Right-Size Every Dataflow Job's Workers
Dataflow streaming jobs are easy to over-provision. The wrong instance type or worker count quietly inflates cost with no performance benefit. Sedai analyzes historical job performance and recommends the instance types and worker counts that fit actual demand, with projected savings you can review before acting.
The Right Dataflow Configuration Isn't a Guess. It's a Moving Target.
Dataflow doesn't reward over-provisioning the way other services do — it punishes the wrong configuration, and that configuration changes as job behavior evolves.
Instance type and worker count are easy to get wrong.
Both are typically set once, without a clear view into how the job actually performs over time.
Streaming jobs don't behave consistently.
CPU, memory, and worker needs shift as data volume and processing patterns change, so a config that fit last quarter may not fit now.
Revisiting configuration requires ongoing analysis, not a one-time fix.
Without continuous visibility into historical usage, teams have no reliable signal for when a job's setup is due for a change.
How We Help
Automated Job Analysis
Sedai runs scheduled background analysis across eligible Dataflow jobs, continuously collecting CPU, memory, and worker utilization data — no manual monitoring required.
Instance & Worker Recommendations
Based on historical usage patterns, Sedai recommends more cost-effective instance types and optimal worker counts tailored to each job's actual behavior.
Savings Reports Before You Act
Every recommendation is saved to a report with projected cost savings, so you can review the impact and make an informed decision before changing anything.