Databricks
June
| Feature | Databricks | June |
|---|---|---|
| Pricing | Free / from $0.07/mo | Free / from $149/mo |
| Free Plan | ✓ Yes | ✓ Yes |
| Rating | 4.5 / 5 | 4.4 / 5 |
| Best For | data-engineering-teams, ml-teams, enterprises, large-scale-analytics | b2b-saas, product-managers, growth-teams, startup-founders |
| Founded | 2013 | 2021 |
| Data Lakehouse | ✓ | ✗ |
| Sql Analytics | ✓ | ✗ |
| Machine Learning | ✓ | ✗ |
| Real Time Streaming | ✓ | ✗ |
| Data Governance | ✓ | ✗ |
| Notebooks | ✓ | ✗ |
| Delta Lake | ✓ | ✗ |
| Auto Reports | ✗ | ✓ |
| Activation Tracking | ✗ | ✓ |
| Retention Analysis | ✗ | ✓ |
| Feature Adoption | ✗ | ✓ |
| Company Profiles | ✗ | ✓ |
| Slack Alerts | ✗ | ✓ |
✓ Databricks Pros
- Unified platform for data engineering, science, and analytics
- Delta Lake provides ACID transactions on data lakes
- Excellent ML/AI capabilities with MLflow integration
- Community Edition is free for learning
✗ Databricks Cons
- Complex pricing with DBU credits
- Requires data engineering expertise to configure
- Vendor lock-in once deeply integrated
✓ June Pros
- Auto-generated reports
- Built for B2B SaaS
- Company-level analytics
- Segment integration
✗ June Cons
- B2B SaaS focused only
- Expensive for early-stage
- Limited custom dashboards
The Verdict
Databricks is built for data engineering teams and ml teams, with a focus on data-lakehouse and sql-analytics. June targets b2b saas and product managers and leads with auto-reports and activation-tracking.
On pricing, Databricks is the clear winner for budget-conscious users — starting at $0.07/mo compared to $149/mo for June. That $148.93/mo difference adds up quickly for growing teams.
Both offer free plans, so you can test each with your real workflow before committing to a subscription.
Feature-wise, Databricks offers broader built-in capabilities (7 features vs 6), while June takes a more focused approach — which can mean a simpler, faster onboarding experience.
This is a genuinely close comparison. If you can, sign up for both free trials (where available) and run a one-week test with your actual team tasks before deciding.