piece of code that works the same no matter where I run it, whether it’s here or whether it’s in the cloud…that’s really what I want.” said Craig Pfeifer, AI Engineer, TCG Inc.

Inference is the next phase of AI infrastructure

AI infrastructure is shifting. The industry’s first wave of spending was dominated by training: building foundation models required massive, sustained compute. The next wave is inference, where those models are put to work in production applications serving real users. Inference workloads now represent the fastest-growing segment of AI cloud spend, and the tooling needs are fundamentally different: variable demand, latency sensitivity, cost pressure at scale, and the need to deploy and iterate quickly.

Runpod has emerged as a major platform for inference workloads. Over 750,000 developers use Runpod to build and deploy AI, with 37,000 serverless endpoints created in March 2026 alone and over 2,000 developers creating new endpoints every week. Teams at Glam Labs, CivitAI, and Zillow run production inference on the platform. The company has reached $120M in annual recurring revenue.

Flash accelerates this momentum by removing the last major friction point in the deployment workflow. Rather than spending time on container configuration and registry management, developers can focus on the application logic and get to production faster.

A platform for the agentic era

Agentic AI is emerging as the dominant pattern in production AI. Autonomous systems that reason, plan, and take action need infrastructure that can handle unpredictable call patterns, chain multiple model calls, and mix different compute types within a single workflow. The container-first deployment model was built for static services, not for the fluid orchestration that agents require.

Flash was designed with this shift in mind. Flash Apps let developers combine multiple endpoints with different compute configurations into a single deployable service. An agent’s orchestration layer can run on one type of compute while the underlying model inference runs on another, all managed and scaled as one unit. Combined with Runpod Serverless’s scale-to-zero economics, Flash becomes a natural compute backbone for agentic systems that need to call models on demand without paying for idle infrastructure.

How it works

Flash supports two deployment patterns. Queue-based processing handles batch and async workloads. Load-balanced endpoints serve real-time inference traffic. Developers specify their compute requirements and dependencies directly in Python, and Flash handles provisioning, scaling, and infrastructure management automatically.

Endpoints auto-scale from zero to a configured maximum based on demand, and scale back down when idle. Flash also includes a command-line interface for local development, testing, and production deployment, giving developers a complete workflow from experimentation to shipping.

Beyond standalone endpoints, Flash Apps support multi-endpoint applications for production architectures that require different compute configurations working together. Developers can prototype on Runpod Pods, package their logic with Flash, deploy to Serverless, and scale to production without switching providers.

Runpod’s position in AI infrastructure

The AI cloud market has grown past $7 billion with over 200 providers, but developers still face difficult tradeoffs. Hyperscalers offer scale but come with complex toolchains, lock-in, and high costs. Neoclouds require enterprise contracts and minimum commitments. Point solutions handle one workload well but force developers to replatform as their needs evolve.

Runpod occupies the gap between these options: self-serve access, a developer-native experience, full lifecycle coverage from experimentation through production, at an affordable cost. Flash extends that position by making the deployment experience match the simplicity of the rest of the platform.

Availability and resources

Flash is available today and can be installed via standard Python package managers. Developers can start deploying within minutes.

Blog: www.runpod.io/blog/flash-is-ga
GitHub: github.com/runpod/flash (MIT license)
Documentation: docs.runpod.io/flash
Examples: github.com/runpod/flash-examples
Requirements: Python 3.10+, macOS or Linux, Runpod account

About Runpod

Runpod is the AI developer cloud. The platform provides the infrastructure AI developers need across the full lifecycle: experiment, train, fine-tune, deploy, and scale. Over 750,000 developers build on Runpod. Specifically for AI workloads, Runpod is the fastest path from AI experiment to production. For more information, visit runpod.io.

 

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SOURCE Runpod