Directory AI & ML PlatformsML Platforms & MLOpsDagster

Dagster

Open Source

About Dagster

Dagster is an operational layer that structures how data is built, observed, and delivered. It is described as a data-aware orchestrator that models data assets and understands dependencies. It is positioned as a unified control plane for data and AI pipelines. It works with existing tools such as Python scripts, Snowflake, dbt, Spark, Databricks, Azure, and AWS. Dagster Pipes provides observability and metadata tracking for jobs running in external systems. Dagster is now part of Prefect.

Products

Dagster+: A platform supporting workloads from ELT and dbt to AI and LLMs with tiered pricing plans.
Dagster+ AI: Runs on Dagster's operational context (assets, runs, lineage, freshness, failures, automation history) to diagnose issues and explain behavior.
Dagster+ Terraform provider: Lets platform teams manage deployments, access controls, alerting, and more as code.

Used for

Building and running asset-based pipelines across tools in a stack.
Observing lineage, dependencies, and data health across a platform.
Shipping pipeline changes validated in a production-like environment before touching production.
Orchestrating existing dbt models and Snowflake assets without custom glue code.
Answering operational questions about pipeline, ad spend, and sales conversations in Slack.

Who it is for

Used by engineers, stakeholders, and agents working from the same operational foundation.
Used by platform teams defining standards for downstream teams.
Targeted at individual builders and production-scale data platforms.

From Dagster's own website, read 2026-10-23. How we build listings.

How to evaluate ML Platforms & MLOps

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Dagster. The category covers two kinds of product, so both frameworks are here. Most buyers need one of them.

AI/ML Platforms

25%

MLOps, Serving & Lifecycle

Reproducible training pipelines and CI/CD for models, a first-class model registry with versioning and stage promotion, real-time and batch/streaming serving, A/B and canary rollout, automated retraining, and rollback — the path from registered model to monitored endpoint and back

20%

Model Development & Experimentation

Notebook and IDE experience, experiment tracking and run comparison, feature engineering and a feature store, AutoML for breadth, distributed training, and support for the frameworks your teams use (PyTorch, TensorFlow, scikit-learn, XGBoost, Spark ML)

20%

AI Governance & Responsible AI

End-to-end lineage from data to deployed model, approval workflows and access control, bias and fairness testing, explainability, model and prompt audit trails, and the evidence trail needed for internal risk review and emerging AI regulation such as the EU AI Act

15%

GenAI & LLM Operations

Managed serving and fine-tuning of open and proprietary LLMs, a vector/retrieval layer for RAG, prompt management, evaluation harnesses for non-deterministic output, agent orchestration hooks, and token and inference cost controls — weighted to how central GenAI is to you

10%

Data, Compute & Cost Control

Proximity to where your data already lives and the egress it avoids, GPU availability and scheduling, spot/preemptible support, autoscaling, idle-resource reclamation, and chargeback/showback visibility so AI spend is attributable and governable rather than a surprise

10%

Platform Openness & Ecosystem

Portability and exit cost (open formats, MLflow compatibility, container-based serving), multi-cloud and on-prem reach, breadth of integrations, collaboration across data science and engineering, and standards conformance so the platform extends rather than confines your stack

MLOps Platforms

25%

Production Monitoring & Reliability

Data and concept drift detection, prediction/quality monitoring, ground-truth join for delayed labels, alerting and automated retraining triggers, plus LLM-specific signals (hallucination, toxicity, response-quality eval) on live traffic

20%

Model Registry & Governance

Versioned registry with stage transitions and approval gates, end-to-end lineage (data → run → model → endpoint), reproducibility, model cards, audit logging, and alignment to your AI risk/inventory policy

20%

Deployment & Serving

One-step deploy to real-time and batch endpoints, autoscaling and GPU-aware serving, canary / shadow / A-B rollout, rollback, multi-model and multi-framework support, and latency/throughput under your traffic

15%

CI/CD & Pipeline Automation

Git-native pipelines, reproducible training/eval/deploy stages, integration with your existing CI (Actions, GitLab, Azure DevOps), environment/dependency management, and IaC-friendly APIs

10%

Experiment Tracking & Reproducibility

Run logging at scale, metric/artifact comparison, hyperparameter sweeps, dataset and code versioning, collaboration, and a durable system-of-record across teams and frameworks

10%

LLMOps Coverage

Prompt and version management, eval datasets and LLM-as-judge scoring, RAG/agent tracing, online evaluation in production, and a path to manage classical models and LLMs through one stack

Other ML Platforms & MLOps Vendors

Azure Machine Learning

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