Logging

3 open source tools compared. Sorted by stars — scroll down for our analysis.

Tool	Stars	Velocity	Language	License	Score
Loki Horizontally-scalable, multi-tenant log aggregation	27.9k	+51/wk	Go	GNU Affero General Public License v3.0	71
Vector High-performance observability data pipeline	21.5k	+57/wk	Rust	Mozilla Public License 2.0	76
Fluentd Unified logging layer	13.5k	+7/wk	Ruby	Apache License 2.0	79

Our Analysis

Loki27.9k★

Loki is Grafana's answer to the ELK stack — a log aggregation system that indexes only metadata (labels), not full log content, making it dramatically cheaper to run. If you already use Grafana for dashboards, Loki plugs in as the native logging backend with the same label-based query model as Prometheus. For teams already in the Grafana ecosystem, Loki is the natural choice. It's 10x cheaper to operate than Elasticsearch because it stores compressed log chunks in object storage instead of maintaining full-text indexes. Elasticsearch/ELK is the powerful but expensive alternative with superior search. Datadog is the fully-managed commercial option. The catch: AGPL license means your modifications must be open-sourced. Loki's label-based querying (LogQL) can't do the arbitrary full-text searches that Elasticsearch handles effortlessly. You must know your labels upfront. High-cardinality labels kill performance. And if you need to search across terabytes of logs for a random string, Elasticsearch is still the right tool. Loki is for structured, label-driven log analysis.

Vector21.5k★

Vector is the observability pipeline that makes Fluentd and Logstash feel like relics. Written in Rust, it handles logs and metrics with 5-10x better throughput, half the memory, and a type-safe transformation language (VRL) that catches errors at compile time instead of in production at 3am. If you're shipping logs in a cloud-native stack, Vector should be your first choice. It replaces Fluentd (CNCF standard but slow, Ruby-based) and Logstash (Java, heavy, Elastic-coupled) while being genuinely easier to configure. Fluent Bit is the lightweight alternative, but Vector beats it 2x on heavy workloads. Datadog and Splunk are the commercial options you're probably trying to escape. The catch: Vector is MPL-2.0 licensed and backed by Datadog (who acquired Timber/Vector). The CNCF crowd may prefer Fluent Bit for vendor neutrality. And if your entire stack is Elastic, Logstash's native integration is hard to replicate.

Fluentd13.5k★

Fluentd is the CNCF-standard log collector that became infrastructure's duct tape. With 1,000+ plugins, it connects virtually any log source to any destination. It's the "universal logging layer" that sits between your apps and whatever observability backend you picked this year. If you're in a Kubernetes shop that values CNCF-standard tooling and vendor neutrality, Fluentd is the safe choice. Vector (Rust, Datadog-backed) is 5-10x faster with lower memory and a better configuration language — it's what you'd pick if starting fresh. Fluent Bit is Fluentd's lighter sibling, purpose-built for edge and container sidecar deployments. Logstash is the Elastic-coupled option. The catch: Fluentd is showing its age. The Ruby core means performance lags behind Rust-based alternatives, and recent benchmarks show it dropping logs before hitting 10K logs per second under pressure. The plugin ecosystem is vast but uneven — quality varies wildly between maintained and abandoned plugins. For new deployments, Vector or Fluent Bit are objectively better choices.