Feature / Live tail

Live Log Viewer for Production Logs

Watch active production logs in Fluxtail as they arrive. Use live tail to confirm receivers are accepting traffic, follow deploy output, filter noisy streams, inspect rows, and debug failures while they are still happening.

Live tail Readable rows Fast filters Incident triage

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Confirm logs are arriving

Open a stream and check whether the receiver is accepting the HTTP, Syslog, OTLP, GELF, collector, or client-library traffic you expect.

Filter without leaving live tail

Narrow rows by stream, level, service, host, source, message text, or structured fields while new logs continue to arrive.

Keep rows readable

A useful live log viewer keeps timestamp, level, service, host, message, and important fields visible in one scan path.

Move into diagnostics when needed

Use facets, histograms, alerts, MCP diagnostics, and built-in AI chat after live tail shows the rows that matter.

Viewer proof

See Fluxtail live tail with real rows

A live log viewer should show the actual event rows first, then make filtering and analysis available from the same stream.

Fluxtail live tail showing active log rows in the product.

Compact live rows with fields worth filtering on

output

2026-04-24T14:12:08Z info  checkout-api production host=checkout-1 payment authorized user_id=1842 order_id=99013
2026-04-24T14:12:11Z warn  queue-worker production host=queue-2 retrying webhook delivery attempt=3 status=502
2026-04-24T14:12:14Z error syslog-ingest production host=edge-1 invalid syslog frame receiver=syslog-prod source_ip=203.0.113.42
2026-04-24T14:12:19Z error web production host=app-4 database timeout route=/api/orders duration_ms=5043

From rows like these, filter by level, service, host, route, receiver, source IP, or request fields before opening histograms or AI diagnostics.

Use case

Watch production logs as they arrive

A live log viewer is useful when you need to see what a service, host, collector, or cluster is doing right now.

01

Verify ingestion

Use live tail logs to confirm that a receiver is accepting traffic and sending events into the expected stream.

new HTTP application events
syslog from hosts, routers, firewalls, or appliances
OTLP, GELF, collector, or client-library traffic
Kubernetes logs sent through collectors

02

Follow a deploy or active failure

Open the affected stream, keep live tail running, and watch whether new warnings, errors, retries, or timeouts appear after the change.

filter by service, level, host, source, or message text
check the rows before and after an error
use facets to spot noisy hosts or services

03

Stay in one readable stream

A real time log viewer reduces jumps between host tails, pod tails, copied terminal output, and old exports when the current rows already answer the first question.

04

Use analysis after the row is visible

Once live tail shows the relevant rows, compare histogram spikes, inspect fields, summarize repeated patterns, or open related MCP diagnostics.

Checklist

What a live log viewer should help you check

The page should answer practical questions quickly, not just scroll rows forever.

01

Are the expected logs arriving?

Check the receiver, stream, source type, and recent rows before assuming the application is silent.

receiver is enabled and accepting traffic
stream matches the source you are checking
timestamps and levels look sane

02

Can you narrow the stream?

Filter by the fields operators actually use during debugging: service, host, level, route, source IP, request ID, namespace, pod, or message text.

start broad with stream and level
then narrow by service, host, or structured field
use facets to find repeated sources

03

Does the row keep enough context?

A readable row should keep time, level, source, service, host, message, and useful fields visible before you need a separate detail view.

04

What should happen next?

If errors repeat, summarize the pattern in AI chat, run MCP diagnostics for exceptions or receiver health, or create an alert for the condition you need to monitor again.

Relationship

How the live viewer relates to centralized log management

Centralized log management collects the rows. The live log viewer is where you first confirm what those rows are doing right now.

Step 1

Aggregate logs centrally

Bring sources together through HTTP, Syslog, OTLP, GELF, or collectors.

Step 2

Break the firehose into streams

Use meaningful source boundaries so the live view stays readable.

Step 3

Read the active window

Use the live viewer to confirm current events, inspect surrounding rows, and keep the relevant filters in place.

Step 4

Continue from the evidence

If the rows point to collection problems, check the log aggregation setup. If they point to repeated errors, summarize the selected slice. If they point to syslog or Kubernetes sources, inspect those source-specific fields next.

Centralized log management with readable live tail, clear streams, and straightforward ingest.

Learn how log aggregation works across apps, hosts, syslog, containers, Kubernetes, OTLP, GELF, and collectors.

Use Fluxtail for AI-assisted log analysis through MCP and built-in AI chat while keeping the raw logs available for verification.

Practical guide to Kubernetes logging: what metadata to keep, how collection paths work, and how Fluxtail keeps cluster logs searchable.

What a syslog analyzer should help you do: filter syslog logs by host, app, severity, and time, then summarize the selected rows.

Next step

Send one real source and read the logs

The fastest check is to point one real source at Fluxtail and see whether the resulting stream is easier to read.

Point one real source at Fluxtail.

Create a receiver, send one source, and inspect the first stream.