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CLDcloud application development

Cloud application development you can feel in page speed.

Cloud application development decides more than uptime. Region choice sets your time to first byte, edge caching sets how fast the main content paints, and every server error makes Googlebot crawl less. We design cloud-native apps on AWS, Azure or Google Cloud around performance budgets, reliability targets and a cost ceiling agreed before the build.

Design my cloud app → Cloud → Core Web Vitals
  • aws · azure · google cloud
  • slos before code
  • cost ceiling agreed upfront
multi-region.mapillustration
  • edge HTML caching close to users
  • services in two regions with autoscaling
  • cross-region replication, traces and SLO alerts
terraform per regionslo dashboardsfinops reviews
definition

What is cloud application development?

Cloud application development is designing software to run on managed cloud services instead of fixed servers. That means containers or serverless functions for compute, managed databases and queues for state, infrastructure as code for every environment, and automated pipelines for releases. Cloud-native apps scale with demand and recover from failures on their own. The architecture also decides what users feel: how quickly pages respond, how they hold up at peak, and what each request costs to serve.

Providers
AWS, Azure, Google Cloud, plus edge platforms like Cloudflare and Vercel
Compute
Containers, serverless or, most often, a mix of both
Reliability
Service-level objectives, tested restores and rehearsed failover
Spend
A cost ceiling set before the build, reviewed after launch
sound familiar?

When the infrastructure becomes the bottleneck.

Tick what applies. Your answers are saved to the brief you send us.

Tap anything that sounds familiar.

what's included

Cloud application development services, built to be run.

You get a system your team can operate on a Tuesday afternoon, not just one that demos well.

01

Architecture & cost model

Compute, data, network and disaster-recovery design, with an estimate of what it costs to run at your expected load.

02

Infrastructure as code

Every environment defined in Terraform, OpenTofu or Pulumi, so a new region or a rebuild is a pull request.

03

Containers or serverless

Services built for the workload: serverless for spiky, event-driven work; containers for steady, long-running ones.

04

Pipelines & previews

CI/CD with automated tests and a preview environment for every change, so reviews happen on running software.

05

Edge delivery & CWV report

CDN and edge rendering configured for your pages, plus a Core Web Vitals report from lab tests and real-user data.

06

Observability, security & DR

Metrics, traces, logs and SLO dashboards, least-privilege access, dependency scanning, and a restore you've actually tested.

cloud → core web vitals

Your cloud architecture is visible in your Core Web Vitals.

Each architecture decision lands on a metric Google measures. Here's how we map them.

core web vitals: hoped for
TTFBOne distant region and cold starts on page-serving functionsRegions near your users and warm paths for anything that renders pages
LCPEvery page rendered at the origin on every requestHTML cached at the edge with stale-while-revalidate, and images from an image CDN
INPLarge client bundles that block every tap and clickServer-rendered pages with lean, split JavaScript
CLSContent injected after load, pushing the layout aroundServer-rendered layout with space reserved for media
Crawl rate5xx spikes at peak, which Google answers by crawling lessAutoscaling plus SLO alerts that fire before error rates climb
Tenant pagesThin, user-generated SaaS pages bloating the indexIndexing rules that keep only valuable public pages crawlable

Targets we build to: Google's "good" thresholds at the 75th percentile, which are LCP ≤ 2.5s, INP ≤ 200ms and CLS ≤ 0.1 (web.dev). Google also reduces crawling when servers slow down or return errors.

how it works

Targets first, then technology.

We agree the numbers the system must hit before choosing a single service.

  1. 01

    Set targets

    Service-level objectives, performance budgets per page type and a monthly cost ceiling, all agreed with you.

    → targets document
  2. 02

    Architect

    Provider and services, portability where it pays for itself, and a running-cost estimate at expected and peak load.

    → architecture + estimate
  3. 03

    Build

    Infrastructure code, pipelines and services built together, with preview environments from the first week.

    → running staging stack
  4. 04

    Validate

    Load and failover tests, security scans, and Core Web Vitals checked in the lab and against real users.

    → test + CWV report
  5. 05

    Run & optimise

    Cost reviews, scaling adjustments and upgrades, steered by the SLO dashboards rather than by gut feel.

    → scheduled reviews
stack

Any major cloud. Portable where it pays.

// aws
LambdaECS / FargateEKSRDS / AuroraDynamoDBCloudFront
// azure & google cloud
App ServiceContainer AppsFront DoorCloud RunGKECloud SQL
// edge
Cloudflare WorkersCloudflare R2VercelNetlifyImage CDN
// platform
DockerKubernetesTerraform / OpenTofuPulumiPostgreSQLRedisKafkaOpenTelemetryGrafana
what drives the cost

What does a cloud application cost to build and run?

Traffic shape, tenancy, availability and your starting point move both numbers. Set yours.

Traffic pattern

Spiky and global traffic needs autoscaling and edge caching to be designed in.

Tenancy

Multi-tenant SaaS adds isolation, billing hooks and per-tenant limits.

Availability target

Each step up adds infrastructure, replication and failover testing.

Starting point

Breaking up a monolith is a different project from building fresh.

keep exploring

Related services.

AZR

Building on Azure

App Service, Functions and Entra ID for Microsoft-first teams.

Explore → MOD

Move a legacy app

Stage a monolith's move to the cloud without a big-bang rewrite.

Explore → API

The API layer

Contract-first APIs with gateways, quotas and observability.

Explore → APP

Custom SaaS apps

The product itself, with a public side built to be found.

Explore →
faq

Cloud development questions.

Cloud-hosted means moving an existing app onto rented virtual machines. Cloud-native apps are designed around managed services, autoscaling, containers or serverless functions and automated deployment, so they scale and recover without manual work.
Decide on your team's existing skills, your identity platform and contracts, how well each provider's services fit, and any data-residency rules. Where portability matters, containers, infrastructure as code and open standards keep your options open.
Build cost depends on scope, architectural complexity, the number of environments and compliance. Running cost depends on compute and data volume, data egress, availability targets (multi-region costs more) and ongoing operations.
Tagging, budgets and alerts from day one, rightsizing, autoscale caps, commitment discounts for steady load, designs that avoid unnecessary egress, and regular cost reviews against the ceiling we agreed.
Yes. It drives time to first byte and largest contentful paint, and uptime matters because Google reduces crawling when servers slow down or return errors. Server-rendered pages also reach AI crawlers that don't run JavaScript.
Serverless suits spiky, event-driven workloads. Containers suit steady, long-running or portability-sensitive services. Most production systems use both, and we choose per service rather than per project.
By being deliberate about it. Containers, infrastructure as code and open protocols keep the core portable, and we accept a provider's managed services where they save real effort. You'll know which parts are portable and which aren't before we build.
start in the cloud

Design your cloud app around the numbers.

Send your site or product and tell us where it hurts: peaks, cost, speed or releases. We'll reply with targets and a first architecture sketch.

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quote within 24 hours · slos and budgets agreed upfront · your cloud accounts