Connect cloud spend to useful work, then change the code and capacity driving unnecessary cost. The engagement connects investigation to implementation, with a baseline, reviewed changes and clear acceptance criteria.
What’s included.
Three steps.
Understand
Agree the task, baseline, source systems and delivery boundary.
Build
Develop and review the code, configuration and integrations in scope.
Verify
Test representative cases, compare with the baseline and hand over.
Recognise the challenge?
Start with the problem you see. Explore what we can change, then open the implementation notes for the detail.
01Data-warehouse workload cost reduction
What you may be seeing
- Our analytics bill grows even when sales do not.
- Dashboards repeatedly scan our whole history.
- The same calculations run in several reporting jobs.
- Data jobs take too long and delay morning reports.
- We cannot tell which reports are consuming the budget.
We find reports and data jobs that repeatedly do the same work, then change how the data is prepared and refreshed.
Implementation & verification
We attribute warehouse consumption to query families, dashboards and scheduled transformations. We rewrite SQL (the language used to query a database) to prune partitions, reduce unnecessary columns and joins, and process only new or changed records where correctness permits. We add reusable aggregates or materialized results and adjust job schedules and compute allocation to the workload. We validate that the optimized pipeline preserves reconciled totals and freshness while lowering cost per useful report.
How we verify the work
- Identify repeated full scans, join expansion and idle compute using execution and billing records.
- Match partition and clustering strategy to actual predicates; avoid incremental logic that misses late updates.
- Consolidate repeated transformations and right-size schedules against report freshness requirements.
- Compare billed bytes/credits, job duration and report freshness after correctness checks.
Tools considered for this work
Delivery: SQL (the language used to query a database)/data-pipeline development + warehouse configuration. BigQuery: job metadata, execution graph, partitioning, clustering and materialized views. Alternatives: Snowflake Query Profile/warehouse controls or Redshift EXPLAIN. Transformations: dbt incremental models and data tests. Reporting: existing BI platform; billing dashboards by job or workload.
02Application cloud-cost engineering
What you may be seeing
- Our hosting costs rise faster than customer activity.
- Each order triggers too many expensive service calls.
- Background jobs run repeatedly without producing extra value.
- We pay for large data transfers between services.
- We cannot explain what one customer transaction costs us.
We connect cloud spend to business activity, remove unused work and match capacity to actual demand.
Implementation & verification
We trace computing, API (a controlled connection between applications) and data-transfer costs to individual business actions rather than treating the cloud bill as one total. We refactor repeated processing, batch appropriate work and introduce cache or deduplication logic where the result can safely be reused. We change inefficient service boundaries, storage access and job schedules when those create unnecessary network or compute charges. We validate cost per completed action alongside latency, error rates and output quality so savings do not degrade delivery.
How we verify the work
- Join workload identifiers to usage and billing; isolate the most expensive repeated paths.
- Eliminate duplicate work before choosing cheaper infrastructure or purchasing commitments.
- Set cache invalidation and idempotency rules for changes touching purchases or account data.
- Normalize savings by successful transactions and traffic mix; include transferred and storage costs.
Tools considered for this work
Delivery: application development + cloud billing and tracing tools. Cost evidence: AWS Cost Explorer, Azure Cost Management or GCP Billing export. Request evidence: OpenTelemetry/APM traces and application job logs. Implementation: existing backend, Redis and queue/batch services as appropriate. Validation: workload replay and cost allocation queries.
03Container and cloud-resource rightsizing
What you may be seeing
- Our servers sit mostly idle, but the bill is high.
- Some containers run out of memory while others reserve too much.
- We pay for unused environments, disks or IP addresses.
- Our cluster grows but does not shrink after a campaign.
- We do not know which instances or resource limits are appropriate.
We check how much computing capacity each application needs and safely adjust its allocation, with enough headroom for busy periods.
Implementation & verification
We compare actual CPU, memory and workload peaks with reserved resources and cloud recommendations. We adjust container requests, limits and instance types, then configure scaling and nonproduction schedules around observed demand. We remove or retire confirmed unused resources through reviewed infrastructure changes and improve placement where fragmentation wastes capacity. We verify that lower spend does not introduce CPU throttling, out-of-memory failures or poor peak-period response times.
How we verify the work
- Use representative peak and quiet periods; distinguish reserved capacity from actual bottlenecks.
- Apply small resource reductions with headroom and rollback thresholds, avoiding conflicting autoscaling signals.
- Schedule idle environments and confirm ownership before retiring resources.
- Accept on lower workload cost with stable latency, throttling, restart and out-of-memory measures.
Tools considered for this work
Delivery: infrastructure-as-code development + cloud/resource configuration. Recommendations: AWS Compute Optimizer; native Azure/GCP recommendations as alternatives. Kubernetes: requests/limits, HPA and VPA recommendations where appropriate. Visibility: Prometheus/Grafana and cloud utilization/billing metrics. Changes: Terraform or the existing deployment manifests; k6 verification.
04Make cloud spending accountable
What you may be seeing
- Nobody owns some of our subscriptions or shared services.
- A discount offer looks attractive, but we do not know if our demand will stay steady.
We establish ownership, spending categories and review rules before changing commitments. You can see the cost of each workload and the reliability trade-offs behind a proposed saving.
Implementation & verification
We establish ownership, spending categories and review rules before changing commitments. You can see the cost of each workload and the reliability trade-offs behind a proposed saving.
How we verify the work
- Map subscriptions, shared resources and labels to accountable teams; agree how shared costs are allocated.
- Review steady demand before proposing a reserved-capacity or spend commitment; compare term, coverage and unused capacity risks.
- For Kubernetes, a platform for running containerised applications, compare application resource requests with actual demand before changing limits.
- Set budget and unusual-spend alerts, then document who investigates and approves a change.
Make unusual spending visible.
Automated checks flag unexpected workload costs. We can use forecasting to compare likely demand with capacity, then review the reliability and commitment risks before a change is approved.
Explore the technical toolkit
Selected around your existing stack, licences, permissions and agreed scope.
Define what better means.
Speed
Loading, interaction and slow-request response times.
Reliability
Successful transactions and recovery under realistic load.
Unit cost
Spend per completed action, with traffic and quality considered.
Targets follow the baseline. These are measurement areas, not promised results.
Engineering terms, explained
- Grounded AI / RAG
- An assistant retrieves relevant approved evidence before generating an answer. Access controls and evaluations still need to be engineered and tested.
- Idempotency
- Retrying the same operation does not create an extra order, payment or record.
- Controlled experiment
- A concurrent comparison of assigned variants, with a defined outcome, observation window and decision rule.
- Core Web Vitals
- Measures of main-content loading, responsiveness to interaction and visual stability.
Technical references: Web Vitals · Azure document access · Structured data guidelines.
Quick answers.
Is this more than a website speed audit?
Yes. We work across the frontend, backend, database, cloud and AI workload. The diagnosis determines which layers need development; the engagement can include implementation and ongoing monitoring.
Can you improve our Azure environment?
We can assess Azure application performance, workload costs, Functions, database usage and monitoring. We agree the relevant services, access and implementation boundaries before work starts.
Will you guarantee a savings percentage?
No. We establish your baseline first and present evidence-backed opportunities. Any saving is checked against reliability, customer experience and output quality.