How we approach problems like these.

A team that had grown fast on AWS for a few years, without ever revisiting the original architecture decisions, is a pattern we see often: instances sized for peak load that hasn't existed since year one, Multi-AZ running in environments that don't need it, and nobody owning the monthly bill review.

The approach starts with a two-week audit: a full cost breakdown by service and tag, a right-sizing pass across every compute and database instance, and interviews with the team to separate workloads that genuinely need headroom from defaults nobody has touched. That produces a prioritized list ranked by savings-to-risk ratio.

Execution goes in waves so nothing ships without a rollback plan: right-sizing compute first (lowest risk, fastest win), then storage tiers and instance classes, then a savings-plan purchase sized against actual usage rather than a vendor's recommendation. A tagging policy and a monthly cost-review ritual keep the drift from re-accumulating afterward.

Every resize is validated against real load data before it ships, not estimated from a dashboard.

Past a certain number of services, the biggest reliability problem usually isn't any single service failing: it's not knowing which upstream dependency caused a given incident, because retries and timeouts were set per-team with no shared standard and there's no uniform view of service-to-service traffic.

The rollout goes in incrementally, starting with a single low-risk namespace to validate sidecar injection, mTLS, and the operational overhead before touching anything customer-facing. Each subsequent wave adds a handful of services, always behind progressive traffic shifting so a bad rollout can be reverted at the mesh layer instead of a full redeploy.

The payoff comes from three things landing together: mTLS closing a long-standing internal security gap, a uniform retry/timeout policy removing a whole class of cascading failures, and SLO-driven routing letting the platform shift traffic away from degraded instances automatically instead of waiting for a human to notice a dashboard.

The result on-call engineers care about: seeing exactly which hop in the request path is failing, instead of paging three teams to find out.

Moving from an on-prem system to AWS ahead of a funding round, with a first external HIPAA audit already scheduled, leaves no room for a rebuild after the fact: the landing zone has to be compliant from the start.

That means a multi-account landing zone with a dedicated security and audit account separated from workloads, encryption enforced at rest and in transit by policy rather than convention, and flow logs, CloudTrail, and Config feeding a centralized audit trail that can't be disabled by an individual account.

Guardrails are implemented as policy-as-code: rules that block non-compliant resources from being created in the first place, rather than a dashboard that flags them after deployment. Engineers don't have to remember the compliance checklist; the platform enforces it.

That's the difference between hoping an audit goes well and having already made the failure states impossible to create.

A single monolith handling checkout, inventory, and fulfillment, where every deploy is a maintenance window customers notice, is too risky to rewrite in one shot. The strangler-fig pattern extracts one bounded context at a time behind the existing monolith, never a big-bang cutover.

Each extracted service goes onto Kubernetes behind a feature flag, running in shadow mode against production traffic before it takes a single real request. Blue/green deploys and real-time traffic shifting mean a small slice of traffic can be routed to the new service, watched for error rate and latency, and dialed up or back within minutes.

The hardest part usually isn't the Kubernetes migration: it's data consistency during the transition, since the monolith's database and the new services' databases have to stay in sync mid-migration. The outbox pattern with change-data-capture keeps both sides consistent without distributed transactions.

Done this way, every bounded context can move off the monolith with no customer-facing downtime, and teams can ship their own service without coordinating a monolith-wide release.