USE CASES·ENTERPRISE

REPLACE THE
STACK.
KEEP THE DATA.

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.

01
SECURITY
ANALYTICS
AT SCALE.

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.

11.5× FASTER BOOL QUERIES
CISO · SOC ANALYST · SECOPS
FULL USE CASE →
REPRODUCIBLE · ES 8.13 · demo/playbooks/SCORECARD.md
XERJ ES 8.13
Bool query 1.38 ms 15.90 ms 11.5×
Range filter 1.34 ms 4.66 ms 3.5×
Match query 0.57 ms 1.45 ms 2.5×
Terms + avg sub-agg 0.38 ms 1.16 ms 3.1×
Date-histogram agg 0.37 ms 0.88 ms 2.4×
Cardinality agg 1.15 ms 1.92 ms 1.7×
Term query 1.36 ms 2.15 ms 1.6×
Terms aggregation 1.34 ms 1.54 ms 1.15×

P50 · 1M-DOC LLM-TELEMETRY CORPUS · FULL 91-CELL MATRIX AT xerj.org/benchmarks →

02
OPERATIONAL
INTELLIGENCE.

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.

1.7× FASTER INGEST
SRE · DEVOPS · PLATFORM ENGINEERING
FULL USE CASE →
INGEST THROUGHPUT · 24H · SINGLE CLIENT
00:00 · 20K docs/s min 20K docs/s · peak 82.51K docs/s 20K docs/s · 24:00
03
AI-NATIVE
SEARCH &
RETRIEVAL.

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.

3.4× FASTER kNN · 100% RECALL
ML ENGINEER · DATA SCIENTIST · AI PLATFORM
FULL USE CASE →
REPRODUCIBLE · ES 8.13 · demo/playbooks/SCORECARD.md
XERJ ES 8.13
Query-string query 1.35 ms 9.35 ms 6.9×
Multi-match query 1.44 ms 5.89 ms 4.1×
kNN · k=10 · 100% recall 0.78 ms 2.64 ms 3.4×
Match query 0.57 ms 1.45 ms 2.5×

P50 · 1M-DOC LLM-TELEMETRY CORPUS · FULL 91-CELL MATRIX AT xerj.org/benchmarks →

04
ELASTICSEARCH
REPLACEMENT.

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.

1.20× SMALLER ON DISK
VP ENGINEERING · CTO · PLATFORM TEAM
FULL USE CASE →
REPRODUCIBLE · ES 8.13 · demo/playbooks/SCORECARD.md
XERJ ES 8.13
Bool query 1.38 ms 15.90 ms 11.5×
Query-string query 1.35 ms 9.35 ms 6.9×
Wildcard query 1.35 ms 9.10 ms 6.8×
Range filter 1.34 ms 4.66 ms 3.5×
Match query 0.57 ms 1.45 ms 2.5×
Term query 1.36 ms 2.15 ms 1.6×

P50 · 1M-DOC LLM-TELEMETRY CORPUS · FULL 91-CELL MATRIX AT xerj.org/benchmarks →

05
UNIFIED
OBSERVABILITY.

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.

6 SYSTEMS → 1 BINARY
OBSERVABILITY LEAD · SRE · INFRA
FULL USE CASE →
REPRODUCIBLE · ES 8.13 · demo/playbooks/SCORECARD.md
XERJ ES 8.13
Range filter 1.34 ms 4.66 ms 3.5×
Terms + avg sub-agg 0.38 ms 1.16 ms 3.1×
Date-histogram agg 0.37 ms 0.88 ms 2.4×
Cardinality agg 1.15 ms 1.92 ms 1.7×
Avg aggregation 1.16 ms 2.23 ms 1.9×
Terms aggregation 1.34 ms 1.54 ms 1.15×

P50 · 1M-DOC LLM-TELEMETRY CORPUS · FULL 91-CELL MATRIX AT xerj.org/benchmarks →

READY?·REQUEST ACCESS

RUN IT ON
YOUR DATA.

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