Cutting Cloud and AI Spend at the Root

Cloud spend rarely explodes in one place. It leaks across hundreds of small inefficiencies: Spark jobs that scan more data than they need, SQL queries tuned for correctness but never for cost, clusters sized for peaks that never come, and LLM inference running on larger models than the task requires. The fix is not a smaller budget. It is an audit that finds where the money actually goes.

Start with the audit

A comprehensive review of the data engineering codebase and cloud spend identifies the highest-impact optimisation opportunities before any fix is implemented. This ordering matters: targeted fixes on the biggest leaks deliver measurable cost reduction, while blind optimisation burns engineering time for little return.

Where the leaks usually are

• Spark and SQL pipelines with unprofiled bottlenecks and oversized compute.
• Delta Lake layouts that have never been optimised for the queries run against them.
• Missing autoscaling policies and tagging strategies, so spend cannot be attributed.
• LLM inference without model selection, batch strategies, or token optimisation.
• Accumulated tech debt that raises the maintenance cost of every change.

Reduce AI inference costs deliberately

LLM and AI inference costs respond well to methodical work: choosing the smallest model that meets the quality bar, batching requests where latency allows, and optimising token usage in prompts and context. These are engineering decisions with direct budget impact, and they compound across high-volume workloads.

The principle generalises: reduce costs at the root, not just the surface, while maintaining performance and reliability. Our optimisation services cover Spark and SQL tuning, inference optimisation, cloud cost management, code quality reviews, and pipeline acceleration.

We reduce costs at the root, not just the surface, while maintaining performance and reliability.