The same lakehouse. Without the platform team.
Databricks invented the lakehouse and remains its high end — if you have the team to run it. AZ-Five is built on the same open Delta Lake format, but delivered as a finished product: BI, data quality, pipelines, product analytics and monitoring already assembled.
Where Databricks is stronger.
A comparison you can trust has to start with what the other side does better.
- Scale and the full ML platform
- Spark-scale compute, notebooks, experiment tracking, model serving and training infrastructure — a complete ML platform AZ-Five does not attempt.
- Enterprise governance at scale
- Unity Catalog, fine-grained enterprise controls and multi-cloud deployment, proven in very large organizations.
- Ecosystem
- A vast partner, marketplace and consulting ecosystem around the platform.
Side by side.
| Capability | Databricks | AZ-Five |
|---|---|---|
| Storage format | Delta Lake — they created it | The same open Delta Lake, managed for you |
| Query engine | Spark and Photon clusters you size and manage | DataFusion in Rust, reading tables in place — nothing to size |
| BI & dashboards | Databricks SQL dashboards, or bring your own BI tool | Governed BI built in — measures, embeds, alerts, quality states |
| Data quality | Expectations inside pipelines | First-class rules, SLAs, profiling and health on every dashboard |
| Product analytics | Not its domain | Events, funnels, replay, flags, experiments, surveys built in |
| ML | Full platform — notebooks to serving | Deliberately lighter: ONNX scoring, drift and performance monitoring |
| Operations | A platform team sizes clusters and owns the setup | Hosted and operated — bring a CSV, not a team |
| Pricing model | Compute-based (DBUs), quoted at enterprise scale | Transparent usage tiers from free, unlimited seats |
COMPARISON CHECKED 2026-09
The fares, side by side.
Databricks's column quotes databricks.com/product/pricing as it read on the date below. AZ-Five's column is the fare table. Prices are theirs to change; if we're out of date, tell us and we'll fix it.
| Capability | Databricks | AZ-Five |
|---|---|---|
| Billing unit | DBUs — normalized compute usage; rates vary by cloud and region | Events, replays, flag requests, storage and compute minutes per tier |
| Free tier | Free trial with credits; your cloud provider still bills for resources | 1M events, 5,000 replays, 1M flag requests, 5 GB storage, 100 compute minutes a month — every feature, unlimited seats, no card |
| Model | Pay-as-you-go with no up-front cost; Committed Use Contracts for discounts at volume | Plus $29/mo and Professional $199/mo with larger allowances and longer history; Enterprise custom, annual; pay-as-you-go past a tier with spend caps |
| Second bill | Your own cloud account is charged separately for compute and storage | None — hosted and operated, one bill |
| Seats | Not the billing unit; cost follows compute | Unlimited seats; cost follows data volume |
| Operating cost | Cluster sizing, tuning and platform operation are yours | No clusters to size; the engine is managed |
FARES AS PUBLISHED 2026-09-08 · CHECK THEIRS BEFORE DECIDING
What the network adds.
- Open formats either way
- Both sides store standard Parquet with a Delta transaction log. Leaving AZ-Five means copying files — the exit stays open in both directions. See the line
- Finished, not assembled
- Quality, lineage, BI and product analytics are already wired together — the integration work a Databricks stack leaves to you. See the line
Straight answers.
Something we got wrong about Databricks? Tell us at hello@az-five.com and we'll fix it.
Is AZ-Five a Databricks alternative for small teams?
For a lean team that wants lakehouse architecture without operating a platform, yes. You get the same open Delta Lake format with BI, quality, pipelines and product analytics already assembled. At Spark scale, or if you need notebooks-to-serving ML infrastructure, Databricks is the right tool.
Is it really the same Delta Lake?
Yes — standard Parquet files with a Delta Lake transaction log, the open format Databricks created. Your tables are readable by any Delta-compatible engine, and export means copying files.
When should I choose Databricks instead?
When you have a data platform team, Spark-scale workloads, or ML needs that span notebooks, distributed training and model serving. AZ-Five deliberately covers the monitoring end of ML, not the training platform.
Do I need Spark skills to use AZ-Five?
No. Ingestion is uploads, connectors and SDKs; transformation is SQL and visual pipelines; the engine is managed. There are no clusters to size and no jobs to tune.
How does pricing compare?
Databricks bills compute in DBUs — pay-as-you-go with committed-use discounts, rates varying by cloud and region, on top of your own cloud provider’s bill for the underlying resources. AZ-Five publishes fixed tiers from free with allowances for events, storage and compute minutes, and there is no second cloud bill.
Try the whole network free.
Unlimited seats, every feature, no card. Bring one dataset and see whether one system beats the stitched stack.