How AI changed performance engineering (2 years later)

Fivetran’s platform engineering leaders describe how AI changed performance work by sharply reducing the time required to gather context from production logs, code history, telemetry, and complex execution paths, making it economical to evaluate many previously overlooked incremental improvements. The team used AI for codebase archaeology, static analysis of hot loops, fleet-wide log mining, rapid prototypes and microbenchmarks, reusable diagnostic skills, and automated operational tooling, while emphasizing that AI-generated findings require validation through trusted metrics and benchmarks. Reported outcomes included up to 70% benchmark import-throughput gains from asynchronous BATCH_COMPLETE signaling, roughly doubled SAP HANA benchmark throughput, 35–40% SAP HANA production improvements, elimination of over 90% of API calls in some GitHub connector scenarios, and targeted connector improvements such as parallelized Okta queries. The authors argue that AI’s primary benefit is not faster coding but faster identification and sizing of worthwhile work, enabling teams to inspect complete fleets rather than samples, avoid low-value projects through negative findings, and automate useful but historically unprioritized tasks. They conclude that AI is most effective when paired with established measurement infrastructure, performance benchmarks, and human judgment for prioritization.

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