Industry
DevOps for E-commerce
Downtime during peak events costs orders. We build e-commerce infrastructure that scales from normal traffic to Black Friday levels without breaking, with fast page loads and checkout reliability that helps protect revenue.
E-commerce infrastructure challenges
Black Friday and peak traffic handling
E-commerce traffic patterns are highly seasonal, and peak events can be many times normal load. We plan load tests against expected peak with headroom, configure Kubernetes HPA and Karpenter for rapid scale-out (node start-up time depends on instance type and configuration), and use pre-warming strategies for known peaks. We also review database connection pool sizing and cache warm-up procedures.
CDN and edge caching strategy
Latency can affect conversion rate. We configure CloudFront (AWS) or Azure CDN with edge caching for product pages, images, and static assets. We implement cache invalidation strategies that do not cause availability gaps after deployments, and configure origin shields to protect backend databases from cache-miss storms.
Reliability SLAs and cart abandonment
Slow pages and failed checkouts can increase cart abandonment. We instrument Core Web Vitals, configure Apdex targets in Grafana, and implement multi-AZ database configurations with automatic failover to help protect checkout availability (failover time depends on the database engine and configuration).
Payment and PCI scope minimisation
Even if you use Stripe or Adyen, your infrastructure may be in scope if card data passes through it. We design tokenisation flows that keep raw card data out of your environment, which can reduce PCI scope and compliance burden. The applicable SAQ type is decided by your integration design and your acquirer or QSA.
Inventory and order management data integrity
Overselling due to race conditions in inventory management is a real production problem. We architect event-driven order processing with idempotent handlers, distributed locking where needed, and dead letter queues to ensure no order is silently dropped.
What makes e-commerce infrastructure different from other workloads?
E-commerce platforms combine unpredictable, spiky demand with a revenue path that must stay available. A marketing email, a social post or a seasonal sale can multiply traffic within minutes, and the part of the system that suffers first is usually the checkout, because it depends on the most downstream services: inventory, pricing, tax, payment and fulfilment.
Most of the platform is also read heavy and cacheable, while a small part is write heavy and strictly consistent. Good architecture treats these two halves differently. Catalogue and content traffic belongs at the edge and in caches, while carts, stock and orders need careful data design, because a lost or duplicated write is a customer-facing failure.
Finally, e-commerce teams ship constantly. Merchandising changes, promotions and integrations arrive weekly or daily, so the delivery pipeline needs to be as dependable as the runtime. A platform that scales well but cannot be deployed safely during a sale freeze is only half solved.
Which architecture patterns hold up under peak traffic?
The most reliable pattern is to keep as much traffic as possible away from the origin. A CDN in front of the storefront, with sensible cache keys, stale-while-revalidate behaviour and an origin shield, absorbs the bulk of anonymous browsing. Personalised and transactional requests then reach a much smaller set of services that can be sized and tested on their own.
Behind the edge, stateless services on Kubernetes or managed container platforms scale horizontally using the Horizontal Pod Autoscaler, with a node provisioner such as Karpenter or the cluster autoscaler adding capacity. Scaling decisions work best when they follow a meaningful signal such as request rate or queue depth rather than CPU alone, and when resource requests and limits are set from measured behaviour.
Where a sale date is known, scheduled pre-scaling and cache warming remove the reliance on reactive scaling for the first minutes of a spike. Reactive autoscaling is still configured as the safety net, but it is not the plan for a launch you can see on the calendar.
- Edge caching with cache invalidation that does not create availability gaps after a release
- Autoscaling on application signals, with pod disruption budgets protecting checkout during node changes
- Connection pooling and read replicas so database connections do not become the hidden ceiling
- Queues between checkout and slower downstream systems such as email, fulfilment and analytics
How do we keep orders and inventory correct?
Correctness comes from designing the order flow so that retries are safe. Payment providers, message brokers and browsers all retry, which means every handler in the order path should be idempotent, keyed on a stable order or payment identifier. This prevents duplicate charges and duplicate orders when a timeout makes a request look as though it failed.
Overselling usually comes from two requests reading the same stock level at once. Depending on the product, the fix is an atomic decrement in the database, a reservation with an expiry, or a single writer per stock keeping unit. The right choice depends on how strict your stock promises are, and we would agree it with your engineers rather than impose one pattern.
Event-driven processing adds dead letter queues and replay so that a failed message is visible and recoverable instead of silently dropped. Alerts on queue age and dead letter depth give the operations team an early warning that orders are stuck, well before customers write in.
| Failure mode | Typical technical control | Signal to monitor |
|---|---|---|
| Duplicate orders after a retry | Idempotency keys on order and payment handlers | Duplicate key rejections |
| Overselling a popular item | Atomic stock update or time-limited reservation | Negative or reserved-stock drift |
| Order lost between services | Durable queue with dead letter queue and replay | Queue age and dead letter depth |
| Cache serves stale price | Short cache lifetimes on price and stock, targeted invalidation | Price mismatch checks at checkout |
How does payment card regulation affect the technical design?
The Payment Card Industry Data Security Standard applies to systems that store, process or transmit cardholder data, and to systems that could affect the security of that data. The most effective technical decision is therefore to keep card data out of your environment altogether, using hosted payment fields or redirects provided by your payment service provider, so that your own systems never handle raw card numbers.
Which self-assessment questionnaire applies, and how much of your platform is in scope, depends on exactly how the payment page is integrated and is confirmed by your acquirer or a Qualified Security Assessor. We do not make that determination. What we do is implement the technical controls that support a smaller scope: network segmentation, least-privilege access, encryption in transit, hardened images, logging and change control.
Certification or attestation is issued by an independent assessor or by your own signed self-assessment, not by an engineering supplier. We provide the evidence-friendly technical foundations, such as infrastructure defined as code, reviewable change history and retained logs, which make the assessment easier to support.
How do observability and release practice protect revenue?
Revenue protection starts with measuring the journey customers actually take. Beyond host and pod metrics, we favour service level indicators for the steps that matter: search returns results, product pages load, add to basket succeeds, checkout completes and payment authorisation responds. These feed service level objectives and burn-rate alerts so that people are paged for customer impact rather than for noise.
Release practice matters just as much during busy periods. Progressive delivery, such as canary or blue-green releases with automated rollback on failed health checks, reduces the blast radius of a bad deploy. Feature flags let merchandising and engineering separate deployment from release, so a risky change can be switched off without a redeploy.
Load testing closes the loop. A test that reproduces realistic shopping behaviour, including cache misses, search and checkout, shows where the first bottleneck appears before a customer finds it. The results feed capacity plans and runbooks, and the same tests are repeated after significant architecture changes.
How would an engagement with us run?
We usually begin with a fixed-scope assessment of the current platform, agreed in writing, covering traffic paths, scaling behaviour, data stores, delivery pipeline and incident history. A named senior engineer leads the work and reviews the findings with your team, so you receive a prioritised plan rather than a generic report.
Implementation then happens in your own cloud accounts and repositories, using infrastructure as code and pull requests your engineers can review. Nothing is held in our accounts, so you keep full ownership and can continue without us. Timing is planned around your trading calendar, with change freezes respected before major sales.
After the build, some teams keep us on for managed operations, on-call support or periodic pre-peak reviews, while others take the runbooks and run it themselves. Either approach is reasonable, and the scope is agreed up front.
Frequently asked questions
Can you make our store handle a sale-day traffic spike?
Do you make us PCI DSS compliant?
Do you work with Shopify, Magento or custom platforms?
Where is the infrastructure we build kept?
Can you support us only around peak season?
Ready for your next peak season?
We can assess your infrastructure before your next peak event and fix the bottlenecks before they cost you revenue.