Winston vs Pino: Choosing a Node.js Logger in 2026

Every Node.js application needs a logger. console.log works until it doesn't — the moment you need structured output, log levels, or output routing, you need a logging library. Winston and Pino are the two dominant choices, and they make fundamentally different trade-offs.

Winston prioritizes flexibility. It has a plugin architecture with 80+ community transports, custom formatters, and a configuration model that handles nearly any output requirement. It's the most popular Node.js logger by npm downloads.

Pino prioritizes performance. It serializes JSON logs 5–10x faster than Winston by avoiding synchronous string formatting in the hot path and offloading I/O to worker threads. It's the default logger for Fastify.

Both produce structured JSON logs. Both support log levels. Both work with Express and any other Node.js framework. The right choice depends on your throughput requirements, operational complexity, and existing infrastructure.

For how structured logging fits into the broader monitoring and logging architecture — metrics, alerts, log aggregation, and how they connect — see our companion guide.

Winston: the flexibility choice

Basic setup

import { createLogger, format, transports } from "winston";

const logger = createLogger({
  level: "info",
  format: format.combine(
    format.timestamp(),
    format.errors({ stack: true }),
    format.json()
  ),
  defaultMeta: { service: "order-service" },
  transports: [
    new transports.Console(),
    new transports.File({ filename: "error.log", level: "error" }),
    new transports.File({ filename: "combined.log" }),
  ],
});

logger.info("Order created", { orderId: "ord_123", userId: "usr_456" });

Output:

{
  "level": "info",
  "message": "Order created",
  "orderId": "ord_123",
  "userId": "usr_456",
  "service": "order-service",
  "timestamp": "2026-06-07T12:00:00.000Z"
}

Strengths

Transport ecosystem. Winston's transport architecture is its defining feature. Transports are output destinations — console, file, HTTP, Elasticsearch, CloudWatch, Datadog, Sentry, Slack, and dozens more. Community transports cover nearly every log destination.

Custom formatters. The format pipeline lets you compose transformations: add timestamps, colorize console output, filter fields, redact sensitive data, and restructure log objects. Formatters are composable — format.combine() chains them.

Querying and profiling. Winston has built-in support for log querying (searching persisted logs) and profiling (timing operations). logger.profile("request") starts a timer; calling it again with the same ID logs the duration.

Weaknesses

Synchronous serialization. Winston serializes log objects in the calling thread. For high-throughput services (10,000+ log events/second), this adds measurable latency to your request handling. The serialization cost is small per log line (~1–5 microseconds) but compounds at scale.

Complex configuration. The format pipeline, transport configuration, and exception handling have many options. Getting the right combination for production use (JSON output, error stack traces, no duplicate console output, proper file rotation) requires reading the docs carefully.

Larger dependency tree. Winston pulls in logform, triple-beam, readable-stream, and the transport packages. The install footprint is larger than Pino's.

Pino: the performance choice

Basic setup

import pino from "pino";

const logger = pino({
  level: "info",
  base: { service: "order-service" },
  timestamp: pino.stdTimeFunctions.isoTime,
});

logger.info({ orderId: "ord_123", userId: "usr_456" }, "Order created");

Output:

{
  "level": 30,
  "time": "2026-06-07T12:00:00.000Z",
  "service": "order-service",
  "orderId": "ord_123",
  "userId": "usr_456",
  "msg": "Order created"
}

Note: Pino uses numeric log levels by default (30 = info, 40 = warn, 50 = error). You can configure human-readable level strings with formatters.level.

Strengths

Serialization speed. Pino generates JSON output 5–10x faster than Winston. It achieves this by avoiding the format pipeline — instead of transforming log objects through a chain of formatters, Pino serializes directly to JSON with custom fast serializers. The benchmarks show Pino processing 30,000+ log lines/second versus Winston's ~6,000.

Worker-thread transports. Pino's transport system (pino.transport()) runs in a separate worker thread. The main thread writes log lines to a stream, and the transport thread reads from the stream and delivers to the destination. This means transport failures (a down Elasticsearch cluster, a full disk) don't block your application's event loop.

const logger = pino({
  transport: {
    targets: [
      { target: "pino-pretty", level: "info" },
      { target: "pino-elasticsearch", level: "info",
        options: { node: "http://elasticsearch:9200" } },
    ],
  },
});

Child loggers. logger.child({ requestId: "req_789" }) creates a child logger that automatically includes the request ID in every log line. This is cheap — Pino implements child loggers as prototype chain extensions, not copies. Creating 10,000 child loggers per second has negligible overhead.

Small dependency footprint. Pino has minimal dependencies. The core package is ~80KB installed.

Weaknesses

Fewer built-in transports. Pino's transport ecosystem is smaller than Winston's. Common destinations (files, pretty-printing, Elasticsearch) are well-covered, but niche transports (CloudWatch, Datadog, Slack) may require writing custom transport functions.

Numeric levels by default. The default numeric level output ("level": 30) is efficient but less readable when scanning raw logs. You can configure string levels, but it requires explicit formatter setup.

Pretty-printing requires a separate package. Pino's core output is machine-readable JSON. For human-readable development output, you need pino-pretty (as a dev dependency or transport). Winston includes colorized console output in its formatter pipeline.

Benchmark comparison

Based on Pino's published benchmarks (reproducible on a standard Node.js setup):

The numbers vary by machine and payload size, but the ratio is consistent: Pino is 4–5x faster than Winston for JSON serialization. The gap widens with larger log objects (more keys, nested structures).

For most applications processing fewer than 1,000 requests/second, the difference is negligible — both loggers add sub-millisecond overhead per log call. The performance difference matters for high-throughput services (API gateways, streaming processors, real-time pipelines) where logging overhead becomes measurable in p99 latency.

Feature comparison

OTel integration

Both loggers work with the OpenTelemetry log bridge API. The OTel Node.js SDK can capture log events from either logger and export them alongside traces and metrics through the OTel Collector.

For Winston, the @opentelemetry/instrumentation-winston package auto-instruments Winston to inject trace context (trace ID, span ID) into log records.

For Pino, the @opentelemetry/instrumentation-pino package does the same. When a log line is emitted inside an active span, the trace ID is automatically added — enabling the log-to-trace correlation that makes distributed tracing practical.

Decision table

The practical recommendation

For new Node.js projects in 2026, start with Pino. The performance headroom, worker-thread transports, and minimal dependency footprint align with modern Node.js best practices. The ecosystem has matured — the transport gap with Winston has narrowed, and pino-pretty covers the development ergonomics.

For existing projects using Winston, don't migrate unless logging overhead is a measured problem. Winston works well for the vast majority of applications. The migration effort (different API, different format pipeline, different transport configuration) isn't justified by performance gains you won't notice below 1,000 req/s.

Whichever logger you choose, monitor the services producing those logs with health checks at app.devhelm.io. Structured logging is only useful if the services are up — and when they go down, an external monitor catches it before your log pipeline falls silent.

Originally published on DevHelm.