New products keep landing on the same warehouse or project.
A workload is the job behind the cost: dbt, Fivetran, Snowpark, Dataform, Dynamic Tables, Cortex, Looker, Tableau, and the other tools already running on the account. Anavsan reads how that job ran, then tells your team what to do next. It recommends the action. Your team approves anything that changes production.
DataOps gets the next action CIO / CTO keeps growth controllable
The example opens on this page. Book a Demo is a conversation.
Your business data stays in your account Production changes wait for your approval
The problem
Applications, pipelines, AI-written queries, and AI workloads keep landing on the same account. Native tools show what was consumed. Anavsan tells your team what should happen next.
New products keep landing on the same warehouse or project.
ELT jobs multiply faster than anyone reviews their compute.
A query can be written in seconds. It can also waste compute for months.
Agents and Cortex jobs add a new class of spend with the same old gap.
Credits, slots, jobs, and warehouses after the fact.
Evaluate the workload. Recommend one action. Your team approves it. Then measure it again.
How a decision gets made
APEX recommends Run, Optimize, Redirect, or Review. Redirect names a better path. It does not move the workload. Your team approves anything that changes production.
Name the work behind the cost, not only the account it landed on.
Run, Optimize, Redirect, or Review. One choice, with the reason.
The change goes through your review. Anavsan does not apply it.
Same workload. Did the cost fall, and does the performance requirement still hold?
Keep it moving. No change.
Use less compute. Same requirement.
Take a better path. You make the move.
Needs a person before it becomes scheduled spend.
See it on this page
Pick a kind of work. Watch the recommendation, and why a team could approve it. The numbers are illustrative. They are not a customer result.
Cutting the warehouse in half did not double the runtime. On Large the same runs take about 11 minutes instead of 8, still one query at a time, still no queue and no spill. The requirement is 45 minutes.
Anavsan recommends the smaller warehouse. Data engineering approves it.
Illustrative data. Not a customer result.
Who it is for
DataOps gets a named action. The CIO and CTO see which workloads still earn their compute, so growth stays controllable.
See which workloads earned their compute, and which ones cost more than the performance they have to deliver. Room for later AI work can follow. It is not the claim this page leads with.
Keep what is earning its cost. Change what is not.
Performance is met and the spend is in line. Leave it running.
One recommendation. The team approves it. Then you measure that workload again.
APEX names the workload, the owner, and the change. Your review deploys it. The same workload is measured again.
The job behind the cost, not only the account it ran in.
The team that can actually change it.
They ship it through the review they already use.
The same workload, measured again, so the ticket stays closed.
Snowflake and BigQuery
The words differ. The decision does not. See the workload, recommend the change, your team approves, then measure it again.
Which work earned its credits, and which should change.
Which jobs earned their cost, and what to change.
Safe to evaluate
Anavsan works from how workloads run and what they cost. It does not take the contents of your tables, and it does not change production on its own.
Snowflake stays in Snowflake. BigQuery stays in BigQuery. Table contents are not the input.
A recommendation is not a production change. Your review still applies.
Where operational data goes, how long it is kept, and how the Native App differs. No sign-in.