Flyte vs H2O.ai

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.

H2O.ai provides software for generative AI, predictive AI, machine learning operations, and AI application development. H2O AI Cloud combines automated machine learning with no-code deep learning in an end-to-end environment for creating, deploying, monitoring, and sharing models and applications. AutoML handles feature engineering, validation, model selection, and tuning, while H2O MLOps validates models and monitors performance. Deployment can use H2O.ai’s managed cloud, a private cloud, or on-premises infrastructure, including Google Cloud Platform, Amazon Web Services, and Microsoft Azure. h2oGPTe supports multimodal, agentic, code-assistant, citation-RAG, guardrail, and model-routing capabilities, with responses grounded in private repositories, knowledge bases, and databases. H2O LLM Studio provides no-code language-model fine-tuning. H2O Wave is an open-source Python framework, and H2O-3 is Apache-licensed for Python and R. The platform supports SQL, natural-language queries, visualizations, team collaboration, and CSV or Parquet exports.