smeltr

Fast, self-hosted telemetry pipelines.
Built visually. Managed with Git.

Get smeltr Try Demo

Free for individuals and for organizations with fewer than 250 employees. Source code on GitHub.

Keep the data that matters.

Filter, shape, and route logs and telemetry between your sources and destinations.

Sources

Kafka, Amazon S3, Syslog receiver, OTLP receiver, HEC receiver, and 15 more

Order Order placed debug: trace-101
Health check Service is healthy debug: trace-102
Error Payment timed out debug: trace-103

Smeltr

  1. 01
    FilterKeep useful records and fields
  2. 02
    ParseTurn raw text into named fields
  3. 03
    EnrichAdd context from your data
  4. 04
    RouteChoose where each record goes

Destinations

Splunk HEC, Elasticsearch, Amazon S3, Kafka, Apache Iceberg, and 8 more

Order Order placed
Error Payment timed out

Useful records.
Only the fields you need.

Search · Detection · Reporting

Example policy: drop health checks and remove debug fields. Three records in, two smaller records out.

Explore sources Explore destinations

Build with real data

See what each change does.

Capture a sample, edit your pipeline, and inspect the output. Save the input and expected result as a regression test.

Configure the step

Open directory lookup step, with source_user as the input and directory (csv) as the lookup table.

See what changes

Two expanded sample records show the added user.department and user.full_name fields.
Open complete screenshot ↗

Configure the step

Open directory lookup step, with source_user as the input and directory (csv) as the lookup table.

See what changes

Two expanded sample records show the added user.department and user.full_name fields.
Open complete screenshot ↗
Details from the running editor, using sample data. This lookup adds a department and full name to each matching record.

Try smeltr in your browser →

  1. 01Capture real data
  2. 02Preview changes
  3. 03Save tests
  4. 04Review and merge

Performance

Make compute go further.

Self-hosted processing designed for efficiency. Filter unwanted data before it reaches downstream systems, and run workers near your sources.

Choose your deployment →

Change management

Git is the workflow.

Develop on a branch. Commit your changes, review the diff and test results, then merge to main to deploy. Workers follow main.

Explore the Git workflow →

Operations

Know what is running.

Trace data flow, investigate warnings, and see which commit each worker runs. Verify the result at the destination.

Operate the fleet →