Open source alternative to Google Cloud Composer, Amazon SageMaker, Google Cloud Vertex AI and Azure Machine Learning
Last verified
Build reproducible Python data pipelines from notebooks and scripts. Apache-2.0 licensed, with cached DAG runs and self-hosted deployment options.
Use Ploomber to turn notebooks into modular pipelines without rewriting them as Airflow operators first.
Your team already has a mature data engineering platform and notebooks are not part of production work.
Use Ploomber when cached task execution and local development speed matter before deployment.
You need managed experiment tracking and hosted training as the primary product.
Use Ploomber's refactoring workflow to split monolithic notebooks into maintainable pipeline tasks.
The notebooks are throwaway analysis and will never be rerun.
Data science work often starts in notebooks, then gets rewritten for production orchestration. That rewrite creates duplicated logic, fragile handoffs, and slow iteration. Full workflow schedulers can be too much when the immediate problem is making analysis code reproducible, modular, cached, and deployable without forcing every notebook into a platform-specific operator model.
Ploomber lets teams use Jupyter notebooks and Python scripts as pipeline tasks instead of rewriting all analysis code for an orchestrator.
A simple YAML API supports quick starts, while the Python API gives teams more flexible workflow definitions.
Ploomber caches previous pipeline results and only recomputes tasks that changed since the last execution.
Single-machine shell scripts plus Kubernetes, Airflow, AWS Batch, and SLURM deployment paths give teams several ways to move a pipeline out of a notebook.
Install Ploomber in a Python virtual environment, then build a local pipeline:
```bash
python -m venv .venv
source .venv/bin/activate
pip install ploomber
ploomber build
```Ploomber is an open source Python framework for building reproducible data pipelines from notebooks and scripts.
Yes. Ploomber supports deployment to single-machine scripts, Kubernetes, Airflow, AWS Batch, and SLURM.
The ploomber/ploomber repository is archived, so teams should verify maintenance expectations before adopting it for new production pipelines.
Alternative to
Lightning AI
Distribute PyTorch training to multi-GPU and TPU with four lines
Alternative to
Google Cloud Vertex AI
Model-definition framework for state-of-the-art ML
Alternative to
Databricks
Open lab for training foundation models together
Alternative to
ChatGPT
Turn any computer into a private AI server
Alternative toOpenAI API
Full transparency LLM: open weights, training code, and data
Alternative to
Databricks (with MosaicML)
EleutherAI's framework for training LLMs at research scale
Featured
Alternative to
Superhuman
AI email assistant that drafts replies and clears your inbox
Alternative toClaude Managed Agents
Open-source agent deployment API. Self-host or use Omnara Cloud.
Alternative to
Grok Bot
AI teammates you own: your keys, your model, your machine.
The projects we add and every new guide. Free, unsubscribe any time.
Claude Code to 
OpenCode
Jira to 
Windshift
Notion to 
AppFlowy
Slack to 
Mattermost
Linear to 
GitLab
Wispr Flow to 
Handy
CapCut to 
Concat
Grok Bot to 
rakazo
Superhuman to 
Inbox Zero
Airtable to 
AITable
Muse to 
OpenClaw
GitHub Copilot to 
Cline
Datadog to 
Netdata
Microsoft Teams to 
Rocket.Chat
Pinecone to 
Qdrant
Zapier to 
n8n
Devin AI to 
OpenHands
Firebase to 
Supabase
1Password to 
KeePassXCConfluence to 
Docmost
Contentful to 
Strapi
Adobe Premiere Pro to 
Shotcut
ChatGPT to 
AnythingLLM
LangSmith to 
Langfuse
