Pick one workload
Begin with a costly, noisy or long-retention log source.
All use cases
Keep the evidence, reduce the machinery
Replace a costly or complex log stack
Move a high-volume or long-retention workload to an analytical foundation that combines text search and SQL without exporting your data.
coreva_logslogs_application_events1H
search service, level or message...⌘ K
Log streamAI
TIMELEVELSERVICEMESSAGE
10:42:18.904ERRORcheckout-apipayment upstream timeout · trace=8fa2
10:42:18.887WARNpayment-svcretry budget at 80% · region=tr-1
10:42:17.512INFOgatewayrequest routed · latency=34ms
Explain this error cluster with AI→
You already centralize logs, but indexing, retention and cluster operations force the team to optimize for the tool instead of the incident.
The logging stack should help resolve incidents—not create its own on-call rotation.
The pain
Your log platform became another product to operate
01
Index and node planning consumes platform time.
02
Retention is shortened to control cost.
03
Archived logs are slow or awkward to bring back.
04
Search and analytical questions split across tools.
The UnifyLogs path
Migrate where the pain is measurable
Reuse the shipper
Connect Vector, Fluent Bit, Logstash, Filebeat, OpenTelemetry or HTTP.
Validate on your data
Compare ingest, storage and representative search and aggregation queries.
What changes
The operating pattern changes—not only the tool.
- A staged migration instead of a risky big bang
- One engine for retrieval and analytical SQL
- Retention decisions based on need, not only index cost
CONTACT / HUBSPOT
Bring us the log problem you have today.
Tell us where your logs live, what breaks during incidents, and what must remain on-prem. We will respond with a practical evaluation path.