
Flyte
8.0#2 in workload automation softwareFree planFree planWebAPILinuxSelf-hosted
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| Fact | Flyte | Prefect |
|---|---|---|
| Durable execution | Flyte 2 can recover from infrastructure failures including OOM kills, preempted nodes and disappearing GPUs. | Prefect describes durable execution features including state tracking, automatic retries, caching, and recovery from failures. |
| Integrations | The Flyte site lists integrations including Apache Spark, Ray, PyTorch Elastic, BigQuery, Snowflake, Weights & Biases, OpenAI Agents SDK, LangGraph, CrewAI, Pydantic AI, MLflow, Dask, and Databricks. | The integrations directory lists AWS, Azure, Bitbucket, Dask, Databricks, dbt, Docker, GCP, GitHub, Kubernetes, Slack, Snowflake, and other integrations. |
| Plan limits | The Team plan includes a 25,000 action-per-run fanout limit, 1,000 concurrent actions, and one cluster; Enterprise lists 50,000+ actions per run and 3+ clusters. | The Hobby plan includes 2 users, 1 workspace, 5 deployments, 500 minutes per month of Serverless credits, and 7 days of run retention. |
| Purpose | Flyte 2 is a durable AI runtime for workloads involving training, serving, and agents. | Prefect is a workflow orchestration platform for data, machine learning, and agents, with workflows written as plain Python. |
Flyte suits teams building AI workflows for training, serving, or agents, particularly those needing dynamic execution and recovery from infrastructure failures. It offers local devbox and cluster deployment options.
Prefect suits teams building Python workflows for data, machine learning, or agents, especially those needing scheduling, monitoring, and retry or recovery features. Its self-hosted and hybrid options may suit teams that want code and data to remain in their infrastructure.