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Scale Data Pipelines in Minutes, Not Weeks
Pipekit is the control plane for Argo Workflows that enables massive data pipelines in minutes, saving engineering time and cloud spend.
The Argo project is used by the following companies and many others
How Pipekit helps you scale
Pipekit gives you production-ready workflows in minutes by configuring Argo Workflows on your infrastructure.
Start triggering workflows, collecting logs, managing secrets, and much more on Day 1.
Setting up new infrastructure takes months of engineering time. Experiment with new tools, complete a POC, architect a solution from scratch.
Dig through documentation
Improvise a POC for your team
Configure integrations for logging, secrets storage, SSO, etc.
Code for 3+ months before going live
Set up or extend your infrastructure in a day. Get expert advice on the best solution for the job. Focus on scaling, not provisioning.
Build an impressive POC on Day 1
Programatically trigger workflows across multiple clusters
Work with experts to architect a solution that will scale for your needs
Go live on production in weeks, not months
Make multi-cluster workloads simple
Orchestrate workloads across multiple clusters simultaneously. Maintain data pipelines across dev, staging and prod. Isolate customer data while still running workflows from one control plane.
Welcome to container-native data pipelines.
Argo Workflows is the open-source workflow engine for running data pipelines on Kubernetes. Learn why ML-driven companies are choosing Argo to scale and reduce cloud spend.
Learn how Pipekit helps companies just like yours
- Use Argo on Pipekit Cloud without running Kubernetes
- Set up in minutes
- Built-in logging
- Control costs with resource limits
- Bring your own infrastructure
- Run multi-cluster workloads
- Integrate new workflows into your legacy systems
- Consult Argo experts to architect the best solution
Pipekit for the ML lifecycle
Explore Pipekit by use case
Automate ETL jobs on terabytes of data
- Problem: Large ETL jobs are difficult to de-bug and costly to re-run.
- Solution: Pipekit provides the control plane for all ETL jobs with easy access to logs for debugging.
Backfill features automatically
Backfill features for the whole data team
- Problem: Data scientists must wait on engineering to backfill features in order to push their models to production.
- Solution: Workflows to backfill features are defined in Pipekit so data scientists can trigger them without engineering.
Push model updates
Trigger ML model updates from within your app
- Problem: User data constantly changes making CRON jobs useless for maintaining models on production.
- Solution: Programmatically trigger workflows with Pipekit's API that update ML models.
Here’s how Pipekit keeps your data safe
AWS, GCP, Azure
Bring your own clusters
Private container registry
Argo & K8s config
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