Case Study: Global Real-Estate Company achieves $1.8M annual savings and accelerated search performance with Elastic

Global real estate company accelerates searches and reduces software licensing costs by $150,000 per month with Elastic Observability

A leading global real-estate franchise — with more than 140,000 agents, nearly 9,000 offices, and operations in 110+ countries — needed to simplify ingestion from 450 geographically distributed MLS sources and fix data-flow inefficiencies in a Kubernetes-based microservices architecture that used Confluent as a central hub. Massive data volumes created processing backlogs and made root-cause identification slow and costly, and a Datadog contract renewal prompted a search for a more affordable, flexible observability solution.

They migrated to Elastic Observability and Elasticsearch, integrating with Kubernetes, Confluent, and custom Logstash plugins, and adopted Elastic APM and Kibana Spaces for unified full‑stack visibility. The switch cut licensing costs by about $150,000/month (≈ $1.8M/year), reduced duplicate data through the hub by 90%, doubled log/APM retention from two weeks to 30 days (and increased storage retention 100%), improved code quality and deployment speed across 450+ services, delivered sub-10 ms address autocomplete, and freed developer time by simplifying issue investigation.