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vector

vectordotdev/vector

A high-performance observability data pipeline

GitHub Website

GraphCanon updated 1mo · GitHub synced 1mo · 25 views this month

22k stars2.2k forksLast push 1mo Rust MPL-2.0

Decision brief

Vector is a high-performance observability data pipeline that supports logs, metrics, and traces in Rust. It enables cost reduction, enhanced security, and data enrichment for unified management.

Good fit when

When you need an end-to-end (agent & aggregator) solution to manage all your logs and metrics.
To consolidate multiple agents into a single tool for reducing agent fatigue and lowering costs.

Avoid when

If you require immediate trace support, as it is still in the beta stage.
When your setup only needs basic data observability and doesn't benefit from advanced enrichment or security features offered by Vector.

Observed Jul 11, 2026 · Source: enrich:decision_facts

vector alternatives Trust report

vector vs lmnr

Verify the decision

Maintenance and security

Full trust report

Maintenance: Very active (1d since push); As of 1mo
Provenance: Not a fork · Organization account; As of 1mo
Security (OSV): No lockfile; As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

cargo add vector

crates.io

How it fits your stack(15)

Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.

Alternative

lmnr

Vector serves as a high-performance data pipeline for collecting, transforming, and routing logs and metrics, while lmnr is an observability platform specifically designed for AI agents that includes features like tracing, signal monitoring, and custom dashboards. The 'alternative' relationship indicates that both tools can be used to enhance the observability of systems, though lmnr focuses more狭

alternative

Integrates

meilisearch

Vector can be used to route logs and metrics to MeiliSearch, integrating into a complete observability solution where MeiliSearch handles search functionalities of the data collected by Vector.

integrates

langfuse

Vector and Langfuse both focus on observability in the context of AI and data pipelines. Vector is for high-performance observability data pipelines, whereas Langfuse provides a platform for evaluating, monitoring, and managing LLMs and their associated metrics. Together they can provide detailed insights into performance and operational health.

integrates

mlflow

MLflow is a platform to manage the end-to-end machine learning lifecycle, including experimentation. Vector can enhance MLflow by providing high-performance observability and stream processing capabilities that can help in monitoring ML models' performance over time.

integrates

graphiti

Graphiti builds real-time knowledge graphs, which can be related to Vector's ability to process streams of data for observability. However, they do not directly integrate or explicitly depend on one another.

gitleaks

Gitleaks secures codebases from leaks, while Vector deals with managing and securing observability data pipelines which can be part of the broader CI/CD process.

pinpoint

Vector focuses on high-performance observability data pipelines, which is similar to Pinpoint’s purpose as an APM tool that handles monitoring and tracing.

Relationship graph

Optional deeper exploration of typed edges and category neighbours.

AlternativeIntegratesRelated

AlternativeIntegratesRelated

Similar tools

Same-category neighbours not already linked as typed edges.

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Vector is a robust, open-source tool for collecting, transforming, and routing observability data. It supports logs and metrics with ongoing support for traces and aims to provide significant cost reductions and enhanced security.

Capability facts

Languages: rust
Source: github.language · Jul 19, 2026

README

Quickstart • Docs • Guides • Integrations • Chat • Download • Rust Crate Docs

What is Vector?

Vector is a high-performance, end-to-end (agent & aggregator) observability data pipeline that puts you in control of your observability data. [Collect][docs.sources], [transform][docs.transforms], and [route][docs.sinks] all your logs and metrics to any vendors you want today and any other vendors you may want tomorrow. Vector enables dramatic cost reduction, novel data enrichment, and data security where you need it, not where it is most convenient for your vendors. Additionally, it is open source and up to 10x faster than every alternative in the space.

To get started, follow our [quickstart guide][docs.quickstart] or [install Vector][docs.installation].

Vector is maintained by Datadog's Community Open Source Engineering team.

Principles

Reliable - Built in [Rust][urls.rust], Vector's primary design goal is reliability.
End-to-end - Deploys as an [agent][docs.roles#agent] or [aggregator][docs.roles#aggregator]. Vector is a complete platform.
Unified - [Logs][docs.data-model.log], [metrics][docs.data-model.metric] (beta), and traces (coming soon). One tool for all of your data.

Use cases

Reduce total observability costs.
Transition vendors without disrupting workflows.
Enhance data quality and improve insights.
Consolidate agents and eliminate agent fatigue.
Improve overall observability performance and reliability.

Community

Vector is relied on by startups and enterprises like Atlassian, T-Mobile, Comcast, Zendesk, Discord, Fastly, CVS, Trivago, Tuple, Douban, Visa, Mambu, Blockfi, Claranet, Instacart, Forcepoint, and [many more][urls.production_users].
Vector is downloaded over 100,000 times per day.
Vector's largest user processes over 500TB daily.
Vector has over 500 contributors and growing.

Documentation

All user documentation is available at vector.dev/docs.

Other Resources:

[Vector Calendar][urls.vector_calendar]
Policies:
- [Code of Conduct][urls.vector_code_of_conduct]
- [Contributing][urls.vector_contributing_policy]
- [Privacy][urls.vector_privacy_policy]
- [Releases][urls.vector_releases_policy]
- [Versioning][urls.vector_versioning_policy]
- [Security][urls.vector_security_policy]

Comparisons

Performance

The following performance tests demonstrate baseline performance between common protocols with the exception of the Regex Parsing test.

Test	Vector	Filebeat	FluentBit	FluentD	Logstash	SplunkUF	SplunkHF
TCP to Blackhole	86mib/s	n/a	64.4mib/s	27.7mib/s	40.

For agents

This page has a .md twin and JSON over the API.

/tools/vectordotdev-vector.md GET /api/graphcanon/tools/vectordotdev-vector GET /api/graphcanon/graph?tool=vectordotdev-vector

vector

Decision brief

Good fit when

Avoid when

Verify the decision

Maintenance and security

Install

How it fits your stack(15)

Alternative

Integrates

Related

Relationship graph

Similar tools

Evidence and technical details

Overview

Capability facts

Categories

Tags

README

What is Vector?

Principles

Use cases

Community

Documentation

Comparisons

Performance

For agents