Insights & Best Practices
Dive into our latest thinking on FinOps, AI cost optimisation and ML engineering. Learn how to stop waste before it starts and extract maximum value from your AI budgets.
Latest Articles
Persistent Cost Inefficiencies in AI Workloads
Idle resources, over‑provisioned clusters and misconfigured storage silently drain budgets. Discover the common pitfalls and how organisations can reclaim up to 90 % of overspend.
Read Article →AI‑Driven FinOps: Real Savings Case Studies
Learn how a bank, a healthcare provider, an e‑commerce platform and a manufacturer each cut between 23 % and 35 % from their cloud bills using machine learning and smart guardrails.
Read Article →ML Cost Drivers: Compute, Storage & Networking
Compute is by far the largest cost driver in ML projects, but storage and network costs can quickly add up. Explore how to right‑size each component for maximum efficiency.
Read Article →The Hidden Cost of GenAI Inference
Generative AI inference can account for up to 90 % of AI spend. Find out why underutilisation rates are so high and how to shrink them with pooling and dynamic scaling.
Read Article →Cutting LLM Training Costs with Spot Instances
Hyperparameter sweeps and large language model training can cost a fortune. See how using spot instances and rightsizing can slash your training bill by up to 90 %.
Read Article →Multi‑Cloud FinOps: Managing AWS, Azure & GCP
Running workloads across multiple clouds creates complexity in billing, tagging and rightsizing. Learn best practices for unified reporting and policy enforcement.
Read Article →Beyond Cloud: FinOps for SaaS & Private Cloud
FinOps doesn’t stop at public cloud. Discover how to bring financial discipline to SaaS licences and on‑prem compute, and why the majority of FinOps teams are expanding their scope.
Read Article →Balancing Cost, Performance & Impact
Achieving the right balance between speed, accuracy and spend can improve outcomes by up to 50 %. We outline a framework to guide trade‑offs and optimise ROI.
Read Article →AI & Automation: The Future of FinOps
AI and automation tools can reduce unexpected bill spikes by 20 % and improve rightsizing efficiency by up to 30 %. Find out how proactive detection and remediation work in practice.
Read Article →FinOps Priorities for 2025: Waste Reduction Leads
Waste reduction, cost allocation and accurate forecasting top the list of FinOps priorities for 2025. See the survey results and what they mean for your organisation.
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