Compare Grafana and Databricks on pricing, features, platforms, and ratings — both Business Intelligence tools, Freemium (Open Source plus Usage-based Cloud)…
At a Glance
Grafana
Databricks
Primary category
Business Intelligence
Business Intelligence
Rating
Not documented
Not documented
Pricing model
Freemium (Open Source plus Usage-based Cloud)
Usage-based (pay-as-you-go per Databricks Unit)
Starting price
Free (self-hosted OSS or Grafana Cloud free tier)
Free Community Edition available; paid usage billed per Databricks Unit (DBU) plus underlying cloud compute costs
Free plan
Yes
Yes
Free trial
Not documented
Yes
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.
Grafana
Open Source (Self-Hosted) — Free N/A
Cloud Free — Free Monthly
Cloud Pro — From $19/month plus usage Monthly
Cloud Advanced — From $55/month Monthly
Enterprise — Custom (from around $25,000/year) Annual
Databricks
Premium — Pay-as-you-go, from ~$0.07/DBU Usage-based
Enterprise — Custom (negotiated with sales) Usage-based
Pros & Cons
Grafana
Pros
Free and open source core with no vendor lock-in to a single data store
Connects to virtually any metrics, logs or trace data source in one interface
Highly customizable dashboards backed by a large community plugin ecosystem
Usage-based Cloud pricing can be more cost-effective at scale than closed observability suites
Cons
Usage-based Cloud pricing can become unpredictable for high-cardinality metrics
Self-hosting the full LGTM stack requires meaningful operational expertise
Some advanced features like SSO and premium connectors require Enterprise licensing
PromQL, LogQL and TraceQL have a learning curve for newcomers
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
Use Cases
Grafana
Infrastructure and Kubernetes Monitoring — Visualize cluster health, resource usage and application metrics collected by Prometheus and other exporters.
Business and Executive Reporting Dashboards — Combine data from SQL databases, APIs and cloud services into shareable business dashboards.
IoT and Sensor Data Visualization — Monitor time-series data streaming from industrial sensors, devices and edge deployments.
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.