Cloud Architecture & DevOps Services
We set up and run the infrastructure underneath the applications we build — and, often, underneath applications somebody else built.
What is cloud and DevOps engineering?
Cloud and DevOps work covers the infrastructure an application runs on and the automation that gets code there safely: hosting, databases, caching, CI/CD pipelines, monitoring, and cost control. Techroniqs works primarily on AWS and Vercel, with Cloudflare at the edge and Redis for caching. Platforms we have built and operated on this stack include Stax.fun, an EdTech product that has sustained 99.9% uptime while serving more than 2,000 monthly active learners.
Key takeaways
- Uptime is an architecture outcome, not a hosting plan. Where the single points of failure are matters more than which provider you pick.
- Vercel and AWS are not competitors in most builds — Vercel for the application tier, AWS for storage, mail, and long-running jobs, is a common and sensible split.
- Caching is the cheapest performance work available. Redis in front of an expensive query is usually a one-day change with a permanent payoff.
- A deploy pipeline that anyone on the team can run without ceremony is worth more than any individual optimisation.
- Cloud cost is a design decision. Most large bills trace to one or two architectural choices, not to general waste.
Who this is for
- Teams whose deploys are manual, risky, or known to only one person.
- Products that have outgrown their original hosting and are now hitting latency or cost ceilings.
- Companies that need monitoring and alerting in place before, rather than after, their first outage.
What you get
Every engagement produces these. Anything else is scoped explicitly rather than assumed.
- Infrastructure setup
- Hosting, database, storage, and networking configured and documented.
- CI/CD pipeline
- Automated tests, per-branch preview environments, and repeatable production deploys.
- Caching layer
- Redis or edge caching placed where measurement says it will actually help.
- Monitoring and alerting
- Uptime checks, error tracking, and alerts that reach a human.
- Cost review
- Where the spend goes and which architectural changes would reduce it.
- Runbook
- Written procedures for deploys, rollbacks, and common failures.
Technology stack
Platforms
- AWS
- Vercel
- Cloudflare
AWS services
- S3
- SES
- EC2
Data
- PostgreSQL
- Redis
- Supabase
Pipeline
- GitHub Actions
- Vercel deployments
- Preview environments
Vercel, AWS, or both
The usual question is framed as a choice. In practice most of the platforms we run use both, with each handling what it is good at.
| Option | Strongest for | Watch out for | Used on |
|---|---|---|---|
| Vercel | Next.js apps, preview environments, global edge delivery | Long-running jobs and heavy background processing | BankBarn, CropGuard, Greenfinder |
| AWS | Storage, email, queues, long-running compute, mature services | More configuration and operational ownership | PredictEV, Cresta.ai, Adaptive Insurance |
| Both | Application tier on Vercel, storage and jobs on AWS | Two bills and two access models to manage | Common default for new builds |
| Cloudflare at the edge | Caching, DNS, and traffic protection in front of either | Another layer to reason about when debugging | Stax.fun |
How we build it
- Automate the deploy before optimising anything else. Manual deploys are the root cause of most avoidable production incidents.
- Give every branch a preview environment so review happens against something real.
- Measure before caching. Adding Redis to a query that was never the bottleneck adds a moving part for nothing.
- Alert on symptoms users feel — error rates and latency — rather than on every infrastructure metric.
- Keep infrastructure configuration in the repository so it can be reviewed and rolled back like any other change.
- Write the rollback procedure before you need it.
Common mistakes
Deploys that only one person can run
Releases stall whenever that person is unavailable, and nobody else can respond to an incident.
Instead: Automate the pipeline and document the runbook.
No monitoring until the first outage
You find out about downtime from a customer.
Instead: Uptime checks and error tracking on day one — they take an afternoon.
Caching everything by default
Stale data bugs that are very hard to reproduce.
Instead: Cache specific, measured, expensive reads with a deliberate invalidation rule.
Treating cloud cost as a finance problem
Spending is trimmed at the edges while the architectural cause remains.
Instead: Trace the bill to the design decision that produced it.
Typical timeline
Ranges reflect engagements we have delivered. Scope, integration count, and decision speed on your side move these more than anything else.
1. Audit
3–5 daysCurrent architecture, single points of failure, and a prioritised list.
2. Pipeline and environments
1–2 weeksAutomated deploys and preview environments running.
3. Reliability work
2–4 weeksMonitoring, caching, and the fixes the audit identified.
4. Operate
OngoingMonthly retainer covering monitoring, updates, and incident response.
Where we have done this
Stax.fun
EdTechPolyLabs
AI-powered, block-based coding platform making programming accessible and fun for kids — inspired by Scratch.
Read the case studyPredictEV
CleanTechVolta Charging — Shell Petroleum
EV infrastructure planning and monitoring platform for Volta Charging — a Shell Petroleum brand. Visualizes ML-predicted EV charging demand across US regions.
Used by Shell & Volta planning teams
Read the case studyAdaptive Insurance
InsurTechPower outage insurance platform automating policy management, claims processing, and third-party verifications for US homeowners.
Read the case studyMosaic
FinTechBlockchain API Platform
Enterprise-grade API platform aggregating cryptocurrency market data from 50+ exchanges for financial institutions.
Read the case studyWhen this is the wrong choice
Stated plainly, because finding this out in month three costs you far more than reading it now.
- We are not a managed hosting provider and do not offer a 24/7 on-call rota.
- We work on AWS and Vercel. Deep Azure or Google Cloud migrations are outside our day-to-day stack.
- Kubernetes-based platform engineering at large scale is not our specialism; we favour managed services for the size of team we typically work with.
What to consider instead
- Managed platform (Render, Railway, Fly.io)
- A small team wants to avoid infrastructure ownership entirely.
- In-house DevOps hire
- You are large enough to need continuous, dedicated infrastructure attention.
- Techroniqs retainer
- You want the pipeline built properly once, then maintained on a monthly basis.
Frequently asked questions
Should we host on Vercel or AWS?
For most Next.js applications, both. Vercel handles the application tier, preview environments, and edge delivery well; AWS handles storage, email, queues, and long-running jobs that Vercel is not designed for. Our engagements have used Vercel alone, AWS alone, and the two together, chosen by what the workload actually needs.
How do you improve uptime?
By finding the single points of failure first, then removing them in order of risk. Typically that means automated deploys with a tested rollback, a caching layer in front of the expensive reads, monitoring that alerts a human, and a documented runbook. Stax.fun has sustained 99.9% uptime on this approach.
Can you reduce our cloud bill?
Often, yes, but the honest answer is that large bills usually trace to one or two architectural decisions rather than general waste. We audit where spend actually goes and tell you which changes would move it, including when the answer is that your costs are reasonable for the workload.
Do you offer 24/7 on-call support?
No. We provide monitoring, alerting, documented runbooks, and incident response on a monthly retainer during working hours. If you need a round-the-clock rota, you need either an in-house team or a dedicated managed-service provider, and we will say so.
Do you work with Azure or Google Cloud?
Our production experience is on AWS, Vercel, and Cloudflare. We can work alongside Azure or Google Cloud services, but a large migration onto either sits outside the stack we use day to day.
What is a preview environment and why does it matter?
It is a complete, running copy of the application deployed automatically for every branch, at its own URL. It matters because it lets reviewers test real behaviour instead of reading a diff or trusting a screenshot, which catches a class of bugs that code review never will.
Related
Talk to an engineer about cloud & devops
A 30-minute call with someone who will be writing the code. We will tell you if we are the wrong fit, and what we would do instead.