Five tools doing one job. Pay for one.
Most enterprise data stacks are an accumulation of decisions made under pressure. Every one was justified at the time; none of them talk to each other. Databasin replaces the stack, keeps the capability, and turns five line items into one bill.
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 above
Pay per minute
Customers
WashU Medicine
Saint Louis University
KU Medical Center
KVC Health
Technology Partners
McCormack Baron
Mers Goodwill
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. Every one carries its
own vendor, renewal, and support relationship. Every one is partially redundant with
the others.
Leak 02
Engineering hours spent on plumbing
Senior engineers maintain pipelines instead of building capability, and 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. No invoice shows the cost of
waiting. It's real anyway: 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. They
always do. Every Epic Clarity upgrade, Workday tenant change, and Salesforce API
update is a potential incident.
03
AI queries on ungoverned data
Deploying an LLM on raw or poorly governed data produces hallucinations and
inconsistent results. A better model doesn't make the governance problem disappear.
It takes 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 so are exit costs. The
organization keeps paying the Complexity Tax because every consolidation attempt has
ended badly.
Area
The Current Reality
The Databasin Fix
Storage
Proprietary format lock-in
Snowflake's and Databricks' native formats make leaving expensive, and 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.
Pipelines
ETL 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
detects upstream changes at ingestion and logs them before they propagate. Bronze is
the raw ingestion layer of Databasin's medallion architecture: bronze for raw data,
silver for transformed, gold for governed analytics. Pipelines adapt instead of
breaking.
AI Readiness
AI 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.
Gold data is validated at silver, documented with metric definitions, and governed by
the platform. The model can't query a table that doesn't have a defined meaning.
Migration Risk
Consolidation 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, then 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. Your team is stretched thin and
the stack fights you at every turn. Databasin consolidates that 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 layers on top of existing Databricks or Snowflake, so there's no forced migration and no governance disruption
- → Open table formats mean no exit cost. You're never locked in again
What changes
BEFORE
Five vendors and five renewals. Senior engineers on-call for pipeline incidents.
AI projects stalled at the data layer, and team capacity consumed by
maintenance instead of new capability.
AFTER DATABASIN
One platform, and pipelines that adapt instead of break. Engineering capacity
goes back 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 rather than removing it.
- → A private install in your own Azure tenant means 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. A private Azure tenant install for regulated
data. Fewer incidents, fewer renewals, and 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.
Most of that cost is redundancy, not capability. Databasin replaces five tools with
one and returns the savings to the business.
- → Five infrastructure line items collapse into one platform spend, quantifiable at renewal rather than 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, because 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 cost
Varies — often $200K+
Databasin (billed per minute)
$0 when stopped
Proven in Production
Built at WashU Medicine. Deployed across industries.
Co-created at Washington University School of Medicine to solve a real production problem, not to pitch investors. The same platform is available to every organization paying the Complexity Tax.
$0
What a stopped cluster costs, billed per minute with no seats and no annual commit
75
+
Pre-built connectors. No connector stack required
Day
1
BYO deployment attaches 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.
$50 credit · No card · First answer in 5 minutes · BYO mode layers Databasin onto your existing Databricks, Snowflake, or Fabric environment