# Spot the run where a query got expensive > Track one Snowflake query's cost over time. Every run sits under one row, so you see which run started costing more and what the platform found wrong. Source: https://altimate.ai/product-highlights/snowflake-query-cost-over-time [.md](https://altimate.ai/product-highlights/snowflake-query-cost-over-time.md) Copy as MD Enterprise Platform ## Track Snowflake Query Cost Over Time, Run by Run Track one Snowflake query's cost over time. Every run sits under one row, so you see which run started costing more and what the platform found wrong. Saurabh AroraJul 5, 2026Jul 31, 20266 min read Track Snowflake query cost over time, run by run TL;DR - **What it is.** Snowflake query cost over time, with every run of the same query under one row on the Code page. Each run carries what the platform found wrong with it, so you can compare one run to the next. Nothing to configure. - **Who it is for.** Analytics engineers and data platform leads who own long-lived queries behind dashboards and nightly jobs. - **What you get.** You find the exact run where a query started costing more, and what changed on it. ## Snowflake Query Cost Over Time Sits Under One Row A query took twelve minutes six months ago. Today it takes forty. It never tripped an alert, because each week only added a minute or two as the upstream table grew. Nobody checks one query week by week. The Groups tab on the Code page does that checking for you. One row is one query. Every run of it sits under that row, going back as far as your history does. The platform writes the group name, so you can tell what a row is before you open it. The problem is that the same query rarely looks identical twice. A nightly job filters on a rolling date window, so its text and its hash change every run. Match on that hash and you see thousands of one-off queries that never repeat. The platform reads past the changing dates and files every run of that query under one group. That gives one query a stable identity across months of runs. A dashboard firing the same SELECT every night becomes one row, with all of those runs under it. You are no longer matching up thousands of separate executions by hand. ## See What the Platform Found Wrong on Each Run The platform checks every run against a list of known anti-patterns, and the finding it attaches to that run is called an insight. Insights have names like `warehouse_resize_prospect`, `query_with_local_spillage` and `query_timed_out`. An insight applies at one of two levels: | Level | Scope | Example | | --- | --- | --- | | **Group Insight** | The query and every run of it | `zero_impact`, `warehouse_resize_prospect` | | **Per-run insight** | One single execution | `query_timed_out` on last night's run but not the night before | Say a query used to come back `warehouse_resize_prospect` and now comes back `query_timed_out`. That tells you two things. The platform first thought the warehouse was too small for that query. Later, the same query stopped finishing at all. You also get a date out of it. The insight changed on one specific run. It did not drift a little at a time across all of them. You know which day to look at. A rising cost line on its own can never tell you that. The diagram below follows one query across five runs, with the run where the insight changed marked in red. A diagram titled Same query hash, changing insight. Runs 1 to 3 carry the insight warehouse\_resize\_prospect at $0.42, $0.44 and $0.51, a dashed red line marks the transition, and runs 4 and 5 carry query\_timed\_out at $1.20 and $1.85. A Groups panel on the right lists two query groups with sparkline cost trends and a current insight column. ## Rank Every Query by What It Cost You The Groups tab is the list you audit from. Every query is one row. You sort those rows by total cost or by cost trend, and each row also shows the insight that applies to it right now. Here one group ran 120 times in four weeks, at a 55 minute average and $3.89K total across those runs. The Groups tab on the Code page, listing parameterized query groups. One group is highlighted, with its total run count of 120, a 55 minute average execution time, a $32.42 average cost, a $3.89K total cost and a $50.77K annualized cost, beside columns for warehouses, users and insights. Sort by cost trend and the query that has been getting more expensive comes to the top of the list. You did not have to know that query existed to find it. Each row rolls up from the Queries feed. Every single execution in that feed already carries its own estimated cost, how long it ran, and its insight tags. The Queries page filtered to Snowflake, listing individual executions with estimated cost, execution time, and insight tags including create\_or\_replace\_table, query\_with\_local\_spillage, select\_star, agg\_before\_join, exploding\_join and long\_running\_query, alongside the warehouse and user for each run. You can click a tag. Click `redundant_filter_condition` and the detail view highlights the exact lines of the WHERE clause behind it. Below that, it restates the filter in plain words. A redundant\_filter\_condition detail. The upper pane highlights lines 495 to 504 of a WHERE clause in the query source, and below it a Query card and a Problem card sit side by side, restating the same filter as a domain list plus a year test plus a quote-stage and booked test. ## What You Get from One Query Group A group gives you more than a list of runs. It tells you what is wrong across every run of a query at once. Then you open the one run that proves it. The opportunities on a group apply to the whole group, not a single run. Every run of this query spills 26.05 GB to local storage. So one fix, resize the warehouse, pays off across all 120 runs at once. The Opportunities tab for a query group, showing two query\_with\_local\_spillage findings, each reporting 26.05 GB spilled to local storage during execution, with an effort rating and a time-savings estimate. From there you open any single run, each with its own cost, run time and timestamp. That is where a group-wide finding turns into the one run you go and read. The Queries tab inside a group: a Query Executions table where every row shares one query hash but carries its own estimated cost, execution time and timestamp, the individual runs that roll up into the group. - **A cost history per query.** Every run of it, with what each run cost you. - **The run where it changed.** The one execution where the insight went from one problem to another. - **The clause to fix.** The lines of SQL behind that insight, highlighted for you. Proof A run's cost is not the warehouse bill divided by the number of queries. The platform reads everything else running on that warehouse at the same time and splits the cost on that basis. That is what makes one run comparable to the run before it. Without it, a cost trend would only be telling you how busy the warehouse was that night. Query Insights is part of the Enterprise Platform, and the [Enterprise Platform overview](https://altimate.ai/platform) covers the rest of what it tracks on your Snowflake spend. ### Talk to us about your warehouse [Book a 30-min demo →](https://calendly.com/d/ctnv-rbc-yr3/altimate-ai-overview) ## Frequently Asked Questions How does the platform know two runs are the same query? What is the difference between a Group Insight and a per-run insight? What kind of regression does this actually catch? Can I see which clause is causing the problem? How do queries get grouped into a job? Where does the cost figure on a run come from? How does this differ from workload root-cause analysis? Where can I see this on my own account? On this page - Snowflake Query Cost Over Time Sits Under One Row - See What the Platform Found Wrong on Each Run - Rank Every Query by What It Cost You - What You Get from One Query Group ## Related Resources Enterprise Platform Root Cause Analysis for a dbt, Tableau, or Snowflake Workload Spike https://altimate.ai/product-highlights/snowflake-workload-root-cause-analysis Enterprise Platform Snowflake Cost Analysis in Plain English, with AI That Cites Every Number https://altimate.ai/product-highlights/snowflake-cost-analysis-ai-cited-answers Enterprise Platform Auto Tune Right-Sizes Snowflake Warehouses Without Slowing Queries https://altimate.ai/product-highlights/snowflake-warehouse-auto-tune-right-sizing Help Doc AI Cost Analysis (DataPilot) https://help.altimate.ai/platform/summary/datapilot-analysis/