As organisations adopt hybrid and multi-cloud strategies to meet growing demands for flexibility, redundancy, and geographic reach, managing cost without compromising on performance has become a balancing act. It’s not just about choosing the cheapest provider—it’s about understanding usage patterns, aligning spend to workloads, and designing systems that scale without silently bleeding money.

The complexity grows when different teams provision resources independently across clouds like AWS, Google Cloud, Azure, and Huawei. Without proper governance and architectural foresight, cost optimisation becomes reactive and chaotic. In this piece, we’ll explore proven strategies for reducing hybrid/multi-cloud spend while keeping performance and developer experience intact.

Visibility First – Know What You’re Spending & Why

Before you can optimise, you need clarity. Visibility into application performance and infrastructure utilisation is essential. Deploy centralised observability tools—such as Grafana to monitor system health, identify services with high traffic or resource usage, and measure the four golden signals (latency, traffic, errors, saturation). Observability tools offer a unified view into usage trends, bottlenecks, and underutilised assets across clouds. They also help us ensure that optimisation efforts don't lead to SLA breaches. 

Equally crucial is cost visibility. Integrate cloud-native billing tools (AWS Cost Explorer, GCP Billing Export, Azure Cost Management) with open-source FinOps solutions like Opencost  and Infracost. Centralising this data into a FinOps dashboard gives real-time insights to detect anomalies, identify idle resources, and forecast future spend, empowering proactive, data-driven decision-making.

Define Clear SLAs/SLOs as Guardrails

Clearly defined Service Level Objectives (SLOs) and Service Level Agreements (SLAs) serve as guardrails for optimisation. They quantify acceptable performance and reliability metrics, allowing teams to confidently optimise resources without impacting customer experience.  Google's free SRE books provide best practices on setting granular SLOs even for internal services.

For instance, an e-commerce site might maintain strict SLOs for its checkout service while being more lenient with non-critical recommendation engines. These metrics clarify how much optimisation headroom exists before performance is impacted, guiding strategic cost-cutting efforts.

Rightsize, Don’t Just Resize

One of the most common cost pitfalls is overprovisioning. Teams often err on the side of caution—provisioning more CPU, memory, or storage than needed “just in case.” In hybrid and multi cloud environments, this is compounded by the fact that pricing models differ wildly between providers. 

Analyse historical usage patterns (leveraging observability tools) to match instance types with actual demand rather than peak loads. Utilise autoscaling features available in Kubernetes environments (GKE, EKS, AKS) and leverage serverless offerings (AWS Lambda, Google Cloud Functions, Azure Functions) for bursty workloads.

Monitor saturation closely and aim for balanced CPU and memory utilisation for your workloads. Regularly review resource allocations, keeping SLOs as guardrails to ensure performance stays within acceptable limits.

Prefer Open Standards & Cloud-Agnostic Tools

A large portion of cloud waste stems from cloud-specific implementations that are costly to move or re-architect. While managed cloud services can save engineering effort, they come at a premium. Evaluate each managed service's cost versus operational convenience. Critical databases may justify managed solutions, whereas smaller caches (e.g., Redis, Memcached) often perform equally well with mature, Kubernetes-based solutions. 

Using open source tools like CloudNativePG for PostgreSQL, a Kubernetes operator that helps provision and manage HA database clusters can provide flexibility and prevent vendor lock-in. Similarly, deploying your own observability stack with Prometheus and Grafana will give your more flexibly across cloud and help you avoid proprietary monitoring services.  Tools like these ensure your systems remain portable, reducing long-term lock-in and making cost-based migration viable.

Strategic Workload Placement

Not all clouds are created equal. Depending on geography, workload type, or service-level guarantees, one cloud provider might offer better cost-to-performance than another.

Some patterns to consider:

• Run latency-sensitive services on clouds with POPs or data centres closer to end users—Huawei, for instance, has strong regional coverage in Africa and Asia.
• Use cost-efficient providers for batch or non-critical workloads, keeping premium vendors reserved for business-critical functions.
• Leverage hybrid architectures—run persistent, secure systems in AWS or Azure, and burst compute to Google Cloud for data processing or ML tasks.

With multi-cloud comes the opportunity to match workload intent to platform strengths - unlocking both savings and better user experience.

Consider Network Egress Costs in Architecture Design

Cross-cloud data transfer costs (network egress) are often overlooked but quickly escalate. Even modest workloads that shuttle data between clouds can incur significant egress charges if not carefully designed.

To mitigate:

• Keep data-intensive services co-located where possible—run compute close to your data.
• Design APIs for efficiency—minimise chattiness and payload sizes between services.
• Use edge services like Cloudflare or ZScaler to cache content and offload traffic where appropriate.
• Avoid backhauling traffic through a single cloud region if latency and cost aren’t justified.
• Regularly monitor egress traffic and adapt architectures proactively to minimise unexpected charges.

Evaluating and restructuring these patterns can drastically reduce monthly bills, especially in multi-region, multi-cloud scenarios.

Implement Tiered Storage & Smart Archiving

Not all data is hot. Yet many teams store logs, analytics datasets, or backups on high-performance storage tiers by default.  

Instead:

• Adopt lifecycle policies that automatically tier older data to cold storage (e.g., Google Cloud Archive, AWS Glacier).
• Use Elastic or Grafana Loki with retention policies and compression for logs that don’t need to live forever.
• Review object storage regularly to avoid storing cold data on costly tiers, ensuring compliance and retrieval speed are balanced against expense.

Storage costs tend to accumulate quietly. Automating tiering and applying just-in-time retrieval patterns (instead of high-availability assumptions) is a quick win for cost-conscious teams.

Establish Robust Tagging & FinOps Practices 

Establish a culture of accountability and cost ownership with robust tagging standards. Attribute costs directly to teams or projects, enabling ownership and informed decision-making. Use tools like Infracost during infrastructure planning to estimate and optimize spending upfront.

Regularly communicate cost insights with stakeholders, balancing effort, impact, and optimisation potential. Ensure teams understand the correlation between growth, usage, and costs, encouraging proactive engagement with cost optimisation initiatives. 

Automate Resource Clean-up

Idle resources are a drain on budgets. Sandboxed environments, forgotten dev VMs, old staging databases—all continue to incur charges unless deliberately cleaned up.

To address this:

• Set TTLs on ephemeral environments—automatically delete resources after a fixed window for non-production environments, encouraging a culture of ephemeral infrastructure managed via Infrastructure as Code (IaC) and GitOps tools like ArgoCD or Terraform. 
• Adopt strict tagging policies for dev/staging/test environments, assigning clear budgets.

Teams that embrace ephemeral infrastructure tend to have significantly leaner cloud bills, as environments are tied to a purpose, not indefinite existence. In addition to reducing waste it also strengthens the organisations disaster recovery capabilities.

Summary: Cost Optimisation is a Design Discipline

Optimising hybrid and multi-cloud costs isn’t about squeezing pennies - it’s about aligning architecture to business intent. The key is visibility, automation, portability and cost ownership. By making cost a first-class citizen in design decisions, you not only reduce waste, but improve agility and operational resilience.

Don’t chase savings at the expense of performance or security. Work with partners like Deimos to establish well-architected, secure, and scalable cloud foundations—so that every dollar spent is delivering value. Click here to request a call back for more information.