Flyte
Dynamic, resilient AI orchestration. Coordinate data, models, and compute as you build AI workflows.
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What is Flyte?
Flyte is an open-source, Kubernetes-native workflow orchestration platform designed for building production-grade data, machine learning, and AI pipelines. It enables teams to author workflows in pure Python without learning a domain-specific language (DSL), with built-in durability, reproducibility, and automatic failure recovery. Flyte dynamically orchestrates complex, long-running, and agentic workflows with autoscaling and infrastructure awareness.
Key features include writing workflows in actual Python, self-healing workflows that retry automatically and pick up where they leave off, dynamic workflow execution with real-time logic and conditions, infra-aware orchestration for provisioning resources, built-in caching and versioning for fast repeatable runs, visualization of plots and data through reports, and the ability to run and test workflows locally using the same Python SDK that runs in production on Kubernetes. Flyte supports integrations with Apache Spark, BigQuery, PyTorch Elastic, Ray, Snowflake, and Weights & Biases.
Flyte is for data scientists, ML engineers, and AI development teams who need to rapidly ship high-quality code to production. It is used by companies like LinkedIn, Spotify, Freenome, Pachama, Warner Bros. Discovery, Wayve, Cradle, Kineo, and MethaneSAT for processing petabyte-scale data, analytics workflows for business and finance, and machine learning pipelines for logistics, image processing, cancer diagnostics, autonomous driving R&D, and protein design.
Flyte 2 OSS is the free open-source version for building durable AI/ML pipelines and agents, while Union.ai is the enterprise platform offering massive scale at 50k+ actions/run, real-time inference with sub-second latency, live remote debugger, reusable warm-start containers under 100ms startup time, observability into resource usage and data lineage, and white-glove support from expert AI engineers.
Flyte pricing
Pricing model: Freemium
Flyte 2 OSS is free and open source - you deploy and manage Flyte yourself on your own cloud infrastructure. It includes full suite of features for orchestrating AI workflows, dynamic AI/ML workflows, infra-aware orchestration, dynamic workflow execution, self-healing workflows, and local execution. Union.ai is the enterprise Flyte platform with paid plans (contact vendor for pricing, no free trial or freemium version). Union.ai includes everything in Flyte 2 OSS plus massive scale at 50k+ actions/run, ultra-low latency, orchestrate/train/serve on unified platform, real-time inference with sub-second latency, live remote debugger, reusable warm-start containers with <100ms startup, observability into resource usage/data lineage/versioning, and white-glove support from expert AI engineers.
Flyte pros
- Pure Python authoring with no DSL required
- Kubernetes-native workflow orchestration
- Built-in durability and reproducibility
- Self-healing workflows with automatic retry
- Dynamic workflow execution with runtime conditions
- Infra-aware orchestration for resource provisioning
- Run and debug workflows locally with same SDK
- Built-in caching for fast repeatable runs
- Strongly typed interfaces with type validation
- End-to-end data lineage and observability
- Autoscale compute dynamically to match workload
- Supports Python, Java, Scala, JavaScript SDKs
- Language-agnostic - can use R or any language
- Native integrations with Spark, Ray, PyTorch, Snowflake
- Visualize plots and data with FlyteDecks reports
- Scheduling for periodic workflow execution
- Fanout, parallelism, and error handling support
- Handles petabyte-scale data processing
- Works for data, ML, analytics, bioinformatics use cases
Flyte cons
- Self-hosted - you deploy and manage on your own infrastructure
- Requires Kubernetes cluster to run
- Steeper learning curve than simpler orchestrators
- Open-source version lacks enterprise features
- No free trial for Union.ai enterprise platform
- No freemium version for Union.ai
- Deployment complexity for platform engineers
- Limited official support without paid enterprise plan
Frequently asked questions about Flyte
What is Flyte?
Flyte is an open-source, Kubernetes-native workflow orchestration platform for building production-grade data and ML pipelines. It is strongly typed, reproducible, and built for teams. Flyte enables highly concurrent, scalable and reproducible workflows for data processing, machine learning and analytics, used by teams at LinkedIn, Spotify, Freenome, Pachama, Warner Bros., and many others.
What programming languages does Flyte support?
Flyte has first-class support for Python with its official Python SDK (flytekit). It also has community-driven Java and Scala SDKs. Flyte is language-agnostic, meaning you can define pipelines in R or any language. Python, Java, Scala, and JavaScript SDKs are available.
Is Flyte free to use?
Yes, Flyte 2 OSS is free and open source. You deploy and manage Flyte yourself on your own cloud infrastructure. It provides a full suite of powerful features for orchestrating AI workflows. Union.ai is the commercial enterprise platform with paid plans.
What is the difference between Flyte 2 OSS and Union.ai?
Flyte 2 OSS is the open-source platform for building durable AI/ML pipelines and agents with community support. Union.ai is the enterprise platform that includes everything in Flyte 2 OSS plus massive scale at 50k+ actions/run, ultra-low latency, real-time inference with sub-second latency, live remote debugger, reusable warm-start containers with <100ms startup time, observability features, and white-glove support from expert AI engineers.
Can I run Flyte locally?
Yes, Flyte 2 offers local execution. Flyte 2 Devbox is available to run a full Flyte backend and UI locally. You can test and debug tasks in your local environment using the same Python SDK that runs in production on Kubernetes. The quickstart allows you to install the SDK and run your first workflow locally in a few minutes.
What integrations does Flyte support?
Flyte has powerful integrations including Apache Spark (run Spark jobs on ephemeral clusters), BigQuery (query BigQuery tables), PyTorch Elastic v1 (PyTorch-native multi-node distributed training), Ray (connect to Ray cluster for distributed model training and hyperparameter tuning), Snowflake (query Snowflake services), and Weights & Biases v1 (ML/AI experiment and inference-time tracking).
How does Flyte handle workflow failures?
Flyte builds fault-tolerant, resilient workflows that retry automatically and pick up where they leave off, making failures inconsequential. It has self-healing workflows that can autonomously recover from failures and continue where they left off. The failure node feature enables designating a specific node to execute in the event of a failure within your workflow.
Who uses Flyte in production?
Flyte is trusted by thousands of AI builders and used by companies including LinkedIn, Spotify, Freenome, Pachama, Warner Bros. Discovery, Wayve (autonomous driving R&D), Cradle (protein design ML development), Kineo, and MethaneSAT (global methane reduction from space). Spotify cuts quarterly forecast time in half with Flyte.
What use cases is Flyte best for?
Flyte is ideal for data pipelines processing petabyte-scale data, analytics workflows for business and finance use cases, machine learning pipelines for logistics/image processing/cancer diagnostics, ETL or ELT workflows, bioinformatics for generating biological insights, agentic workflows with stateful execution, and any custom orchestration challenge beyond just data.
How do I get help with Flyte?
The best way to get help is through Slack at slack.flyte.org where you can ask anything related to Flyte and get a response within a few hours. The community has channels for office hours, newsletter, general discussion, feedback, and deployment help. You can also access comprehensive docs, tutorials, end-to-end examples, and guides for platform engineers to deploy and maintain Flyte.