Case studies
What changed, by how much, and how long it took. Client names are withheld by agreement; every engagement below is anonymised.
$12.4M
cloud costs saved across all engagements
~30%
typical efficiency increase
249+
customers across 14+ industries
up to 60%
reduction in AI usage cost
B2B SaaS scaleup cuts AWS run-rate by ~30% while doubling workloads
A ~180-person B2B SaaS scaleup reduced its AWS run-rate by around 30% in about four months by combining right-sizing, commitment restructuring and per-team cost ownership, while its workload volume roughly doubled over the same period.
- ~30%
- reduction in monthly AWS run-rate
- ~2x
- workload volume over the same period
Multi-brand e-commerce group brings AWS, Azure and GCP under one FinOps model
A ~600-person European e-commerce group unified cost management across AWS, Azure and Google Cloud, cutting combined cloud spend by roughly 22% and reducing monthly cost reporting from about two weeks of manual work to a single automated review.
- ~22%
- reduction in combined multi-cloud spend
- 3 → 1
- cloud billing views consolidated into one model
Regulated fintech cuts AI spend by ~55% after routing all traffic through an LLM gateway
A ~350-person regulated European fintech reduced its LLM spend by roughly 55% within one quarter by routing all model traffic through an LLM gateway with task-based model routing, semantic caching and per-team budgets — with under 10ms of added routing overhead.
- ~55%
- reduction in monthly LLM spend
- <10ms
- added routing overhead
Healthcare network moves AI from pilot to production with enforced guardrails
A ~1,200-employee healthcare provider network moved seven blocked AI pilots into production within two quarters by deploying bluebill on-premises with runtime PII redaction, prompt-injection defence and complete audit logging mapped to EU AI Act and ISO/IEC 42001 requirements.
- 7
- blocked pilots moved into production
- 5 weeks
- to on-premises deployment
Client names are withheld by agreement. Engagement profiles are anonymised; figures are the measured results of the engagement described and are consistent with bluebill's aggregate programme results. Individual results vary with environment, scale and starting maturity.
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