The number one complaint we hear from enterprise leaders is not about technology capability, it is about Unit Economics.
CoeurData Editorial Team • 7 min read
The "Cloud Hangover" is real.
The number one complaint we hear from enterprise leaders is not about technology capability, it is about Unit Economics. Organizations that migrated to Snowflake, Databricks, or any of the hyper-scalers for agility are now facing monthly compute bills that defy gravity.
When the CFO demands answers, leadership typically turns to four parties for help. But if you look closely, you will realize that none of them are incentivized—or equipped—to actually solve the problem.
Asking Snowflake or Databricks to help you reduce your bill is like asking your electric company to help you use less power.
The Incentive: Their revenue model is Consumption. Their stock price depends on your "Net Revenue Retention" (NRR) growing every year.
The Reality: They want you to run more workloads, process more data, and spin up more clusters. They are the beneficiaries of your inefficiency.
You hire a large System Integrator (SI) to manage your data estate.
The Incentive: Their revenue model is Billable Hours.
The Reality: Efficient code is is not an SLA they have to meet. Complex, "spaghetti" pipelines require large teams to maintain and fix. They are incentivized to sell you "Managed Services" to fix breakages, not to engineer the breakages out of existence.
You hire a specialized consulting firm to perform a "Cost Audit" or a "Performance Tuning Sprint."
The approach: They send in senior architects to analyze your logs, refactor your worst queries, and hand you a shiny report showing 20% savings. Then, they leave.
The Reality: This is a band-aid, not a cure. The day they walk out the door, your team writes new code. A junior developer commits a bad join; a data scientist runs a heavy loop. Within 6 months, entropy takes over, and your costs creep right back up to where they started. You cannot "rent" optimization; you need to operationalize it.
This is where most leaders get confused. They buy expensive "Observability" or "Quality" tools, thinking they have covered their bases. But these tools look at the wrong layer of the stack.
Tools like Monte Carlo or Datadog scan your logs and metadata.
The Gap: They are fundamentally Reactive. They alert you after the job has failed or the bill has spiked. They see the car crash; they don't apply the brakes.
Tools like dbt tests or Great Expectations run SQL queries against your tables to check for NULLs or duplicates.
The Gap: They check the Data, not the Code. You can have perfectly accurate data generated by horribly inefficient code. A pipeline can pass every "Quality Test" while still burning 10x the necessary compute budget.
Tools like Collibra or Alation map where data came from.
The Gap: They look at the past. They can tell you what ran, but they can't tell you how efficiently it ran.
If the vendor wants consumption, the SI wants hours, the consultant leaves after the fix, and the tools only look backwards—who is ensuring your costs don't spiral out of control tomorrow?
This is exactly why we built Undraleu.
Undraleu is a platform that performs Continuous Code Analysis. We don't just fix your costs once; we govern them forever.
We don't look at Logs (Reactive): We look at the Source Code (Proactive).
We don't "Audit" once (Consulting): We sit in your CI/CD pipeline. Every time a developer commits code—whether it's today or two years from now—Undraleu scans it.
We Enforce the Law: If a developer tries to push a "Cartesian Join" or a "Driver Node Explosion" pattern, Undraleu blocks the deployment.
Optimization isn't a project; it's a discipline.
The only way to prevent "Cost Creep" is to stop bad code from ever entering your environment. At CoeurData, we act as the neutral governor that ensures your platform stays efficient, long after the consultants have gone home.
We are so confident that your codebase contains hidden financial inefficiencies that we are offering a Free Assessment.
We will plug Undraleu into your ETL repository (Databricks, DBT, IDMC, PySpark, ADF or AWS Glue) and run a scan of your codebase. In two weeks, we will hand you a Technical Debt Scorecard showing exactly where your inefficient code is burning cash—and how much you can save by fixing it.
Stop guessing. Start engineering your costs.