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Code & Cost Optimisation

Stop overpaying for cloud and AI. We find the waste, fix it, and make sure it stays fixed.

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AFTER RUNBEFORE RUN

Code & Cost Optimisation

What we can do

Audit and fix inefficient code, queries, and pipelines draining your cloud budget.

From Spark optimisation to SQL tuning and infrastructure right-sizing, we cut costs at the root while keeping performance and reliability intact.

“We start with a full audit of your codebase and cloud spend, find the highest-impact fixes, and implement them so you see measurable cost reduction without losing performance.”

System flow / Code & Cost Optimisation

Find, fix, verify

Start from observed workloads and spend, target the biggest bottlenecks, then review the effect of each change.

01 / OBSERVE / Profile workloads

Spark jobs, SQL queries, pipelines and inference

How We Cut Your Cloud Costs

  1. 01

    Code & Query Optimisation

    • Pipeline & query optimisation
    • Spark, SQL, Delta, Databricks
    • Cloud cost analysis
    • Workload review
    • Diagnose inefficiencies
    • Reduce tech debt
  2. 02

    Cost Efficiency

    • Infrastructure spend analysis
    • Reduce compute consumption
    • LLM & AI inference cost reduction
    • Model selection & batch strategies
    • Token optimisation
  3. 03

    Code Quality & Maintainability

    • Code quality & tech debt reviews
    • Codebase assessment
    • Improve scalability
    • Reduce maintenance costs

Scope, made tangible

Explore the work areas

Select a work area to see what it covers, including the technologies used for it. Scope and work products are tailored to your environment.

AREA 01

Spark & SQL Optimisation

Profile and refactor Spark, SQL, and ETL pipelines to eliminate bottlenecks and reduce compute.

  • Spark job profiling
  • SQL query tuning
  • Delta Lake optimisation
  • Spark UI
  • Profilers

Delivery path / Code & Cost Optimisation

One delivery framework, tailored to this system.

Each phase is shaped around the systems, controls and operating needs of this service.

  1. 01 PHASE

    Spend & Codebase Audit

    Audit workloads, codebases and cloud spend to find opportunities.

    Check / output

    Codebase and spend assessment

  2. 02 PHASE

    Bottleneck Diagnosis

    Profile Spark, SQL and ETL paths; diagnose bottlenecks and tech debt.

    Check / output

    Bottleneck review

  3. 03 PHASE

    Targeted Optimisation Changes

    Apply targeted query, pipeline, inference or infrastructure changes.

    Check / output

    Optimisation changes

  4. 04 PHASE

    Reliability & Cost Controls

    Preserve performance and reliability while reviewing cost controls.

    Check / output

    Reliability and cost review

  5. 05 PHASE

    Follow-Up Opportunity Review

    Revisit workload performance and cloud spend for further opportunities.

    Check / output

    Follow-up review

Engagement outputs

Typical work products

Final outputs are agreed during discovery, based on your environment and the work in scope.

  • 01

    Workload assessment

    Codebase, query, pipeline and cloud-spend review.

  • 02

    Bottleneck analysis

    Profiled Spark, SQL, ETL or inference paths.

  • 03

    Prioritised optimisation plan

    Targeted opportunities across code, compute and model use.

  • 04

    Scoped changes

    Query, pipeline or infrastructure tuning agreed for the engagement.