The Durable
AI Runtime
Run workloads for training, serving, and
agents, auto-recover from any failure.
Built for scale, performance, and variable compute environments
No DSL, no YAML hell. A TaskEnvironment declares images, resources, secrets and retry policy; tasks are plain async functions. Branch, loop and fan out with ordinary control flow — the runtime records IO for every step so a crash resumes instead of restarting.
The same Python API serves long-running apps next to your workflows. Native integrations wrap Streamlit, FastAPI, vLLM, SGLang and Ollama — declare the app, image, resources and scaling; Flyte handles the serving plumbing.
OOM kills, preempted spot nodes, disappearing GPUs — Flyte is infra-aware and resubmits with corrected resources, automatically.
Retry and resume from the failed task with the exact same inputs. Never rerun the whole pipeline for one bad step.
Branch, loop and make decisions during execution — built for agents and dynamic pipelines, not static DAGs.
Infrastructure scales up and down to match workload demand. Right-size every task; stop burning idle GPU hours.
Thousands of parallel tasks and distributed jobs — map, fan-out and streaming map-reduce without rewriting your pipeline.
Serve vLLM and SGLang apps, sandbox agent code, and debug with a UI built for production runs.
Complex financial reports delivered on Flyte-orchestrated pipelines.
Read case study ↗ WayveAutonomous driving R&D, acceleratedScalable orchestration for petabyte-scale vision workloads.
Read case study ↗ CradleFaster ML for protein designModel development for generative biology on reproducible pipelines.
Read case study ↗Orchestrate, ship and scale AI systems from experiment to production: training, real-time inference, and observability on managed infrastructure.