Your data platform is underperforming. The cost is a symptom.

We optimize query performance, right-size compute, and restructure pipelines so your data platform runs the way it should. When workloads are tuned, you stop paying for resources you do not need.

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Data Cost Optimization

Most data platforms underperform because they were built for a different workload, scaled without tuning, and never questioned the vendor defaults. Warehouses run queries that take minutes when they should take seconds. Connectors sync entire tables when incremental loads would suffice. Clusters stay oversized for peaks that happen once a month. These are performance problems that show up as cost problems.

80%

ELT cost reduction after pipeline performance optimization

$40M/yr

Compute savings at a Fortune 500 after performance optimization

0 hrs

Downtime during platform migrations

Use Cases

Engagements where this is the right work.

Query performance audit

Your dashboards take minutes to load and your warehouse bill jumped 40% in the same quarter. Both symptoms have the same cause. We trace performance bottlenecks to specific queries, pipelines, and architecture decisions and fix the ones wasting resources.

Runaway cloud data costs

Your Snowflake or Databricks bill doubled and nobody can explain why. We pull your usage data, trace the spend to specific queries, pipelines, and compute patterns, and show you exactly where the money is going and what to cut first.

Platform right-sizing

You are paying for enterprise capabilities your workload does not need, or your data outgrew a platform that made sense two years ago. We match your volumes, query patterns, and latency requirements to the right tool and tier and handle the migration if switching platforms is the answer.

Architecture review before renewal

Your annual contract is coming up. Before you sign, let us show you what a right-sized architecture would look like. Most clients find they are either overpaying for capabilities they do not need, or underusing features they already have.

Pre-renewal contract review

Your warehouse or ELT contract is up. We benchmark current usage against the contract, identify the levers, and tell you what a defensible negotiation looks like.

Our Process

A structured approach that delivers results at every stage.

Diagnose Performance Bottlenecks

Every engagement starts with the same discovery. Queries scanning entire tables when a partition filter would eliminate 90% of the I/O. Warehouses auto-scaling past what the workload requires. ELT jobs rebuilding complete datasets when incremental loads would suffice. Multiple tools running the same transformations because nobody decommissioned the old pipeline. These are not cost problems at their root. They are architecture and performance problems. The bill is just the symptom.

Output: Diagnostic report with cost drivers, bottlenecks, immediate fixes, and long-term architectural recommendations

Optimize Architecture and Workloads

We start with the changes that deliver the most performance improvement with the least disruption: query optimization, warehouse right-sizing, and pipeline consolidation. Then we evaluate whether architectural changes would deliver additional gains worth the migration effort. If the right platform for your workload is Snowflake, we optimize Snowflake. If your workload would run faster on MotherDuck or BigQuery, we tell you that too. Every recommendation is based on your data volume, query patterns, latency requirements, budget constraints, and team capabilities.

From our podcasts: Serverless Analytics Data Warehousing with MotherDuck, and DuckDB: A Game Changer in Data Analytics with Alex Monahan.

Output: Refactored queries, right-sized infrastructure, and rewritten transforms

Measurable Results

We reduced ELT resource consumption by 80% for a DTC e-commerce client by restructuring their pipeline architecture and moving to a better-fit ingestion platform. We improved job success rates from 50% to 90% at a Fortune 500 technology company through compute optimization, eliminating wasted retries and freeing engineering time. We have migrated multiple clients from overbuilt infrastructure to right-sized platforms with better query performance and dramatically lower bills. The pattern is consistent: organizations accumulate performance debt over time, and resolving it frees both engineering capacity and budget.

Output: Before/after cost report with month-over-month savings tracking

Technologies

We pick the right tool for the problem, not the other way around.

Insights

Perspectives from our team on data cost optimization.

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Results

Real outcomes from real engagements.

Food & Beverage / Retail

Data Warehouse Migration for a Growing Coffee Chain

Redshift to MotherDuck

Warehouse platform

Eliminated cluster provisioning and management

Infrastructure overhead

Read the full story

E-Commerce / Direct-to-Consumer

ELT Cost Reduction for a Subscription Apparel Brand

Enterprise Technology

Telemetry Lake Optimization for an Enterprise Security Team

30%

Infrastructure cost reduction

50% to 90%

Pipeline success rate

Read the full story

Frequently Asked
Questions

Will migrating platforms break our existing pipelines and dashboards?

Not if you do it right. We run old and new systems in parallel, validate outputs match, and only cut over when everything is verified. Zero-downtime cutovers are an option when the client requires them — they cost more in engineering time, so we recommend them only when the business case justifies the premium. Either way, we plan for continuity.

How long does a cost optimization engagement take?

The audit typically takes 2-3 weeks. You get a written report with specific findings, dollar amounts, and prioritized recommendations. If you move forward with migrations, most complete within 2-4 months depending on complexity.

What if we are locked into a multi-year contract?

We work within your contract constraints. Sometimes the biggest savings come from restructuring how you use a platform, not switching platforms. We optimize queries, consolidate warehouses, and eliminate redundant pipelines, all within your current stack if needed.

How do you measure results?

We pull usage and performance data from your cloud and data platform monitoring. Query execution times, compute utilization, job success rates, and billing data. You see what is underperforming, why, and what changes would fix it. The cost savings are a measurable byproduct of resolving performance problems.

Will you recommend switching platforms to save money?

Only when the workload justifies it. Many cost problems are caused by how a platform is configured, not which platform you are on. We optimize what you have first. If a different platform would genuinely deliver better performance at lower cost for your data volumes, query patterns, and concurrency requirements, we will tell you that and handle the migration. But we never recommend a switch just because a cheaper option exists. The right platform is the one that best achieves your objectives.

Do you have vendor partnerships that bias your recommendations?

We have implementation expertise with MotherDuck, Estuary, and other platforms, but no reseller agreements or referral fees. Our recommendations are based on your workload characteristics, performance requirements, and objectives. If staying on a higher-cost platform is the right call for your latency or concurrency requirements, that is what we recommend. We choose the platform that best achieves your goals, not the one with the lowest sticker price.