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Platform & Infrastructure

Cloud & DevOps Services Deploy on a Friday

Our cloud and DevOps services give you infrastructure you can deploy to on a Friday. We set up CI/CD pipelines, infrastructure as code, staging environments, automated backups, monitoring, and alerting on AWS, Vercel, or your own servers — with cost controls that catch a runaway workload early.

  • Rollback is one command
  • Restores are tested, not assumed
  • Budget alerts, including AI spend
Review Your InfrastructureAll Services

How it runs · 5 steps

  1. Audit what exists
  2. Codify the infrastructure
  3. Automate the pipeline
  4. Instrument and alert
  5. Test the restore

Built with

  • AWS
  • Vercel
  • Cloudflare
  • Self-hosted
  • Terraform
  • Pulumi
  • Docker
  • GitHub Actions

More in Platform & Infrastructure

Questions first?Talk to an engineer

The measure of a deployment setup is how confidently you can undo things

We aim for a pipeline where a revert is a routine action rather than an incident, and where restoring from backup has actually been tested rather than assumed. Those two properties are what let a team ship on a Friday afternoon without a private sense of dread.

For AI workloads there is a third: spend. Token costs scale with success in ways a pilot never reveals, so per-model budget caps and alerting are part of the infrastructure rather than an afterthought once the first large invoice arrives.

The Problem

What this usually fixes

  • Deploys that need a person and a runbook

    Shipping requires someone who knows the sequence, which means fixes wait for availability and nobody deploys before a weekend.

  • Infrastructure that exists only in a console

    Nobody can reproduce the environment, changes are unreviewable, and the configuration lives in one person's memory.

  • Backups nobody has restored

    The backup job reports success every night. Whether the data can actually be restored is an open question until the day it matters.

  • Cloud and AI bills that surprise

    Costs are discovered monthly in arrears, with no per-service attribution and no cap on a workload that has started looping.

How We Work

The process

Each step produces something you can review — a document, an environment, or working software — rather than a percentage in a status report.

  1. Audit what exists

    Current environments, deployment path, data flows, and the single points of failure — written down and ranked by risk.

  2. Codify the infrastructure

    Terraform or Pulumi definitions under version control, so environments are reproducible and a production change is a reviewable diff.

  3. Automate the pipeline

    Test, build, and deploy on merge, with a preview environment per pull request and rollback as one command rather than a rebuild.

  4. Instrument and alert

    Uptime, error rate, latency, and log aggregation, with alerts routed to people rather than to a dashboard nobody opens.

  5. Test the restore

    Backups verified by actually restoring them into a scratch environment, and the procedure documented so anyone can run it.

What You Get

Why teams choose this

  • Shipping stops being an event

    Automated pipelines and one-command rollback turn a deploy into a routine action rather than a scheduled ceremony.

  • Environments you can reproduce

    Infrastructure as code means staging genuinely matches production, and a new environment is a command rather than a week.

  • Recovery you have rehearsed

    A restore that has been performed is a plan. A backup that has never been restored is a hope with a cron schedule.

  • Costs visible before the invoice

    Per-service attribution and budget alerts — including per-model caps for AI workloads — so overspend is caught in hours, not weeks.

Stack

Technologies we build with

Tools we have delivered production work on, not a capability matrix. We pick per project and will explain the trade-off behind each choice.

Cloud
  • AWS
  • Vercel
  • Cloudflare
  • Self-hosted
Infrastructure
  • Terraform
  • Pulumi
  • Docker
Pipelines
  • GitHub Actions
  • Preview environments
Observability
  • Sentry
  • Uptime monitoring
  • Log aggregation
Industries

Who this is for

Relevant to any team shipping software regularly — and urgent for any team that currently cannot.

  • B2B software
  • Fintech
  • E-commerce & retail
  • Healthcare
  • Logistics
  • Marketplaces
  • Education
  • Professional services
Questions

Cloud & DevOps: common questions

Related reading

What do cloud and DevOps services actually include?

A typical engagement covers five things: automated CI/CD so tests, builds, and deploys run on merge; infrastructure as code so environments are reproducible and reviewable; monitoring and alerting for uptime, errors, and latency; backup and tested restore procedures; and cost visibility with budget alerts. For teams running AI workloads it also covers per-model spend caps and token-cost attribution, which conventional cloud monitoring does not surface.

Should we use AWS, Vercel, or our own servers?

Vercel suits front-end-heavy products where deployment simplicity and edge performance matter more than infrastructure control, and it is the fastest path for most Next.js and Astro applications. AWS suits systems with substantial backend, data, or compliance requirements, or where cost at scale justifies the operational overhead. Self-hosting makes sense under strict data-residency obligations or at a scale where managed pricing stops being competitive. Most products we build run a hybrid of the first two.

Can you help reduce our cloud costs?

Yes, and the first step is attribution rather than optimisation — most teams cannot say which service or feature drives their bill, which makes every cost decision guesswork. Once spend is attributed, the recurring wins are consistent: over-provisioned instances, storage nobody deleted, logs retained far longer than anyone needs, and staging environments running around the clock. For AI workloads, caching and model selection per task usually matter more than infrastructure tuning.

How do you handle deployments without downtime?

Rolling or blue-green deployment so a new version takes traffic only once it is healthy, with the previous version still running and ready to take it back. Database migrations are the harder half: they are written to be backwards-compatible so old and new code can run simultaneously during the transition, which is what makes an instant rollback possible. A rollback that requires a database restore is not a rollback — it is an incident.
Sectors

Where we apply this

The same work looks different depending on the constraints it has to hold. These cover what changes.

Related

Services that pair with this

Would you deploy on a Friday afternoon?

If the honest answer is no, that is the problem worth fixing first. We will audit what you have and rank the risks.

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