Fluent Bit, Logstash, Vector, OpenTelemetry Collector, or direct HTTP. Parseable accepts logs from every shipper your stack already uses, with no re-instrumentation required.
Query logs with the SQL your team already knows, or ask a question in plain English and let Parseable generate and run the query. Dashboards and alerts use the same interface.
Spot patterns before they spread. Use AI summaries to understand log patterns quickly and forecasting to identify unusual trends earlier. Parseable helps teams move from reactive searching to proactive log monitoring.
Why Parseable
Parseable gives teams one place to ingest, search, query, visualize, and alert on logs without stitching together a separate logging stack. Logs can be investigated with SQL, plain-English queries, AI summaries, dashboards, and alerts.
Every log stream lands in columnar Parquet on S3, GCS, or Azure Blob. Each field is its own column, so querying severity never reads message or any other column you did not ask for.
Ask a question in plain English and Parseable generates and executes the SQL. Switch to raw SQL when you need full control.
Jump from a log line to the trace that produced it, or pivot to metrics for the same time window. Parseable correlates signals across logs, metrics, and traces without a separate tool.
Parseable AI groups related log lines into clusters, surfaces the root-cause pattern, and writes a plain-English summary so your team spends time on the fix, not the search.
Log burst detected
db-postgres connection pool exhausted. 312 ERROR logs in 90s from billing-worker.
Set alerts on error rate, log volume, missing heartbeats, or any SQL condition. Route to Slack, PagerDuty, email, or webhooks without adding a separate alerting layer.
Use Cases
Search application and infrastructure logs alongside the traces and metrics from the same request. Follow an error across services without switching tools or losing the surrounding context.
Search application and infrastructure logs alongside the traces and metrics from the same request. Follow an error across services without switching tools or losing the surrounding context.
Collect logs from MCP servers, LLM applications, and agents in one place. Track token usage, model latency, errors, and cost across inference calls and sessions.
Total sessions
1,246
−12%
Total tokens
18.2M
+23%
Latency P95
5.32s
−7%
Error rate
2.4%
−17%
My order #88821 hasn't arrived and I'd like a refund...
My package was supposed to arrive yesterday, can you...
I received the wrong item and I would like to exchange...
I want to delete my account and all associated data...
I just wanted to say how great the support was during...
Can you confirm that my payment of $142.50 was processed...
I'd like to upgrade my plan to Pro but the button isn't...
Query product events together with the application logs behind them. Compare feature adoption, user journeys, and API performance without reducing events to pre-aggregated metrics.
Capture and analyze user activity, system events, and security logs to ensure compliance and security. Columnar storage keeps audit queries fast even at billion-event scale.
2026-04-29T09:23:59.847
2026-04-29T09:24:01.203
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