Five enterprise workloads. One binary. No Logstash, no Beats, no separate vector database, no dedicated Kibana cluster. Each section below is a real deployment pattern with measured numbers against Elasticsearch 8.13 — and a live chart from the product dashboards.
Your SOC team spends more time waiting for dashboards than investigating threats. The battery on the right runs 8 canonical SIEM queries — top source IPs, auth-failure clustering, lateral movement, brute-force detection — with measured latency on 1M events. XERJ is the yellow bar. Elasticsearch is the gray one.
Ship logs at line rate. Query hot data in milliseconds. No ILM policy puzzles, no shard tuning, no Logstash pipeline. The chart shows sustained ingest throughput over 24 hours — 80K docs/s from a single client, diurnal traffic pattern, zero write-stalls.
Hybrid semantic + keyword search in one query. The cluster plot shows 830K real queries grouped into six intent clusters — RAG retrieval dominates. Every cluster is one index query away, not a separate Pinecone call. The memory comparison below shows the cost of running vectors at scale.
Same API on port 9200. Same query DSL. Same client libraries. The comparison shows what changes: memory, disk, cold start, config surface, and component count. Yellow is XERJ. Gray is a 4-node Elasticsearch cluster doing the same job.
Logs, traces, and metrics in one store. The comparison shows the operational surface: one binary vs six, one config vs twelve, one query language vs three, one bill vs five. OTLP in, Prometheus out. No Grafana-Loki-Tempo-Mimir stack to babysit.
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