Databricks is a leading data and AI platform company operating across AWS, Azure, and Google Cloud. Its core offering, the Databricks Data Intelligence Platform, includes capabilities such as Delta Lake, Unity Catalog, Databricks SQL, Mosaic AI, and Apache Spark-based data engineering and machine learning workflows. Databricks increasingly sells into enterprises that are rationalizing fragmented analytics stacks and deciding how much infrastructure they want to manage themselves.
You are an Account Executive at Databricks preparing for a regional planning review. Several CIOs in your territory are asking a basic but commercially important question: how do IaaS, PaaS, and SaaS differ, and where should Databricks position itself relative to each model? The challenge is not just to define the terms, but to turn that understanding into a practical account strategy. Databricks needs a clear narrative for when to position against raw cloud infrastructure, when to coexist with cloud-native platform services, and when to displace point SaaS analytics tools.
Your VP wants a recommendation for how Databricks should prioritize its sales motion over the next 12 months for mid-market and enterprise accounts that are modernizing their data estates.
| Item | Value |
|---|---|
| North America target territory accounts | 400 |
| Accounts currently using only cloud IaaS (AWS/Azure/GCP primitives, no unified lakehouse) | 160 |
| Accounts using a mix of cloud PaaS data services and Databricks in limited teams | 140 |
| Accounts relying primarily on legacy or modern SaaS BI/data tools | 100 |
| Average annual contract value if Databricks expands from team use to enterprise standard | $350K |
| Average annual contract value for net-new departmental land | $90K |
| Typical sales cycle: IaaS-led modernization | 9 months |
| Typical sales cycle: PaaS consolidation/expansion | 6 months |
| Typical sales cycle: SaaS displacement for advanced AI/data workloads | 4 months |
| Available incremental field investment for the region | $1.5M |
Additional observations: