Azkaban
6.1#18 in workload automation softwareWebAPILinuxmacOS
VisitAzkaban vs Flyte
What both publish
| Fact | Azkaban | Flyte |
|---|---|---|
| Deployment | The documentation provides instructions for setting up Solo Server and Multi Executor Server deployments. | Flyte 2 can run locally with a devbox or on a user's cluster; the site provides `pip install flyte` and `flyte start devbox` setup commands. |
| Extensibility | Plugins can add viewer pages, trigger methods, user authentication methods, alerting methods, and job-type executors. | Flyte integrations are implemented through a plugin system and the project invites users to build and submit their own integrations. |
| Integrations | Documented job-type plugin examples include Hadoop ecosystem components, Pig, Hive, and VoldemortBuildAndPush. | 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. |
| License | The project’s GitHub page identifies its license as Apache-2.0. | The Flyte repositories and SDKs are published under the Apache License 2.0. |
| Purpose | Azkaban is a distributed workflow manager created at LinkedIn to manage Hadoop job dependencies and run jobs in sequence. | Flyte 2 is a durable AI runtime for workloads involving training, serving, and agents. |
Azkaban plans
No prices published.
Flyte plans
- Flyte OSSFree
- Team$950 a month
- Union.ai Team (enterprise Flyte platform)$950 a month
- EnterprisePriced on request
Pick Azkaban if…
Azkaban suits teams managing scheduled batch jobs and dependency-based workflows in self-hosted environments. It is relevant when Hadoop integrations, APIs or distributed executors are needed.
Pick Flyte if…
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.