Take your AI pipelines from vibes to production

Most AI and ML pipelines are glued together with scripts and notebooks. They’re fragile, hard to test, impossible to track, and isolated from the rest of your data.

Build reliable AI and ML pipelines with a data orchestrator that scales with your hyper-growth.

Ship at the speed of AI

Stop spending time troubleshooting your ad hoc pipelines and spend more time shipping features.

Leave your competitors in the dust

Accelerate development with local testing, branch deployments, and software engineering best practices your teams expect, so you can ship AI products to market faster.

All your data under one roof

Context is king, so break down your silos and get a single unified control plane for all your data. State of the art observability for your SOTA foundation models.

Reliability by design

Built-in monitoring, quality checks, and lineage tracking so you can prevent outages before they hit your customers. Reliability so good, they’ll never know you vibe-coded your way there.

Built for today and tomorrow

Dagster’s composable architecture  supports your existing data platform while enabling you to build for the future. Adapt to a rapidly changing data landscape without having to rebuild your platform from scratch.

Everything you need to ship AI and ML pipelines with confidence

End-to-end AI and ML pipelines

Manage your AI and ML pipelines from data ingestion to production in a single unified platform. Track assets with complete lineage across the entire data lifecycle.

Run and scale AI/ML workflows with confidence

Dagster handles orchestration so you don’t have to stitch together jobs manually. Easily scale your pipelines, track runs, and monitor models—all with built-in reliability, retry logic, and visibility.

World class observability

Track your compute and model usage across teams with powerful insights reporting. Monitor workloads with built-in alerting to avoid unexpected surprises.

Unified ML pipelines

EvolutionIQ, an AI-driven insurance insights company, transformed its fragmented system into a unified platform with Dagster. This transition enhanced the development and deployment of their machine learning models, leading to significant gains in efficiency and scalability.

Faster development and testing
Testing and debugging of ML model updates were radically improved, from hours to minutes.
From months to less than a week
EvolutionIQ improved their client onboarding time from months to less than a week.

Why teams love Dagster

Seamless integration & scalability

Connect to your existing data stack and scale as your business grows—without bottlenecks.

Built for developers, loved by data leaders

A developer-friendly platform that provides the transparency, control, and insights data leaders need.

Future-proof your data strategy

Scale with your growing data and AI workloads ahead with a flexible, open-source foundation that evolves with your needs.

Ship data and AI products faster

Automate, monitor, and optimize your data pipelines with ease. Get started today with a free trial or book a demo to see Dagster in action.

Try Dagster+

Latest writings

The latest news, technologies, and resources from our team.

Evaluating Model Behavior Through Chess
Evaluating Model Behavior Through Chess

January 7, 2026

Evaluating Model Behavior Through Chess

Benchmarks measure outcomes, not behavior. By letting AI models play chess in repeatable tournaments, we can observe how they handle risk, repetition, and long-term objectives, revealing patterns that static evals hide.

How to Enforce Data Quality at Every Stage: A Practical Guide to Catching Issues Before They Cost You
How to Enforce Data Quality at Every Stage: A Practical Guide to Catching Issues Before They Cost You

January 6, 2026

How to Enforce Data Quality at Every Stage: A Practical Guide to Catching Issues Before They Cost You

This post gives you a framework for enforcing data quality at every stage so you catch issues early, maintain trust, and build platforms that actually work in production.

When Sync Isn’t Enough
When Sync Isn’t Enough

January 5, 2026

When Sync Isn’t Enough

This post introduces a custom async executor for Dagster that enables high-concurrency fan-out, async-native libraries, and incremental adoption, without changing how runs are launched or monitored.