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 is the reproducible XERJ-vs-Elasticsearch query matrix — bool filters, range scans, term lookups and aggregations — measured on a 1M-doc corpus. XERJ is the yellow bar; Elasticsearch 8.13 is the gray one.
P50 · 1M-DOC LLM-TELEMETRY CORPUS · FULL 91-CELL MATRIX AT xerj.org/benchmarks →
Ship logs at line rate. Query hot data in milliseconds. No ILM policy puzzles, no shard tuning, no Logstash pipeline. The chart shows a 24-hour diurnal ingest pattern — XERJ sustains it with zero write-stalls, and ingests 1.5–1.7× faster than Elasticsearch in the reproducible benchmark.
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 benchmark below is the reproducible XERJ-vs-Elasticsearch latency for kNN and text retrieval.
P50 · 1M-DOC LLM-TELEMETRY CORPUS · FULL 91-CELL MATRIX AT xerj.org/benchmarks →
Same API on port 9200. Same query DSL. Same client libraries. The comparison on the right is the reproducible query-latency head-to-head. Yellow is XERJ; gray is Elasticsearch 8.13 on the identical workload.
P50 · 1M-DOC LLM-TELEMETRY CORPUS · FULL 91-CELL MATRIX AT xerj.org/benchmarks →
Logs, traces, and metrics in one store — one binary instead of a Grafana-Loki-Tempo-Mimir stack to babysit. OTLP in, Prometheus out. The chart on the right is the reproducible query-latency head-to-head vs Elasticsearch 8.13 on log-shaped data.
P50 · 1M-DOC LLM-TELEMETRY CORPUS · FULL 91-CELL MATRIX AT xerj.org/benchmarks →
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