Compare Databricks and Evidence on pricing, features, platforms, and ratings — both Business Intelligence tools, Usage-based (pay-as-you-go per Databricks…
At a Glance
Databricks
Evidence
Primary category
Business Intelligence
Business Intelligence
Rating
Not documented
Not documented
Pricing model
Usage-based (pay-as-you-go per Databricks Unit)
Open Source / Freemium
Starting price
Free Community Edition available; paid usage billed per Databricks Unit (DBU) plus underlying cloud compute costs
Free (self-hosted); Evidence Cloud from $15/user/month
Free plan
Yes
Yes
Free trial
Yes
Not documented
Platforms
Web
Web
Team collaboration
Not documented
Not documented
AI features
Yes
Yes
Public API
Yes
Yes
Pricing Compared
Starting price reflects the lowest paid tier, not the full cost for every team size or usage level.
Databricks
Premium — Pay-as-you-go, from ~$0.07/DBU Usage-based
Enterprise — Custom (negotiated with sales) Usage-based
Evidence
Open Source (Self-Hosted) — Free N/A
Team — $15/user/month monthly
Pro — $25/user/month monthly
Enterprise — Custom annual (quote-based)
Pros & Cons
Databricks
Pros
Unified platform reduces the need for separate data warehouse, data lake, and ML tooling
Built on widely adopted open-source projects (Spark, Delta Lake, MLflow), lowering vendor lock-in risk
Scales efficiently for very large data engineering and machine learning workloads
Strong native support for generative AI and custom model development via Mosaic AI
Cons
Consumption-based DBU pricing can be complex to forecast and may escalate at scale
Requires meaningful technical expertise in Spark, SQL, and cloud infrastructure
No flat, predictable starting price for smaller teams or individual users
Cost governance and cluster configuration require dedicated platform engineering resources
Evidence
Pros
Reports are version-controlled as code, enabling Git-based review, diffing, and deployment workflows familiar to engineering teams
Free and open source to self-host with no artificial feature limits on the core framework
Templated pages eliminate manual duplication when building many similar reports across regions, products, or customers
Designed for embedding analytics into customer-facing products, not just internal dashboards
Cons
Requires comfort writing SQL and Markdown; not accessible to non-technical business users who want pure drag-and-drop reporting
Smaller ecosystem and community than established BI incumbents like Tableau, Looker, or Power BI
Evidence Cloud pricing is per-user, which can add up for larger analytics teams
AI features are metered via credits, adding a variable cost dimension beyond the base subscription
Use Cases
Databricks
Enterprise Data Engineering and ETL — Building and orchestrating large-scale batch and streaming data pipelines using Delta Lake and Databricks Workflows.
Machine Learning and Generative AI Development — Training, tracking, and deploying ML models and building custom LLM applications with Mosaic AI and MLflow.
Enterprise Analytics at Scale — Running warehouse-style SQL analytics and BI reporting directly on governed lakehouse data via Databricks SQL.
Evidence
Internal analytics for lean data teams — A small analytics engineering team builds and maintains a full internal reporting suite using the same Git workflow they already use for data pipeline code.
Customer-facing embedded analytics — A SaaS company embeds Evidence-built reports directly into its product to give customers self-service analytics without building a custom dashboard from scratch.
Automated multi-entity reporting — An organization uses templated pages to automatically generate a separate performance report for every region, store, or account from a single report definition.