Services
High-load, Cloud & DevOps
Back ends that hold at peak: queues, caching, sharding, event streams, Kubernetes on AWS/GCP/Azure, observability and load testing.
When do you need this?
You need high-load engineering when growth starts breaking things: traffic spikes take the site down, the database has become the bottleneck, background jobs run for hours, the monolith cannot be deployed without a maintenance window, and cloud bills grow faster than revenue. We fix these with architecture, not with bigger servers. The cost of waiting is real: in the Uptime Institute survey, over half of respondents said their most recent significant outage cost more than USD 100,000, and about one in six said over USD 1 million (Uptime Institute, 2024).
What do we do?
We do five things: architecture, performance, cloud and Kubernetes, observability and reliability — always with a target in numbers (p95 latency, error rate, cost per month). Cost is a first-class target because waste is normal: Flexera reports that organisations estimate roughly a quarter to a third of their cloud spend is wasted (Flexera, 2024).
- Architecture: queues (RabbitMQ, Kafka, SQS), caching (Redis), read replicas, sharding, CQRS/event sourcing where justified.
- Performance: profiling, query optimisation, connection pooling, async I/O, load testing with k6/Locust and a target in numbers.
- Cloud and Kubernetes: AWS, GCP, Azure; Terraform; autoscaling; blue/green and canary deploys; cost optimisation.
- Observability: metrics, logs, traces (Prometheus, Grafana, OpenTelemetry), alerting and on-call runbooks.
- Reliability: backups and restore drills, multi-AZ, disaster-recovery plans, security hardening.
Vertical vs horizontal scaling: which comes first?
Vertical scaling (a bigger machine) is the right first move for a database or a single hot service because it needs no code change; horizontal scaling (more instances behind a balancer) is what keeps you up at peak and through failures, but it requires stateless services, shared caches and queues. The AWS Well-Architected reliability guidance says the same: scale horizontally to increase aggregate availability and design so that one failure does not take everything down (AWS, 2024); on Kubernetes the Horizontal Pod Autoscaler does this automatically once services are stateless (Kubernetes docs, 2025).
| Criterion | Vertical (scale up) | Horizontal (scale out) |
|---|---|---|
| How | Bigger CPU, RAM, disk on one node | More instances behind a load balancer |
| Code changes | None | Stateless services, external sessions and cache |
| Ceiling | Hard limit of the largest machine | Practically unbounded |
| Fault tolerance | Single point of failure | Survives loss of instances or a zone |
| Cost curve | Steep at the top end | Linear; can scale down at night |
| Typical use | Databases, quick relief | Web and API tiers, workers, autoscaling |
Which stack do we use?
The stack is chosen for the hot path: Python (asyncio, FastAPI), PHP (Laravel Octane, Swoole), Node.js, Go where it matters; PostgreSQL, MySQL, ClickHouse, Redis, Elasticsearch; Kafka/RabbitMQ; Docker, Kubernetes, Terraform, GitHub Actions/GitLab CI. Kubernetes is the default orchestrator because it is now the norm: the CNCF annual survey found two thirds of respondents running Kubernetes in production (CNCF, 2023). We also work with on-premise and hybrid setups when data residency requires it.
How do we engage?
We start with an audit that ends in a written report and a prioritised plan, then continue either as a fixed-scope project or a dedicated team, and stay on for operations and on-call support under an SLA. Success is measured the way the DORA research programme measures it — deployment frequency, lead time for changes, change failure rate and time to restore service (DORA, 2024) — plus p95 latency under target load and monthly cloud cost. Dashboards for these are part of the deliverable, so you keep seeing them after we leave.
Frequently asked questions
Case studies
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