The baseline.
The workload before the change: what the service did, how much of it, and what that cost in latency, resources, and money.
Selected work
The result matters. So does the reasoning.
A closer look at engineering work and the decisions behind it. Reported outcomes are specific to the project described.
Production service rewrite
Python→Rust
PROFILE / REBUILD / MEASURE↗
Performance · resource efficiency
Changing the workload’s cost at the source.
A production service rewritten from Python to Rust, with reported reductions in latency and resource consumption. The case study explains what changed, what the reported numbers cover, and what the account leaves out, such as implementation effort and the measurement window.
10×reported improvement
~$1Mestimated annual savings
How we write up work
What changed, what was measured,
what is still unknown.
A savings number is only useful when you can see the conditions behind it. Every case study here keeps those three things apart. We publish a case only when the baseline, the change, and the measurement can all be stated.
The change.
What was actually done, and why that layer rather than another one: code, database, architecture, or capacity.
The measurement.
What was measured afterwards, over what window, and how reliability and error rates moved with it.
The gaps.
What the account does not show: engineering effort, comparable workload, or verified financials. Named, not filled in.
Start with the problem
What could work better?
Bring the bottleneck, the rising bill, or the release that takes too long. We’ll help you find a useful next step.