Infrastructure engagements start with measurement: query logs, resource utilization, deploy frequency, and the cost report. Fixes are sequenced by impact, so the handful of queries behind most of the latency and the manual deploy steps behind most of the risk go first. Everything is left as code, documented, and rehearsed, so your team can run it without us.
Service 04 of 04
Database Optimization, Cloud Infrastructure and DevOps
Infrastructure problems rarely announce themselves. They show up as a report that takes a minute to load, a deploy that everyone avoids on Fridays, or a cloud bill that doubled without a matching increase in customers. Each has a specific, measurable cause, and each is cheaper to fix than to tolerate.
We work at three layers. At the database we profile real query logs and fix the handful of queries, indexes, and schema decisions responsible for most of the latency. At the infrastructure layer we right-size compute on Docker, Linux VPS, or AWS and put it under infrastructure as code. At the delivery layer we build CI/CD pipelines that test, build, and ship on every commit with a rollback that always works.
PostgreSQLRedisDockerKubernetesAWSLinuxTerraformGitHub ActionsOpenTelemetryGrafana
Who this is for
- Teams with a database that has become the bottleneck and nobody wants to touch
- Companies on a VPS or early cloud setup that needs to become reproducible and recoverable
- Engineering teams that deploy manually and want a pipeline they can trust
What you receive
- Slow-query report with execution plans, index changes, and measured before-and-after latency
- Infrastructure as code for every environment, with least-privilege IAM
- Container platform on Docker Compose or Kubernetes, sized to the workload
- CI/CD pipeline with tests, scans, gated promotion, and automatic rollback
- Backups with rehearsed restores and documented recovery objectives
- Monitoring, alerting, and runbooks for the incidents that matter
Architecture pattern
How the system is put together.
The components we ship, in the order a request moves through them. Every boundary is typed, every failure path is designed, and every action is logged.
- 1
Database performance
Slow-query profiling, execution plan analysis, index design, connection pooling, partitioning for large tables, and read replicas for reporting workloads.
- 2
Infrastructure as code
Every environment described in code and reproducible from scratch, with separate staging and production, secrets in a managed vault, and least-privilege IAM.
- 3
Container platform
Docker images built once and promoted through environments, orchestrated with Compose on a Linux VPS or Kubernetes on AWS depending on scale and team.
- 4
CI/CD pipelines
GitHub Actions pipelines that lint, test, build, scan, and deploy, with environment gates, canary or blue-green strategies, and automatic rollback on failed health checks.
- 5
Backups and recovery
Automated backups with tested restores, defined recovery point and recovery time objectives, and runbooks for the incidents that matter.
Failure prevention
Tested restores
A backup that has never been restored is a hope. Restores are rehearsed on a schedule and timed against the recovery objective.
Health-checked rollouts
Deploys promote only when health checks pass, and roll back automatically when they do not.
Capacity alerts
Disk, connections, queue depth, and error rates alert before they become outages, with runbooks attached to every alert.
Cost visibility
Spend tagged by service and environment, reviewed monthly, with idle resources reclaimed automatically.
Targets we design to
<1h
recovery time objective with rehearsed restores
<15min
commit to production through the delivery pipeline
100%
of infrastructure reproducible from code
Questions about this service
It depends on scale and team. A single VPS with Docker Compose, backups, and a deploy pipeline serves many B2B products for years. Kubernetes earns its complexity when you need autoscaling across many services or multiple regions. We recommend the smallest platform that meets the requirement.
Indexes are built concurrently, schema changes are reversible and deployed ahead of code, and large migrations run in batches against a replica first. Every change is measured against the query logs that motivated it.
Usually, because right-sizing, caching, and reclaiming idle resources are part of the work, and spend is tagged by service so it stays visible. The exact figure depends on the starting point, which the audit measures first.
In production
Case studies built on this discipline.
Ready to scope database optimization, cloud infrastructure for your team?
Book an architecture call. We will map the workflow, the systems it touches, and the number this discipline should move for you.
- No-obligation call
- Architecture first, build second
- You own all the code