Flyte vs ZenML

Flyte 2 is a durable runtime for AI workloads involving training, serving and agents. Users write workflows as plain Python async functions and use a TaskEnvironment to declare images, resources, secrets and retry policies. Flyte resumes from failed tasks and can recover from out-of-memory kills, preempted nodes and disappearing GPUs. Branches, loops and runtime decisions support dynamic workflows and agents. Native serving integrations include Streamlit, FastAPI, vLLM, SGLang and Ollama, with replicas able to scale to zero when idle. Listed integrations also cover Apache Spark, Ray, PyTorch Elastic, BigQuery, Snowflake, Weights & Biases, OpenAI Agents SDK, LangGraph, CrewAI, Pydantic AI, MLflow, Dask and Databricks. It runs locally with a devbox or on a customer cluster. Union.ai says customer-cloud data planes keep workflow inputs, outputs, logs, code and reports outside its control plane. Flyte is available as free Apache 2.0 open-source software.

ZenML orchestrates AI and machine-learning pipelines and agents written in Python. Pipelines can run on Kubernetes, Vertex AI, SageMaker, AzureML, or a laptop without rewriting code. ZenML versions step outputs such as models and evaluation datasets, supports re-execution from any step, and reuses cached results when code and inputs have not changed. Deployment choices include local, SaaS, and self-hosted setups, with Docker and Kubernetes Helm methods. ZenML says it stores metadata while data, artifacts, and compute remain in the customer's infrastructure or VPC. Its integrations directory lists 66 tools, including LangGraph, OpenAI Agents SDK, PyTorch, MLflow, AWS, and Google Cloud Vertex AI Pipelines. The self-hosted Open Source plan is free and includes unlimited executions and projects. Scale costs $999 a month and includes 2,000 executions, three projects, and five snapshots. Enterprise has custom pricing and adds SSO, custom-role RBAC, audit logs, and air-gapped deployment. ZenML says it is SOC 2 Type II and ISO 27001 certified.