Dimensionnement optimal avec AWS Lambda Power Tuning

Lambda gives you one performance dial: memory. CPU and network scale with it, so memory tuning controls both speed and cost.

More memory costs more per millisecond but often finishes faster. Sometimes a higher memory setting is cheaper overall.

You usually optimize for one of:

• Lowest cost (minimize GB-seconds).
• Lowest latency (fastest response).

The best memory for each can differ.

AWS Lambda Power Tuning is an open-source Step Functions tool that runs your function at several memory sizes and charts cost vs speed.

You give it the function ARN and a list of memory values. It invokes each many times, measures duration and cost, and recommends the best setting.

The output gives the optimal power value and shows the cost/latency curve. A "balanced" strategy weighs both equally.

For CPU-bound code, raising memory until duration stops improving usually finds the sweet spot. Beyond it you pay more for no speed gain.

If the function mostly waits on the network or a database, extra memory barely helps. Keep memory low to save cost.

Apply the recommended memory to the function configuration.

Tuning reflects current code and dependencies. Re-run power tuning after major code changes or new library versions.

Switching to the arm64 (Graviton) architecture often improves price-performance further. Tune memory after migrating architectures.

Test your tuning knowledge.

You learned to right-size memory with Lambda Power Tuning, choosing for cost or latency, recognizing I/O vs CPU-bound behavior, and combining tuning with arm64.