Best fit
Who should shortlist this first
- Data Governance buyers
Databricks provides a unified platform combining data engineering, data science, machine learning, and generative AI for organizations building data-driven applications.
Pricing
Pay-as-you-go
Reviews
3,000+
Founded
2013
Team Size
5001-10000 employees
Current Deal
Free community edition
About Databricks ML
Databricks provides a unified data and AI platform that enables organizations to build, deploy, and monitor machine learning models. The platform includes AutoML, feature store, model serving, and MLflow for the complete ML lifecycle.
Pricing
Pay-as-you-go
Pay-as-you-go: Based on DBU consumption
Buyer Fit & Commercial Snapshot
Buyer teams
Common buyer roles
- AI-Powered
- Enterprise-Ready
- API Available
Commercials
Commercial snapshot
Pricing
Pay-as-you-go
Reviews
3,000+
Founded
2013
Team Size
5001-10000 employees
Procurement
Questions to answer before purchase
- Confirm security, access controls, and onboarding ownership directly with the vendor.
- Validate how Pay-as-you-go pricing scales as usage grows.
- Review website and support resources before procurement review.
- Compare the current deal terms against standard packaging and renewal structure.
Buyer-fit and commercial detail available
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Stack Fit, Alternatives & Trust
Ecosystem
Commonly evaluated with
PipedriveADPSASGoogle Classroom VCAI-PoweredEnterprise-ReadyAPI Available
Alternatives
Other products buyers may compare
- Informatica Governance
- Talend Data Quality
- Microsoft Purview
- Collibra Governance
- Matillion
- Alation Governance
Trust
Signals available today
- Data science teams praise the unified ML platform and AutoML capabilities. Users value the integration of data engineering and ML workflows.
- LinkedIn company profile available
- Profile refreshed Apr 13, 2026
- Public profile launched Apr 13, 2026
Executive scan
Summary and what a claimed profile unlocks
Databricks ML is a data governance product positioned for buyers that want stronger context around pricing, category fit, and real-world proof before committing to a shortlist.
How should buyers evaluate this profile?
Start with category fit, pricing posture, and buyer proof. Then confirm rollout support and procurement readiness directly with the vendor.
What makes the profile stronger after a vendor claims it?
Claimed profiles unlock richer buyer-fit notes, rollout guidance, procurement details, outcome proof, alternatives, and freshness updates.
Deeper stack and trust research available
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Case Studies
Enterprise deployment at scale
A mid-market company implemented Databricks ML across 3 departments, reducing operational overhead and consolidating their workflow into a single platform...
ROI within first quarter
After switching to Databricks ML, the team reported measurable improvements in efficiency and a positive return on investment within 90 days...
Case studies available
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