hello@logkit.io · Status · GitHub
Platform Solutions

One ingest pipeline.
Every service.
Zero surprises.

Stop drowning in log volume and routing spaghetti. LogKit gives platform teams a centralized, schema-enforced pipeline that standardizes telemetry across monoliths, microservices, and serverless functions.

MIT-licensed SDK · Enterprise-grade platform

LogKit platform architecture showing centralized ingestion and routing
The Platform Friction

Why manual log management scales poorly.

Every new service team brings its own logging library, field naming conventions, and routing logic. Without a centralized contract, your observability stack becomes a fragile patchwork.

Volume explosion: Unstructured JSON strings from thousands of microservices saturate your storage and query engines, slowing down response times for critical alerts.

Inconsistent schemas: You spend more time writing regex filters to parse log lines than debugging the actual issue. `user_id` in service A, `userid` in service B, and `userId` in service C.

Routing complexity: Trying to route dev logs to a local dashboard while keeping prod logs isolated for audit requires brittle grep scripts and cron jobs.

The LogKit Pipeline Architecture

A unified data flow that removes the "black box" from observability.

LogKit pipeline architecture diagram
  • 01

    Source SDK

    Zero-allocation instrumentation enforces schema locally before data leaves the host.

  • 02

    Central Ingestion

    A single, highly available endpoint receives events from all services via gRPC and HTTP.

  • 03

    Schema Validation

    Events are validated against the centralized Registry. Invalid data is rejected or corrected automatically.

  • 04

    Routing & Storage

    Intelligent routing directs logs to the correct environment, retention policy, or alerting channel.

Schema Registry

The Single Source of Truth for your logs.

Define your data contract once, enforce it everywhere. The Schema Registry acts as the gatekeeper for your observability pipeline.

Centralized Definitions

Define standard fields (service_name, environment, trace_id) and optional fields once in the Registry. All SDKs validate against this definition at runtime.

Validation at Edge

Invalid events (missing required fields, wrong types) are rejected or automatically corrected by the SDK. No need to write post-ingestion parsers.

Versioning & Deprecation

Introduce new schema versions without breaking existing log queries. The platform automatically maps old fields to new ones during migration periods.

Environment Routing

Dev, Staging, and Prod on a single pane.

Separate your noise from your signal with environment-aware routing policies and dynamic sampling.

Dynamic Sampling

Reduce prod ingestion costs by 90% without losing debugging power. Automatically sample 10% of traffic in production while ingesting 100% in dev and staging. Sampling is transparent to users.

Isolated Views

Configure routing rules that automatically tag logs with their origin environment. Query logs from production without seeing noise from CI/CD pipelines or local development builds.

Compliance & Audit

Full visibility into who accessed what.

Maintain strict governance over your log data with immutable audit trails and field-level redaction.

Immutable Audit Logs

Track every schema change, user login, and permission grant. The platform logs who modified a query, who deleted a log index, and when it happened.

Field-Level Redaction

Automatically mask PII such as credit card numbers, SSNs, and email addresses before ingestion. Redaction rules are configurable and applied at the pipeline layer.

RBAC & Data Residency

Control access to logs based on team roles. Configure strict data residency policies to ensure logs for EU customers remain within EU data centers.

Architecture Review

Ready to centralize your observability?

Stop patching log pipelines. Schedule a platform architecture review with our engineering team to design a logging strategy that scales with your infrastructure.