AI Frameworks · flyteorg

flyte

Flyte is an open-source workflow orchestration platform for machine learning and data pipelines, written in Go with Python SDK support. It enables teams to define, schedule, and execute complex AI/ML workflows at scale using declarative Python code, with local development and Kubernetes-native deployment.

Talk to DEV.co Discuss Implementation

Source: GitHub — github.com/flyteorg/flyte

7.1k

GitHub stars

845

Forks

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	flyteorg/flyte
Owner	flyteorg
Primary language	Go
License	Apache-2.0 — OSI-approved
Stars	7.1k
Forks	845
Open issues	202
Latest release	v2.0.27 (2026-07-02)
Last updated	2026-07-08
Source	https://github.com/flyteorg/flyte

What flyte is

Flyte 2 provides a Python-first SDK for defining tasks and workflows with async support, container-based execution via TaskEnvironment, and gRPC-based communication. The open-source backend for distributed Kubernetes deployment is listed as 'coming soon'; production use currently relies on Union.ai's hosted service.

Quickstart

Get the flyte source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/flyteorg/flyte.git2cd flyte3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

ML Pipeline Orchestration

Define complex multi-step ML workflows in pure Python with automatic containerization, caching, and dependency management. Suitable for data preprocessing, model training, and inference pipelines.

Local-to-Cloud Development Workflow

Start developing and testing workflows locally using the Flyte CLI and TUI, then deploy to production infrastructure without code changes. Reduces friction between development and operations.

Model Serving with FastAPI Integration

Combine workflow orchestration with model serving using the FastAPIAppEnvironment extension. Useful for teams needing both batch ML pipelines and real-time inference endpoints.

Implementation considerations

Kubernetes cluster required for production deployment; local development can use in-process or container-backed execution via Flyte CLI.
Python 3.x SDK is primary interface; Go codebase manages orchestration backend, but developers interact primarily via Python decorators and CLI.
Container image definition and management (via flyte.Image) is central to task isolation; base image selection and dependency pinning must be planned upfront.
Async task execution supported natively (asyncio.gather pattern); synchronous workflows also supported but may underutilize concurrency capabilities.
Open-source backend still in development; evaluate whether hosted Union.ai service or waiting for self-hosted backend aligns with timeline and vendor-lock concerns.

When to avoid it — and what to weigh

Require Self-Hosted Enterprise Backend Today — Open-source backend for distributed Kubernetes deployment is marked 'coming soon.' Current production use requires Union.ai's hosted service; self-hosted options are not yet available in the OSS repo.
Need Strict License Restrictions Beyond Apache 2.0 — Apache 2.0 permits commercial use but requires license/copyright notice and discloses patent rights. If your compliance policy restricts patent clauses or permissive licenses, conduct legal review.
Simple Cron/Task Scheduling is Sufficient — Flyte is purpose-built for complex, data-aware orchestration. For basic scheduled task execution, lighter tools (e.g., Airflow, cron) may be more appropriate and have lower operational overhead.
Non-Containerized or Legacy Compute Environments — Flyte's execution model assumes containerized tasks and Kubernetes. Environments without Docker/container support or requiring direct host execution are not well-aligned with the platform.

License & commercial use

Apache License 2.0 (ALv2). Permissive OSI-approved license allowing commercial use, modification, and distribution with attribution and patent protections for contributors.

Commercial use is permitted under Apache 2.0. However, verify alignment with your legal/compliance team regarding patent clause implications. Backend deployment strategy (Union.ai hosted vs. future self-hosted OSS) will determine commercial support and SLA arrangements; clarify vendor-lock and support terms before committing to production use.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Container-based execution provides process isolation. gRPC communication is standard; TLS/mTLS support typical but specific hardening steps not detailed in provided data. No security audit, vulnerability disclosure process, or penetration testing results mentioned. Evaluate backend security posture (authentication, RBAC, audit logging) with Union.ai if using hosted service, or upon OSS backend release. Secrets management approach (env vars, mounted secrets) requires explicit design.

Alternatives to consider

Apache Airflow

Mature, widely-adopted workflow orchestration with extensive operator ecosystem. Stronger community ecosystem but higher operational complexity; better suited for heterogeneous environments.

Prefect 2.x

Python-native workflow platform with Pydantic-based tasks and strong local development experience. Similar ease-of-use but different backend architecture and ecosystem.

Kubeflow Pipelines

Kubernetes-native ML pipeline orchestration; tight integration with Kubernetes and strong ML-ops features. Steeper learning curve; less Python-first ergonomics than Flyte 2.

Software development agency

Build on flyte with DEV.co software developers

Try Flyte 2 locally with pip install flyte, explore the live demo, or contact us to discuss production deployment options and integration with your ML infrastructure.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

kusion kubetorch flybirds

Related on DEV.co

Explore the category and the services that help you build with it.

AI Frameworks AI Development DevOps & Deployment Talk to DEV.co

flyte FAQ

Can I use Flyte for production ML workflows without Union.ai's hosted service?

Not yet with the open-source release. The README states the open-source backend is 'coming soon.' Production use currently requires Union.ai's hosted service. Contributions to the OSS backend may accelerate self-hosted availability.

Is Flyte 2 backward-compatible with Flyte 1 workflows?

No. Flyte 2 is a major redesign in architecture and Python SDK. Flyte 1 is maintained separately on the master branch. Migration requires workflow rewrite.

What container runtimes does Flyte support?

Not explicitly stated in provided data. Flyte.Image abstractions suggest Docker-based images; assume Docker/containerd are expected, but confirm with documentation or backend specs.

Can I run Flyte workflows without Kubernetes?

Yes, for development and testing. The Flyte CLI supports local execution (in-process or container-backed devbox mode). Production distributed execution requires Kubernetes; self-hosted backend details are pending.

Software development & web development with DEV.co

Need help beyond evaluating flyte? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and ai frameworks integrations — and maintain them long-term.

Ready to Orchestrate ML Workflows?

Try Flyte 2 locally with pip install flyte, explore the live demo, or contact us to discuss production deployment options and integration with your ML infrastructure.

Talk to DEV.co Schedule a Consultation