LegalCare.io
Cutting Azure Costs by 80%LegalCare.io provides an AI-powered legal assistant that automates legal documentation and research. Their Azure infrastructure costs were exceeding budget and threatening profitability, requiring a systematic FinOps intervention.
Challenges
LegalCare.io's Azure spend had grown unchecked as the platform scaled, with over-provisioned VMs, unoptimised queries, and poor load balancing driving costs far beyond what the workload justified.
Over-provisioned VMs were consuming budget on idle capacity that never matched actual demand.
Unoptimised database queries caused high storage costs and slowed AI model response times.
Poor load balancing created unpredictable traffic distribution and inflated resource usage.
Solution
Nife Cost identified and remediated LegalCare.io's Azure waste through auto-scaling, query optimisation, and regional workload redistribution.
Auto-Scaling Policies: Dynamic VM scaling ensured resources matched actual demand at all times.
Database Query Optimisation: Queries re-written to reduce data retrieval times and storage costs.
Regional Workload Shifting: Workloads moved from expensive Azure regions to cost-effective alternatives.
Results
80%
Azure cost reduction
Monthly Azure spend cut from over-budget to well within target.Faster
AI model inference
Query optimisation improved response times for AI-powered features.Increased
Reliability
Right-sized infrastructure improved uptime and reduced error rates.Cut your cloud costs without sacrificing performance. Try Nife Cost. 💡
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