Snowflake Cost Optimization
OhChimp reads your Snowflake credit usage to cut idle warehouse spend: tighten auto-suspend, reset multi-cluster minimums, verify the drop on your bill.
What OhChimp optimizes for Snowflake
Some of the cost levers OhChimp checks for Snowflake. Each one becomes a reviewable plan you approve before anything changes.
Warehouses that never auto-suspend
A warehouse with auto-suspend off keeps billing credits per second while it sits idle between queries. OhChimp finds warehouses that never suspend yet consumed real credits in the window, and drafts an ALTER WAREHOUSE plan to set a short auto-suspend (60 seconds is typical) so they stop billing when no query is running.
Auto-suspend set too slow
A long auto-suspend timeout means a warehouse lingers online for minutes after its last query, paying for compute it does not use. OhChimp flags warehouses that suspend only after longer than 10 minutes, then writes the plan to tighten the timeout so idle time stops adding up.
Multi-cluster minimum above one
Setting MIN_CLUSTER_COUNT above one keeps extra clusters running around the clock, even when concurrency does not need them. OhChimp spots warehouses pinned that way and plans the change to set the minimum back to one, letting autoscaling add clusters only when demand actually shows up.
Credit attribution by warehouse
You cannot cut what you cannot see. OhChimp reads WAREHOUSE_METERING_HISTORY and ranks your warehouses by credits consumed, with an estimated monthly cost per warehouse, so the biggest spenders are obvious before you touch a setting. Dollar figures use Snowflake Standard list price by default and accept your negotiated contract rate.
Compute vs cloud-services split
Cloud-services credits can quietly creep up alongside compute, and the two hide inside one total. OhChimp reads METERING_DAILY_HISTORY to break your credit usage into compute and cloud-services, so a cloud-services bill that has grown out of proportion is easy to spot.
Snowflake cost optimization FAQ
How does OhChimp connect to Snowflake?
Through the Snowflake SQL API using key-pair JWT authentication. You assign OhChimp's public key to a Snowflake user and grant a read-only role. OhChimp signs a JWT with the matching private key and runs read-only statements against ACCOUNT_USAGE and SHOW WAREHOUSES. It reads usage metadata and warehouse settings only.
Does OhChimp need write access or see my data?
No. OhChimp runs as the read-only role you grant and only reads credit metering, warehouse settings, and the compute versus cloud-services split. It never queries your tables, and it never stores your application data, secrets, or query contents. The plans it writes are ALTER WAREHOUSE statements you review and apply yourself.
What Snowflake costs can OhChimp actually reduce?
Idle warehouse credits, mostly. OhChimp finds warehouses that never auto-suspend or suspend too slowly, multi-cluster warehouses with a minimum cluster count above one, and ranks every warehouse by credits consumed so you see the biggest spenders. It also splits your usage into compute and cloud-services credits.
Who applies the changes, and can they be rolled back?
You do. Each fix is a reviewable plan, an ALTER WAREHOUSE statement, with a confidence score, a risk level, and rollback steps. Nothing changes in your account until you click apply, and you are always the one who clicks.
How are Snowflake savings verified, and what does it cost?
Against your real Snowflake bill. A plan is marked VERIFIED only after 7 or more days, a drop of at least 10%, and 3 consecutive positive checks. Until then it stays flagged not implemented. Pricing is a flat monthly fee with no cut of your savings, plus a year-one ROI guarantee: a full refund of subscription fees if it does not pay for itself in your first 12 months on a paid plan.
The auto-suspend lever, start to finish: Snowflake AUTO_SUSPEND: stop paying for idle warehouses.
Related integrations
Teams running Snowflake usually run these too. OhChimp finds the waste in each and proves it on the bill.
Confluent
Cluster sizing, throughput, and storage
Databricks
Cluster sizing, idle DBUs, query cost
PlanetScale
Plan tier, row reads, and storage