Performance Trade-offs & Optimization Strategy Questions

Reframe it: this is not really a binary conflict

Increasing autoscaling cooldowns (the waiting period after a scaling action before scaling again, to avoid reacting to every small fluctuation) as a single global setting is a blunt instrument. DevOps's thrashing problem, rapidly scaling up and down in response to noise, is likely coming from the bulk of ordinary traffic, while Product's non-negotiable is a handful of specific, critical flows. Those two asks are usually compatible once you stop treating "the system" as one uniform thing.

How I'd resolve it

1. Segment the policy. Apply a longer cooldown broadly, which fixes the thrashing and its cost and instability, while giving the critical flows their own scaling policy, or a bit of always-on baseline capacity, with a short cooldown or none at all, so they are insulated from the broader change.
2. Quantify both sides before deciding, not just assert them. For example: if the cooldown moves from 60 seconds to 300 seconds, how many extra seconds of under-provisioned capacity could a critical flow actually see during a genuine spike, and how often has that historically happened. Compare that against the real cost of thrashing, wasted spend, instability, alert fatigue.
3. Bring that data to both sides. Show DevOps the latency-risk numbers for the critical flows so they see why a blanket setting will not work, and show Product the cost and stability data driving DevOps's request. Most "won't budge" positions soften once the other side's cost is made concrete rather than argued in the abstract.
4. If a genuine conflict remains after segmenting, for example the critical flows truly need guaranteed capacity DevOps cannot provide reactively, resolve it as a business trade-off: the cost of always-hot standby capacity for those specific flows, presented as an explicit dollar line item Product can choose to pay for.

The core move as engineering lead

The job is not to pick a winner, it is to find the axis where both requirements can be true at once, which usually exists once you separate critical from non-critical traffic, and to put a concrete number on what it costs if it genuinely does not.

Making the case to product

I'd start by translating the ask into terms product actually weighs: what does the current tight SLO (service-level objective) actually buy users on this specific feature, versus what it costs to maintain. For a non-critical feature, a very high SLO is often solving a problem nobody has, since the feature copied its target from a customer-facing system rather than having one set deliberately for its own usage pattern.

Concrete example

Say an internal reporting dashboard inherited a 99.95% availability target from the main customer-facing API, but it's only used by a handful of internal staff during business hours. Relaxing it to 99.5% would save real infrastructure cost (redundant capacity, over-provisioned failover). Concretely: over a 30-day month (43,200 minutes), 99.95% allows about 22 minutes of downtime and 99.5% allows about 216 minutes, so the relaxation trades roughly three more hours of allowed downtime a month, a gap unlikely to ever be noticed on a system that's only watched during business hours, not around the clock.

Handling the "not sold" objection

Rather than asking for a permanent commitment up front, I'd propose it as a bounded experiment: relax the SLO for a defined window, say four to six weeks, with an explicit rollback if it causes real pain. That lowers the perceived risk of agreeing and gives product a concrete off-ramp if they're wrong to worry, or right to worry.

Guardrails to catch regression fast

I wouldn't just watch the raw SLO number, since that's a lagging indicator. I'd track leading signals instead: support ticket volume mentioning the feature, retry or error-rate spikes, and alerts from any other service that depends on it. I'd also set a review date on the calendar regardless of outcome, so the decision gets revisited deliberately rather than being forgotten once it stops looking risky.

What an error budget is

An error budget is the inverse of the SLO (service-level objective): if the target is 99.9% availability, the budget is the remaining 0.1%, which works out to roughly 43 minutes of allowed unreliability per month. It turns "be reliable" from a vague aspiration into a number the team can spend or overspend.

Using it to decide feature versus reliability work

The policy has to be agreed and written down before it's needed, not negotiated in the moment. Each month, if the budget remaining is healthy, the team ships features on the normal roadmap. If the burn rate shows the budget getting exhausted early, or it's already gone, feature work pauses and the team shifts to reliability work until the budget recovers. This is mechanical, not a monthly argument, which is the whole point of having the policy.

Worked example

Budget is 43 minutes for the month. By day 15, a bad deploy has already burned 30 minutes, 70% of the month's allowance. Per policy, new feature launches freeze, and engineering effort redirects to root-causing and hardening until the burn rate normalizes and there's budget left to responsibly ship again.

When product disagrees

Because this is payment processing, I'd keep the override bar high given the real cost of a reliability failure: chargebacks (a forced payment reversal initiated by the customer's bank, whose cost the merchant absorbs), regulatory exposure, and trust. If product wants to ship anyway, the answer isn't a debate in the moment, since the policy was already agreed by both eng and product leadership ahead of time. Any exception has to escalate to whoever owns that trade-off at the leadership level, and gets logged as an explicit, visible risk acceptance with a stated business reason, rather than quietly overriding the policy and eroding it for next time.

How I'd rank the three

Out-of-memory (OOM) crashes are usually first, because they're an availability risk with asymmetric downside: a crash can cascade into an outage, and outages tend to escalate on their own. Rising cost and creeping p99 (99th-percentile) latency are both real, but neither typically threatens uptime the way crashes do.

Before locking in that order, I'd check whether the three are actually connected. Memory pressure that causes OOM crashes often also causes garbage-collection pauses that show up as p99 latency spikes, and both frequently mean the service is under-provisioned or has a memory leak that's also driving up instance size and therefore cost. If that's the case, one root-cause fix addresses all three, and the "ordering" argument becomes much easier to make because it isn't really a trade-off at all.

Quantify before deciding

I'd put a number on each: crash rate and the share of requests it affects, the cost trend (is it flat or accelerating), and how far p99 latency has drifted from its target. Say OOM crashes affect 2% of requests twice a week, cost is growing 5% a month, and p99 latency has drifted 40% above its target: that pattern argues for fixing OOM first, since it is the only one of the three with real outage risk attached. A slow, stable cost creep is a very different priority than one that's about to blow the quarterly budget.

Explaining the ordering to stakeholders

Each stakeholder is right that their issue matters, so I wouldn't frame this as "your issue loses." Instead I'd translate all three into one shared currency, usually risk and dollars: OOM crashes risk customer-facing outages and churn, cost growth risks budget overruns, and p99 latency risks conversion and SLA (service-level agreement) breaches. I'd show the ranking with the reasoning attached, not just the conclusion, give each stakeholder a concrete date their issue gets addressed, and flag if the shared root-cause investigation might resolve more than one of their concerns at once. The goal is for everyone to leave knowing their concern is on the plan, with a date, even if it's not first.

A strong answer here walks through one concrete initiative rather than speaking in generalities. Here's the shape of a solid example.

Situation

A background processing service's cloud spend had grown steadily for months while the team's other priorities absorbed attention, and it was flagged in a cost review.

Alternatives considered

• Scale down or renegotiate instance types: fast, but doesn't address the underlying inefficiency and just delays the next cost review.
• Add a caching layer in front of a repeatedly-recomputed step: medium effort, medium risk, targets the actual waste.
• Rewrite the core processing algorithm: highest potential payoff, but the highest effort and risk, with a real chance of introducing bugs in a working system.

Why caching won

Profiling showed a large share of compute was spent recomputing the same results for repeat inputs. A cache addressed that specific waste directly, was reversible (it could be disabled instantly if it caused staleness problems), and didn't require touching the core algorithm's correctness. The rewrite was shelved as a later option if caching alone wasn't enough.

Validating it worked

Before rolling out broadly, I'd prototype the cache on a subset of traffic and measure hit rate and the actual cost delta over a full billing cycle, not just a few hours, since traffic patterns vary by day. I'd also watch a correctness guardrail, since a caching layer can silently serve stale data if invalidation is wrong.

Result and reflection

A believable outcome is a meaningful reduction in redundant compute with no correctness regressions, validated over at least a couple of weeks of real traffic rather than a single good day. Looking back, the honest reflection is usually about what would have caught a caching bug faster, for example, a staleness alert that fires automatically rather than relying on someone noticing.