Autolemetry Backends

Vendor backend configurations for Autolemetry - simplified setup helpers for popular observability platforms.

What are Backends?

Backends are vendor-specific configuration helpers that simplify setting up Autolemetry with observability platforms like Honeycomb, Datadog, New Relic, etc.

They handle:

• Correct endpoint URLs for each vendor
• Authentication headers and API key formats
• Protocol selection (gRPC vs HTTP)
• Region-specific configurations
• Best practice defaults

Backends vs Plugins

| Package | Purpose | Examples | | ------------------------ | ----------------------------------------------------- | --------------------------- | | autolemetry-backends | Configure where telemetry goes (outputs) | Honeycomb, Datadog, Grafana | | autolemetry-plugins | Instrument libraries to create telemetry (inputs) | Drizzle ORM, custom SDKs |

Think of it this way: Plugins create the data, backends send it somewhere.

Installation

npm install autolemetry autolemetry-backends

Quick Start

Honeycomb

import { init } from 'autolemetry';
import { createHoneycombConfig } from 'autolemetry-backends/honeycomb';

init(
  createHoneycombConfig({
    apiKey: process.env.HONEYCOMB_API_KEY!,
    service: 'my-app',
  }),
);

Datadog

import { init } from 'autolemetry';
import { createDatadogConfig } from 'autolemetry-backends/datadog';

// Direct cloud ingestion (serverless, edge)
init(
  createDatadogConfig({
    apiKey: process.env.DATADOG_API_KEY!,
    service: 'my-lambda',
  }),
);

// Or use local Datadog Agent (Kubernetes, long-running services)
init(
  createDatadogConfig({
    service: 'my-api',
    useAgent: true,
  }),
);

Available Backends

🍯 Honeycomb

Honeycomb provides powerful distributed tracing and observability.

import { createHoneycombConfig } from 'autolemetry-backends/honeycomb';

init(
  createHoneycombConfig({
    apiKey: process.env.HONEYCOMB_API_KEY!,
    service: 'my-app',
    environment: 'production',
    version: '1.0.0',
    dataset: 'my-dataset', // Optional: for classic accounts
  }),
);

Features:

• Auto-configures gRPC protocol (Honeycomb's preferred)
• Supports both classic datasets and modern service-based routing
• Environment and version tagging
• Head-based sampling configuration

View full Honeycomb configuration options →

🐕 Datadog

Datadog provides comprehensive APM, infrastructure monitoring, and logs.

import { createDatadogConfig } from 'autolemetry-backends/datadog';

// Cloud ingestion (best for serverless/edge)
init(
  createDatadogConfig({
    apiKey: process.env.DATADOG_API_KEY!,
    site: 'datadoghq.com', // or 'datadoghq.eu', 'us3.datadoghq.com', etc.
    service: 'my-lambda',
    environment: 'production',
    enableLogs: true, // Optional: also send logs
  }),
);

// Agent-based (best for Kubernetes/VMs)
init(
  createDatadogConfig({
    service: 'my-api',
    useAgent: true,
    agentHost: 'localhost', // or 'datadog-agent.default.svc.cluster.local'
    agentPort: 4318,
  }),
);

Features:

• Direct cloud ingestion OR local agent
• Multi-region support (US1, US3, US5, EU, AP1, FedRAMP)
• Unified service tagging (service, env, version)
• Optional log export via OTLP
• Kubernetes-friendly agent configuration

View full Datadog configuration options →

Why Use Backend Configs?

Without backend configs (manual):

import { init } from 'autolemetry';

init({
  service: 'my-app',
  endpoint: 'https://api.honeycomb.io:443',
  protocol: 'grpc',
  otlpHeaders: {
    'x-honeycomb-team': process.env.HONEYCOMB_API_KEY!,
    'x-honeycomb-dataset': 'production',
  },
  environment: 'production',
  version: '1.0.0',
});

With backend configs:

import { init } from 'autolemetry';
import { createHoneycombConfig } from 'autolemetry-backends/honeycomb';

init(
  createHoneycombConfig({
    apiKey: process.env.HONEYCOMB_API_KEY!,
    service: 'my-app',
    environment: 'production',
    version: '1.0.0',
  }),
);

Benefits:

• Less code, fewer mistakes
• Vendor best practices built-in
• Validated configurations
• Easy to switch vendors

Using Environment Variables

All backends work great with environment variables:

import { createHoneycombConfig } from 'autolemetry-backends/honeycomb';

init(
  createHoneycombConfig({
    apiKey: process.env.HONEYCOMB_API_KEY!,
    service: process.env.SERVICE_NAME || 'my-app',
    environment: process.env.NODE_ENV,
  }),
);

Or use Autolemetry's built-in env var support:

# .env
OTEL_SERVICE_NAME=my-app
OTEL_EXPORTER_OTLP_ENDPOINT=https://api.honeycomb.io
OTEL_EXPORTER_OTLP_HEADERS=x-honeycomb-team=YOUR_API_KEY

import { init } from 'autolemetry';

// Reads from env vars automatically
init({});

Migration from autolemetry/presets

If you were using autolemetry/presets/*, migration is simple:

Before (v1.x):

import { createHoneycombConfig } from 'autolemetry/presets/honeycomb';

After (v2.x):

npm install autolemetry-backends

import { createHoneycombConfig } from 'autolemetry-backends/honeycomb';

The configuration options are identical - only the import path changed.

Philosophy

Autolemetry follows the principle: "Write once, observe everywhere".

Backend configurations are:

• Optional: Use raw init() config if you prefer
• Vendor-agnostic at core: Keeping these separate maintains the vendor-neutral philosophy
• Best practices: Configurations follow vendor recommendations
• Tree-shakeable: Import only what you need

TypeScript

Full type safety with TypeScript:

import type {
  HoneycombPresetConfig,
  DatadogPresetConfig,
} from 'autolemetry-backends';

const honeycombConfig: HoneycombPresetConfig = {
  apiKey: process.env.HONEYCOMB_API_KEY!,
  service: 'my-app',
};

const datadogConfig: DatadogPresetConfig = {
  apiKey: process.env.DATADOG_API_KEY!,
  service: 'my-app',
  site: 'datadoghq.com',
};

Contributing

Want to add a new backend configuration? Please open an issue to discuss.

Popular backends we'd love to support:

• Grafana Cloud
• New Relic
• Lightstep
• Elastic APM
• AWS X-Ray
• Google Cloud Trace

License

MIT