Purl vs ELK Stack
One binary replaces three systems
Elasticsearch needs 3+ nodes, 16GB+ RAM each, and a dedicated engineer for shard management. Purl replaces the entire ELK Stack with a single binary + ClickHouse — 10x less resources, 10x faster queries.
Common Frustrations
Why teams switch from ELK Stack
These are the top reasons developers leave ELK Stack for something simpler.
Three systems to maintain
Elasticsearch, Logstash, and Kibana are three separate codebases with different config files, different upgrade paths, and different failure modes. One breaks, everything breaks.
JVM memory nightmares
Elasticsearch runs on the JVM. Tuning heap size, garbage collection pauses, and circuit breakers is a full-time job. OOM kills at 3 AM are a rite of passage.
Shard management hell
Too many shards? Cluster slows down. Too few? Can't scale. Wrong ILM policy? Data loss. Shard allocation is the #1 operational pain point for ELK operators.
Resource hungry
A production ELK cluster needs 3+ nodes with 16GB+ RAM each — minimum $400/month in infrastructure before you even ingest a single log line.
Feature Comparison
How Purl stacks up
A detailed side-by-side comparison of key features and capabilities.
| Feature | Purl | ELK Stack |
|---|---|---|
| Architecture | ||
| Components | 1 binary + ClickHouse | Elasticsearch + Logstash + Kibana |
| Minimum RAM | 512MB | 16GB+ (JVM heap per node) |
| Production nodes | 1 (single node) | 3+ (cluster minimum) |
| Storage engine | ClickHouse (columnar) | Elasticsearch (Lucene) |
| Compression ratio | 10–20x | 1.5–3x |
| Performance | ||
| Query speed (50M rows) | ~180ms | 3–5 seconds |
| Ingest rate (same hardware) | 150K logs/sec | ~20K logs/sec |
| Full-text regex (100M logs) | ~450ms | 10–30 seconds |
| Operations | ||
| Shard management | None (automatic) | Manual ILM policies required |
| JVM tuning | Not applicable | Heap size, GC, circuit breakers |
| Data retention | Automatic TTL (partition drop) | ILM + rollover + delete |
| Cluster recovery | docker compose up (seconds) | Hours (shard allocation) |
| Upgrade process | Pull new Docker image | Rolling restart, version compat |
| Features | ||
| Query language | KQL + ES-compatible _search | Query DSL + KQL (Kibana) |
| Live tail | WebSocket streaming | Kibana Discover (polling) |
| Pattern detection | Built-in (materialized views) | ML anomaly detection (paid) |
| Alerting | Telegram, Slack, Webhook | Watcher (paid X-Pack) |
| OTLP ingestion | Native endpoint | Via Logstash plugin |
| ES-compatible API | _search, _msearch, _field_caps | Native |
| Cost | ||
| Software license | From $0 (Free tier) | Open source (SSPL/AGPL) |
| Infrastructure (100GB/day) | ~$80/mo (1 VPS) | ~$400–800/mo (3+ nodes) |
| Ops engineer time | ~1 hour/month | 10–20 hours/month |
Migration Guide
Switch from ELK Stack in minutes
A straightforward migration path with zero downtime.
Install Purl alongside ELK — both can run simultaneously. Just run: docker compose up.
Point your existing Filebeat/Fluent Bit/Vector shippers to dual-write to both Purl and Elasticsearch.
Purl supports ES-compatible _search API — test your existing Kibana queries directly against Purl.
Map Kibana saved searches → Purl saved searches, Watcher alerts → Purl alerts (Telegram/Slack/Webhook).
Run both for 24–48 hours. Compare query results to verify data parity.
Switch all shippers to Purl only. Shut down Elasticsearch, Logstash, and Kibana. Reclaim 16–64GB RAM.
Ready to leave ELK Stack behind?
Start your free migration today. No credit card required. Your logs, your infrastructure, your rules.