By Bartosz K. — Published: 22 December 2025 — Updated: 5 January 2026 — 9 min read
Infrastructure spending is often the largest line item in a technology budget, and it is frequently more wasteful than engineering teams realise. Industry research consistently finds that a substantial proportion of cloud spend is on over-provisioned or entirely unused resources. At the same time, chasing cost reduction without a clear methodology risks creating reliability problems and technical debt that cost far more to unpick than the savings ever justified. This article presents a structured approach to IT infrastructure optimisation — one that reduces costs while actually improving system reliability and operational visibility.
When organisations first migrate to the cloud, they typically provision resources based on worst-case estimates. Servers are sized for peak load; databases are provisioned for anticipated growth; unused development environments are left running around the clock. This conservatism makes sense during migration but becomes costly as systems stabilise and teams fail to re-evaluate their allocations.
Common sources of cloud waste include:
Taken together, these inefficiencies often represent 30–40% of total cloud spend. The opportunity is significant, and most of it can be captured without any reduction in performance or reliability.
Rightsizing is the process of adjusting resource allocations to match observed usage rather than theoretical peaks. For virtual machines, this means analysing CPU, memory, and network utilisation over a representative period — typically a month — and selecting instance types that are appropriately sized for actual demand, not theoretical maximums.
All major cloud providers offer tooling that identifies rightsizing opportunities: AWS Compute Optimizer, Azure Advisor, Google Cloud Recommender. These tools analyse utilisation metrics and suggest smaller or more cost-efficient instance types. The recommendations are often conservative; in practice, teams that implement them alongside autoscaling typically find they can go further without any reliability impact.
Fixed-size infrastructure is inherently wasteful for workloads with variable demand. An application that serves ten times as much traffic during business hours as overnight should not maintain the same infrastructure size around the clock. Autoscaling — automatically adding and removing compute capacity in response to demand — allows infrastructure costs to track actual usage far more closely.
Horizontal autoscaling (adding more instances) is well-supported across all major cloud platforms and suitable for stateless application tiers. Vertical autoscaling (using larger or smaller instances) is appropriate for some database workloads. For highly variable or intermittent workloads, serverless computing (AWS Lambda, Azure Functions, Google Cloud Run) takes this further — you pay only for actual compute time used, with no idle cost.
Many infrastructure costs come from environments that do not need to run continuously. Development, testing, and staging environments typically need to be available during business hours but can be safely shut down in the evenings and on weekends. Implementing scheduled start/stop automation for non-production environments is one of the fastest and highest-impact cost reduction measures available, often delivering 50–70% savings on those environment costs with minimal operational overhead.
Data storage costs accumulate quietly and compound over time. Key optimisation strategies include:
Tiered storage: Modern cloud storage services offer multiple tiers at different price points based on access frequency. Data accessed daily belongs in standard storage; data accessed monthly can move to infrequent-access storage at significantly lower cost; archived data rarely accessed can move to archival storage at a fraction of standard pricing. Lifecycle policies automate this tiering based on data age and access patterns.
Data compression and deduplication: For large datasets, applying compression can reduce storage requirements substantially. For backup and archive workloads, deduplication eliminates redundant copies of data that would otherwise be stored multiple times.
Database right-sizing: Managed database services are often significantly over-provisioned. Review storage allocations and compute tiers for all managed database instances, and consider whether read-heavy workloads can be served from read replicas on smaller instance types.
You cannot optimise what you cannot see. Comprehensive observability — metrics, logs, and traces across all infrastructure components — is a prerequisite for effective cost management. Without visibility into actual resource utilisation, teams are forced to provision conservatively to avoid problems they cannot detect until they occur.
A well-instrumented infrastructure enables engineering teams to identify which services are consuming disproportionate resources, which database queries are running inefficiently, and where bottlenecks are creating performance problems that additional compute is compensating for rather than solving. Often, the highest-value infrastructure optimisation is not a smaller instance type but a better-indexed database query.
Some infrastructure cost problems cannot be solved by configuration changes alone — they require architectural evolution. Monolithic applications running on large virtual machines may be more cost-efficient when refactored into services that can be deployed on managed container platforms and scaled independently. Synchronous, tightly coupled architectures may benefit from event-driven patterns that decouple producers and consumers, reducing the over-provisioning needed to absorb traffic spikes.
Architecture modernisation carries higher upfront effort than operational optimisations, but delivers larger long-term savings and often improves reliability and developer productivity as a side effect.
Infrastructure optimisation is not a one-time project — it is an ongoing practice. Cloud environments change as teams add services, data volumes grow, and usage patterns evolve. Embedding cost visibility into engineering culture — through tagging and cost allocation, regular infrastructure reviews, and clear ownership of cloud spend by engineering teams — ensures that inefficiencies are caught early rather than accumulating over years.
At BKI, we help organisations design and operate cloud infrastructure that is both performant and cost-efficient. If you are looking to reduce your infrastructure spend without compromising reliability, let's talk.