AWS reliability: the 7 patterns we implement most often
Teams usually ask for “high availability”. What they actually need is **reliability as a capability**: design, change management, and recovery that keeps customer impact low even when components fail.
AWS captures this well in the **Well‑Architected Framework** (Reliability Pillar): build strong foundations, resilient architecture, consistent change management, and proven recovery processes.
Below are seven practical patterns we implement most often when helping teams run production workloads on AWS.
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1) Start with explicit RTO/RPO (not vibes)
Before you design:
- **RTO**: how quickly do we need to recover? - **RPO**: how much data can we lose?
Those two numbers shape everything: database topology, backups, replication, and failover strategy.
2) Design for blast radius containment
Aim for “fail small”. Typical levers:
- isolate environments (accounts) and workloads (VPCs) - use queues to absorb spikes and dependency failure - apply throttling and bulkheads so one workload can’t starve others
3) Multi‑AZ is necessary — but test it
It’s common to see Multi‑AZ configured but never exercised.
We recommend:
- scheduled “game day” failovers - validating alarms and runbooks - confirming downstream behaviours (timeouts, retries, backpressure)
4) Make change management a reliability feature
Many incidents start as change incidents.
Patterns that reduce risk:
- canary / blue‑green deployments - feature flags for risky behaviour - automated rollback (and rehearsed rollback)
5) Build observability around customer journeys
Infrastructure metrics are necessary, but not sufficient.
We like to add:
- SLOs and error budgets - synthetic checks for key flows - dashboards that show “how many customers are failing right now?”
6) Treat AWS Post‑Event Summaries as design input
AWS publishes Post‑Event Summaries (PES) for issues with broad customer impact.
We use PES-style thinking internally too:
- what failed? - what was the scope? - what mitigations reduce recurrence? - what would we change in *our* workload design?
7) Recovery is a product: practise it
Backups that restore slowly (or not at all) are not a safety net.
We validate:
- backup integrity - restore time - permissions needed during an incident - who is on point and how comms happens
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How PG Technologies helps
We help teams make AWS reliability measurable and repeatable:
- AWS Well‑Architected reviews (reliability/cost/security) - platform engineering and cloud architecture - observability, SLOs, and incident readiness - progressive delivery + rollback design
Sources
- AWS Well‑Architected Framework: https://aws.amazon.com/architecture/well-architected/ - AWS Well‑Architected Reliability Pillar: https://docs.aws.amazon.com/wellarchitected/latest/reliability-pillar/welcome.html - AWS Post‑Event Summaries (PES): https://aws.amazon.com/premiumsupport/technology/pes/
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