COSTLENS
Find out exactly where your data platform costs are going.
A structured audit that traces every dollar from your BigQuery or Snowflake bill back to the queries, pipelines, and dashboards that caused it.
Book a 30-Minute Audit CallData platform costs don't spiral because of bad technology.
They spiral because nobody connects architecture, workflows, usage, and costs.
Your dbt project started with 50 models. Now it has 400. Each one triggers compute. Some run hourly when daily would suffice. Some materialize tables that duplicate other tables. Nobody has a full picture of the downstream impact.
Your dashboards started lean. Now there are hundreds. Many refresh every 15 minutes. Most are opened once a month—or never. But they all query your warehouse constantly.
Meanwhile, your warehouse bill becomes a black box. Finance sees a number. Engineering sees query logs. Nobody sees the connection between business decisions and infrastructure costs.
That's the problem CostLens solves.
Sound familiar?
- Your BigQuery or Snowflake bill increased 40%+ in the last quarter
- You can't explain which teams, dashboards, or pipelines drive most of the cost
- dbt models keep multiplying but nobody tracks their compute impact
- Dashboards refresh constantly, but nobody knows if anyone uses them
- The data platform team gets blamed for costs they don't control
- Finance asks questions you can't answer with current tooling
If you checked more than two, you're leaving significant money on the table.
FRAMEWORK
The Data Cost Stack
I analyze your data platform through four layers. Each layer reveals different cost drivers—and different optimization opportunities.
1. Usage
Who runs what, when, and how often. This layer identifies the queries, users, and schedules that consume the most resources. It answers: which workloads actually drive your bill?
2. Flow
How data moves through your platform. This layer traces the path from source to dashboard, mapping dependencies between dbt models, pipelines, and reports. It reveals redundant transformations and inefficient refresh patterns.
3. Design
How your data is structured and stored. This layer examines partitioning, clustering, materialization strategies, and warehouse configuration. It identifies architectural decisions that inflate costs—and alternatives that don't.
4. Ownership
Who is responsible for what. This layer maps costs to teams, projects, and business units. It answers: who should care about which costs? Without clear ownership, optimization recommendations go nowhere.
DELIVERABLES
What you get
Cost Attribution Report
Every major cost traced back to specific queries, pipelines, dashboards, and teams.
Cost Driver Analysis
The 20% of workloads that cause 80% of your bill—ranked and explained.
dbt Model Review
Which models are expensive, which are redundant, and which need materialization changes.
Dashboard Efficiency Review
Dashboards sorted by cost-per-view. Identify the ones nobody uses that cost the most.
Architecture Review
Partitioning, clustering, warehouse sizing, and scheduling opportunities.
Quick Wins List
5-10 changes you can implement this week to reduce costs immediately.
Long-Term Roadmap
Strategic recommendations for sustained cost efficiency as you scale.
BACKGROUND
Why work with me
I've spent 8 years building and optimizing production data platforms—for scale-ups and enterprise, processing tens of terabytes daily, with six-figure monthly cloud bills.
I've worked across BigQuery, Snowflake, and Databricks. Batch architectures, streaming, lakehouse. Multi-cloud environments where cost visibility is hard and cost attribution is harder.
Recent results:
- • Media company, €80k/month BigQuery spend: Identified €21,000 in monthly savings within 2 hours. Main driver: a dashboard refreshing every 15 minutes that nobody used for real-time decisions.
- • Pharma company, Snowflake: Reduced warehouse costs by 35% in 6 weeks through materialization changes and warehouse right-sizing.
FAQ
Common questions
How long does the audit take?
The core analysis takes 1-2 weeks depending on the complexity of your environment. You'll receive the full report shortly after.
What access do you need?
Read-only access to your warehouse metadata and query logs. I never touch production data. Specific requirements depend on your platform (BigQuery INFORMATION_SCHEMA access or Snowflake ACCOUNT_USAGE views).
Will this disrupt our operations?
No. The audit uses only metadata and historical query logs. I run read-only queries during off-peak hours. Your pipelines and dashboards are unaffected.
Do you help implement the recommendations?
The audit deliverable is designed to be actionable by your team. If you want hands-on implementation support, we can discuss a follow-up engagement.
How much does it cost?
Audits typically range from €5,000 to €15,000 depending on environment size and complexity. Most audits identify 10-50x their cost in first-year savings.
Let's find where the money is going.
A 30-minute call to understand your situation. No pitch, no pressure.
Book a 30-Minute Audit Call