Splunk is a powerful but expensive log management tool. This article shows how I used the free ELK stack (Elasticsearch, Logstash, Kibana) to build comparable functionality myself — log aggregation, full-text search, and dashboards, without license costs.
Architecture: Logstash collects logs from all servers (via Filebeat as a lightweight log shipper), parses and normalizes them, and writes them into Elasticsearch. Kibana serves as the search and visualization frontend.
Particularly useful: Kibana dashboards for error rates, response times, and usage patterns across multiple services, plus full-text search across all collected logs for troubleshooting.
Limits of the DIY approach: operating and scaling the ELK cluster yourself requires expertise and ongoing maintenance — the supposed license-cost advantage is partly offset by the operational overhead.
Retrospective (2026)
Looking back, I’d say the trade-off I described in 2014 has only gotten sharper. Today I’d rarely recommend running your own ELK cluster unless you genuinely need that level of control — managed observability platforms and hosted OpenSearch have absorbed most of the operational pain I warned about, and they do it well. What’s changed more fundamentally is the search experience itself: back then “full-text search” meant crafting the right query by hand, whereas now AI-agent-assisted log analysis can surface the anomaly or root cause for you, often faster than a dashboard would. In my current work on agent architectures, this is exactly the shift I find most interesting — the value has moved from “can I search my logs cheaply” to “can something intelligent read them for me.” So the self-build motivation from 2014 is largely gone, but the underlying problem of taming log data is, if anything, more relevant than ever.