Enterprise Data Operations
Five tools doing one job. Pay for one.
Most enterprise data stacks are an accumulation of decisions made under pressure — each one justified at the time, none of them talking to each other. Databasin replaces the stack, keeps the capability, and cuts the bill by up to 80%.
Typical Enterprise Stack
What most organizations pay for the same capability Databasin delivers in one platform
Snowflake / Databricks$80–200K/yr
CData connectors / MuleSoft$25–50K/yr
Azure Data Factory / Fivetran$30–80K/yr
Tableau / Power BI Premium$20–60K/yr
OpenAI / AI API licensing$15–40K/yr
Estimated annual total$170–430K/yr
Databasin — all of the aboveUp to 80% less
Customers
WashU Medicine
BJC Health
Saint Louis University
KVC Health
McCormack Baron
Compana Pet Brands
Where the Money Goes
Your data budget has four leaks. Most organizations are only watching one.
Infrastructure licensing is the visible line item. Engineering overhead, opportunity cost, and stalled AI ROI are the ones that compound silently.
Leak 01
Tool sprawl with overlapping licenses
Connector layer, ETL orchestrator, warehouse, BI tool, AI API — each with its own vendor, renewal, and support relationship. Each partially redundant with the others.
Leak 02
Engineering hours spent on plumbing
Senior engineers maintaining pipelines instead of building capability. Every upstream schema change triggers an incident. The on-call rotation is a tax on your best people.
Leak 03
Decisions delayed by data availability
Leadership can't act on data they can't access. The cost of waiting isn't on any invoice — but it's real. Every week of delay on a strategic decision has a dollar value.
Leak 04
AI investments that never ship
AI projects stall at the data layer — missing lineage, quality issues, governance gaps. Months of compute and talent consumed before anyone realizes the foundation wasn't ready.
The Problems We Solve
Stack consolidation isn't a migration project. It's a sequence.
The reason organizations don't consolidate isn't that they don't want to. It's that every previous attempt required a rip-and-replace that created more risk than it solved. Databasin is additive — you layer it on, prove it out, then retire the tools it replaces.
01
Proprietary storage lock-in
Snowflake and Databricks native formats create exit costs. Once data accumulates, moving it requires a migration project that's expensive, risky, and slow. The stack calcifies.
02
Brittle ETL that breaks on upstream changes
Tightly coupled pipelines break when source systems change their schemas — and they always do. Epic Clarity upgrades, Workday tenant changes, Salesforce API updates — each one a potential incident.
03
AI queries on ungoverned data
Deploying an LLM on raw or poorly governed data produces hallucinations and inconsistent results. The governance problem doesn't disappear with a better model — it requires a better foundation.
04
No single source of truth
Every business unit owns its own extracts, definitions, and maintenance burden. The same data is processed five times and governed zero times. Leadership stops trusting any of it.
05
Five renewals where one should exist
Connector license. ETL orchestration. Warehouse compute. BI tool. AI API. Each renewal requires justification, each vendor relationship requires management, each contract creates a dependency.
06
Consolidation feels riskier than staying put
Migration anxiety is rational — data gravity is real and exit costs are real. The organization keeps paying the Complexity Tax because every consolidation attempt has ended badly.
Area
The Current Reality
The Databasin Fix
StorageProprietary format lock-in
Snowflake's and Databricks' native formats make leaving expensive. Data gravity compounds over time. Every passing month makes migration harder and more costly.
Delta Lake and Apache Iceberg are vendor-neutral open standards. Your data is readable by any compatible engine, now and in the future. No exit tax. Switch compute providers without migrating data.
PipelinesETL that breaks on schema changes
Tightly coupled pipelines fail when upstream systems update. Epic Clarity refreshes, Workday tenant upgrades, Salesforce API changes — each a potential incident. The on-call engineer is paying a tax on someone else's decision.
Immutable bronze with schema versioning — the raw ingestion layer of Databasin's medallion architecture (bronze for raw data, silver for transformed, gold for governed analytics) — means upstream changes are detected at ingestion and logged before they propagate. Pipelines adapt instead of breaking.
AI ReadinessAI on ungoverned data
LLMs querying raw or poorly governed data produce hallucinations and inconsistent results that vary by which table the model happens to query. The model is always blamed, but the data foundation is always the problem.
The Insights AI layer is architecturally constrained to query gold-layer data only — validated at silver, documented with metric definitions, governed by the platform. The model can't query a table that doesn't have a defined meaning.
Migration RiskConsolidation requires rip-and-replace
Every previous consolidation attempt created more risk than it solved because it required migrating storage, reconfiguring governance, and cutting over BI tools all at once. The organization keeps paying the Complexity Tax because the alternative looks worse.
BYO mode means Databasin layers on top of your existing environment. Connectors, Integrations, and Insights attach to your existing Databricks, Snowflake, or Fabric instance. You add capability first, retire tools at renewal — zero forced migration.
Who Are You?
Same savings. Three different conversations.
The Head of Data, the CIO, and the CFO all see the same cost problem from different angles.
For CDOs, Heads of Data & Data Platform Leaders
Stop managing infrastructure. Start delivering value.
You're accountable for cost, quality, and delivery — with a team stretched thin and a stack fighting you at every turn. Databasin consolidates the stack so your team's time goes back into analytics.
- →One platform for connectors, pipelines, storage, and AI — one vendor, one renewal, one support relationship
- →Schema versioning and self-healing pipelines eliminate the maintenance burden eating your team's capacity
- →BYO mode means you can layer on top of existing Databricks or Snowflake — no forced migration, no governance disruption
- →Open table formats mean no exit cost — you're never locked in again
What changes
BEFORE
Five vendors. Five renewals. Senior engineers on-call for pipeline incidents. AI projects stalled at the data layer. Team capacity consumed by maintenance instead of new capability.
AFTER DATABASIN
One platform. Pipelines that adapt instead of break. Engineering capacity redirected to gold-layer analytics and new use cases. AI projects reach production because the foundation is ready.
For CIOs, IT Directors & Infrastructure Leaders
Less surface area. Fewer incidents. One platform to secure.
Every additional vendor is a security surface, a compliance requirement, and a contract to manage. Consolidating to Databasin reduces all three — while adding capability, not removing it.
- →Private install in your own Azure tenant — data never leaves your governance perimeter, PHI-ready by architecture
- →One platform to audit, one security posture to maintain, one vendor to manage
- →Open table formats eliminate the proprietary lock-in that makes future migrations risky
- →Self-healing pipelines reduce on-call burden and incident frequency without adding headcount
What changes
BEFORE
Five vendors in the data stack. Five security reviews. Five compliance postures to document. On-call rotation for pipeline incidents that are fundamentally someone else's architecture decision.
AFTER DATABASIN
One vendor. One security review. Private Azure tenant install for regulated data. Fewer incidents, fewer renewals, less surface area to secure and manage.
For CFOs, Finance Directors & Budget Owners
Cut the Complexity Tax. Redeploy the savings.
Data infrastructure is one of the fastest-growing line items on most IT budgets — and most of the cost is redundancy, not capability. Databasin replaces five tools with one and returns the savings to the business.
- →Up to 80% reduction in total data infrastructure spend — quantifiable at renewal, not just in principle
- →One license, one invoice, one vendor relationship — predictable cost without consumption billing surprises
- →Engineering hours recaptured from maintenance and redirected to revenue-generating capability
- →AI ROI actually materializes — the data foundation that AI projects need is ready from day one
The numbers
Typical 5-tool stack$170–430K/yr
Engineering overhead (conservative)$120–200K/yr
Stalled AI project costVaries — often $200K+
Databasin ACV (starting)$24K/yr
Proven in Production
Built at WashU Medicine. Deployed across industries.
Co-created at Washington University School of Medicine — not to pitch investors, but to solve a real production problem. The same platform, available to every organization paying the Complexity Tax.
80%
Average cost reduction vs. comparable lake house and analytics stacks
200+
Pre-built connectors — no connector stack required
Day 1
BYO deployment — attach to your existing environment without a migration project
"
The Complexity Tax is real, measurable, and showing up on your P&L right now. Databasin was built to eliminate it — not by removing capability, but by removing the redundancy you're paying for three times over.
Jake Gower — Co-Founder & CEO, Databasin
Ready to Cut the Stack
Five tools.
One platform.
Starting now.
BYO mode available — layer Databasin onto your existing Databricks, Snowflake, or Fabric environment. 14-day free trial available.