Performance Optimization
Cloud spend and cloud performance are the same problem viewed from two directions, and both are usually diagnosed by guesswork. We measure first — where the time goes, where the money goes, and which of the two you are actually short of — and then change the things the measurements implicate rather than the things that are easiest to change.
What this covers
Right-sizing against real utilization
Most environments are provisioned against an estimate made once and never revisited. We size compute, storage, and database capacity against observed utilization including peaks, then apply the commitment and lifecycle options — reserved capacity, savings plans, storage tiering, scheduled shutdown of non-production — that fit the demand pattern the data actually shows.
Cost visibility and accountability
A consistent tagging model, enforced at deployment rather than corrected afterwards, so spend can be attributed to a team, product, or customer. With attribution in place, budgets and anomaly alerts become meaningful and chargeback reporting becomes possible — and cost stops being a single number that only one person is accountable for.
Caching, CDN, and query tuning
For latency-sensitive paths we profile the request end to end and address what the profile shows: a missing index, an N+1 query, an uncached call in a loop, an origin serving assets a CDN should. Caching is introduced with explicit invalidation rules, because a cache without a documented invalidation story trades a latency problem for a correctness one.
Autoscaling tuned to real traffic
Scaling policies built from your actual traffic shape — including how fast load arrives and how long instances take to become useful. We tune thresholds, cooldowns, and warm capacity so the system absorbs real spikes without thrashing, and we load test the policy, since an untested autoscaling configuration usually fails in the direction of an outage.
How the work runs
We instrument before we optimize. Without a baseline there is no way to demonstrate that a change helped, and optimization work that cannot be measured tends to continue past the point where it is worth doing.
Changes are made one at a time against that baseline, with the result recorded. That produces a defensible before-and-after per change and leaves your team able to repeat the method after the engagement ends. It also catches the changes that made things worse, which a batch of simultaneous optimizations reliably hides — and in cost work particularly, a change that looks like a saving often just moves spend into a line item nobody is watching.
What you get
- A measured baseline for latency, throughput, and cost per workload
- Right-sized resources with commitment coverage matched to demand
- Spend attributable to team or product through enforced tagging
- Tuned caching and query paths with documented invalidation rules
- Autoscaling policies validated under load test rather than in production
Who this is for
- Organizations whose cloud bill is growing faster than their usage
- Teams with latency problems they have not been able to isolate
- Groups that need cost attributed per product, team, or customer
Talk through your performance optimization work
Tell us what you are running today and what is not working. We will tell you whether this is the right engagement, or point you at the one that is.
Start a conversation