Solutions for everyone who touches the bill
Cost analytics, ranked savings and the Cognis AI DBA — shaped for your team, your industry and the problem you need solved.
The problems we solve
Each one is built into every scan, with the evidence and the math shown.
Query waste
Stop paying for bytes nobody needed
Most bills come from a few queries that look filtered in SQL and still read everything. We rank them by monthly cost and show the filter, cluster key or column list that would have skipped the rest.
Learn moreStorage optimization
Pay only for the bytes you still use
Storage is the quiet line item: the wrong billing model, tables nobody queries, and layouts that make every job read too much. We compare billing models and flag tables whose layout no longer fits.
Learn moreCapacity & pricing
The meter is often the waste, not the SQL
On-demand vs Editions, idle reservation baselines, slot contention and jobs queued for slots. We split 'wrong SQL' from 'wrong capacity', so you fix the meter when the meter is the waste.
Learn moreMetadata-only access
We price your data without reading it
SlashPeak prices your warehouse from catalog metadata alone, on BigQuery today and on Snowflake and Databricks next. The scanner never asks for dataViewer or SELECT on your tables.
Learn moreCost attribution
A finding without a name never gets fixed
Every query finding names who ran it (person or service account), which tool sent it (dbt, Looker, Sheets, Airflow…) and which environment it hit, so each fix reaches the team that owns it.
Learn moreNot sure where to start?
Your first scan sets the order
Connect a read-only service account and run your first scan. Every finding is ranked by what it could save you each month, so the biggest fix is always first, with the math shown.
Start for freeThe same numbers, the view each team needs
Engineers get the query to fix. Finance gets a number it can trust. Leaders get a plan.
Data engineers
Find the pipelines that drive the bill
See which scheduled queries, pipelines and tables cost the most, and get a ranked list of what to fix first — with the SQL and the table named.
- Top jobs and tables by cost
- Partition and clustering gaps
- Repeated and full-scan queries
Analytics engineers
Know what every dbt model costs
Tie warehouse spend to the dbt models that produce it. Fixes for model config land as pull requests on your repo, never as silent changes.
- Cost per dbt model
- Materialization advice
- Fixes as pull requests
DBAs
Capacity, storage and access in one view
Watch slot usage, reservations and storage billing side by side. The Cognis AI DBA can apply routine fixes inside a policy you set, and almost every change can be rolled back.
- Reservation and slot health
- Storage billing model
- Audited, reversible changes
FinOps & finance
One spend number everyone trusts
Spend split by project, team, label and user, in your own timezone. Pricing-model aware, so on-demand and capacity costs are never mixed up.
- Spend by team and label
- Month-to-date and forecast
- Anomaly alerts
Heads of data
A plan, not just an invoice
A read-only scanner, a separate write key only for the fixes you allow, and a log of every change — controls security can sign off on, plus realized savings you can report upward.
- Realized savings over time
- Full audit trail
- Metadata-only access
Built for the way your industry uses data
Every industry wastes warehouse spend in its own way. These are the patterns we look for first.
Financial services
Regulated data, heavy reporting
Risk and regulatory reports re-run the same wide scans daily. Access must stay tight and every change auditable.
- Repeated report scans
- Metadata-only access
- Audit trail for every change
Insurance
Long retention, large history tables
Years of policy and claims history sit in storage, and actuarial queries scan far more of it than they need.
- Stale and cold storage
- Storage billing model
- Partition filter gaps
Healthcare & life sciences
Sensitive data, ad-hoc research
Researchers run ad-hoc queries over large clinical and genomic tables, and study datasets sit idle once a project ends.
- Ad-hoc full-table scans
- Idle study datasets
- SQL masked in BigQuery
Retail & e-commerce
Seasonal spikes, many dashboards
Traffic peaks drive query peaks. Dashboards that refresh too often and SELECT * over event tables add up fast.
- Spend anomalies at peak
- Dashboard refresh cost
- SELECT * over event tables
SaaS & technology
Product analytics at scale
Event pipelines grow faster than budgets. Split spend by team and service, and pick the right pricing model.
- Spend by team and service
- On-demand vs capacity
- Pipeline cost trends
Media & gaming
Huge event volumes
Telemetry and engagement tables are big and queried often. Clustering and partition pruning matter most here.
- Clustering advice
- Streaming vs batch cost
- Large-table scan waste
Telecom
Network-scale logs
Network and usage logs land continuously. Storage growth and full scans over log tables are the usual cost drivers.
- Storage growth trends
- Log-table scan waste
- Reservation sizing
Don’t see your industry?
Tell us how your team uses the warehouse. We will show you where the spend goes.
Talk to usLet AI do the routine work
Inside a policy you set. Every change is logged, and almost all can be rolled back.
Start on BigQuery
Live on BigQuery today. Join the Snowflake or Databricks waitlist for the same read-only access.
Take control of your warehouse bill
Understand, reduce and govern your cloud data spend with SlashPeak — starting today.
- 30-day free trial
- No credit card
- Read-only access
- Set up in minutes