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Flyte

Flyte is an LF AI & Data project for building production data and ML pipelines: tasks and workflows are Python functions with strongly typed interfaces, each execution is versioned and cacheable, and the whole thing runs on Kubernetes with a Go control plane underneath.

LicenseOpen source (permissive)· Apache-2.0
DeploymentSelf-hostedManaged
PricingFreeSubscription
Operational complexityHigh
WorkloadBatch
LanguageGo

Use it when

  • Reproducibility is the feature: typed task interfaces are checked before anything runs, executions are versioned, and unchanged tasks are skipped through caching.
  • ML and data pipelines share one platform on Kubernetes, with Spark and Ray operator plugins.
  • Typed Python that produces containers for you beats writing container specs by hand: the Argo trade, one level up.

Think twice when

  • There is no Kubernetes cluster or platform team; the control plane is heavier than Argo's and much heavier than Prefect's.
  • Generation risk matters: Flyte 2.0 shipped in late 2025 with the 1.x line behind it, and the distributed Flyte 2 backend is still maturing.
  • Vendor concentration is a concern: an LF AI & Data project, but developed principally by Union.ai, which also serves its documentation.
  • Tasks must be authored in something other than Python; the Java SDK is archived.

How it runs

Self-hosted on Kubernetes: FlytePropeller creates a pod per task and deletes it after, connectors hand tasks to external services without holding a pod, and triggers cover cron, fixed rate, and artifact events. Typed I/O offloads large values to blob storage. Union.ai sells the managed platform, billed per action plus allocated compute.

Details

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How Flyte answers the questions Workflow Orchestration turns on.

Workflow Orchestration
How it works
Authoring modelTyped Python: tasks are functions whose inputs and outputs are checked before the run
Unit of workA task: an async Python function under @env.task, grouped by a TaskEnvironment carrying the image, resources and defaults. Flyte 2 dropped the static @workflow DAG: composition is ordinary Python, tasks awaiting tasks, and fan-out is asyncio.gather
Passing dataTyped function signatures checked against their annotations, with the io types (File, Dir, DataFrame) offloading large values to blob storage and passing a reference rather than the bytes
TriggersA Trigger attached to a task, automated by Cron with a five-field expression and a timezone, FixedRate in minutes, or OnArtifact, firing whenever a named artifact is produced; runs otherwise start from the CLI, the SDK or the API
Delivery guaranteeRetries per task; caching skips one whose inputs and version are unchanged
Caching and rerunscache="auto" hashes the function body, image and code bundle into a version, so editing the function invalidates the entry; "override" pins a version you set and "disable" turns it off. ignored_inputs drops inputs from the key, salt namespaces it, and serialize makes concurrent identical calls wait on one result
Running it
Where tasks runA pod per task, created and deleted by FlytePropeller; operator plugins for Spark and Ray, and Web API tasks that start no pod
Connections
Task languagesPython and nothing else: Flyte 2 ships one SDK, `flyte` on PyPI, and flytekit-java was archived in April 2026. Other languages run inside the task's own container image, or behind a connector
Connectorsflyte.connectors, the mechanism Flyte 1 called agents, a registry of async connectors that hand a task to an external service and poll it rather than holding a pod for the duration; anything not covered is a container image
Cost
Billing unitFree to self-host: Flyte 1 in full, and Flyte 2 through a single-binary chart still at v0.2.0 while its distributed backend is "coming soon". Union.ai, the vendor, bills per action at $0.0075, an action being one invocation of a task, trace or condition, with retries not counted and duration irrelevant, from $950 a month that is itself usage credit, plus allocated compute by the second

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