vector
Vector is an open-source, high-performance observability data pipeline written in Rust that collects, transforms, and routes logs, metrics, and traces to multiple vendors. It runs as either an agent on individual hosts or an aggregator in centralized clusters, giving teams control over observability costs and data flow.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | vectordotdev/vector |
| Owner | vectordotdev |
| Primary language | Rust |
| License | MPL-2.0 — OSI-approved |
| Stars | 22.2k |
| Forks | 2.2k |
| Open issues | 2.5k |
| Latest release | vdev-v0.3.4 (2026-06-17) |
| Last updated | 2026-07-08 |
| Source | https://github.com/vectordotdev/vector |
What vector is
Built in Rust with end-to-end pipeline architecture, Vector offers sub-component reliability (disk buffer persistence, file rotation handling, signal management) and achieves 10× throughput over competitors in benchmark scenarios. It supports unified log/metric/trace ingestion with vendor-agnostic routing and transformation via a declarative configuration model.
Get the vector source
Clone the repository and explore it locally.
git clone https://github.com/vectordotdev/vector.gitcd vector# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Configuration is declarative (TOML); plan for schema validation and testing of pipelines before production rollout; no visual UI for graph building.
- Disk buffering and backpressure are built-in but require careful tuning (disk quota, buffer policy) to avoid runaway disk usage or data loss under load.
- Component features are modular; verify that required source/sink/transform combinations are available or stable before committing to a data pipeline design.
- Agent vs. aggregator topology must be decided upfront; hybrid deployments require cross-node coordination and careful naming/routing to avoid duplicates.
- Metrics support is labeled 'beta'; test metric collection, transformation, and routing in staging before relying on for production alerting.
When to avoid it — and what to weigh
- Need Out-of-the-Box APM Correlation — Traces support is marked 'coming soon'; logs and metrics are mature, but distributed trace correlation and native span correlation are not yet production-ready.
- Require Minimal Operational Footprint — Vector requires explicit configuration (TOML/YAML) and active monitoring; it is not a zero-config sidecar like some lightweight agents.
- Prefer Single-Vendor Lock-in with Native SDKs — If your team is deeply integrated with a platform's native agent ecosystem (e.g., Splunk UF), switching to a neutral pipeline adds operational complexity unless cost/flexibility gains justify it.
- Cannot Tolerate Rust Ecosystem Dependencies — Binary is self-contained, but if your org has strict supply-chain controls on compiled binaries or zero tolerance for Rust runtime environments, review build and security posture.
License & commercial use
Vector is licensed under Mozilla Public License 2.0 (MPL-2.0), a weak copyleft license. Source code modifications must be disclosed, but derivative works can be proprietary if the software is not distributed. Redistribution of unmodified binaries is permitted.
MPL-2.0 permits commercial deployment of unmodified Vector binaries without disclosure or license obligations. However, any modifications to Vector's source code must remain under MPL-2.0 and be made available to users receiving the modified software. Consult legal counsel if you plan to bundle Vector with proprietary code or modify its internals. The project is maintained by Datadog's open-source team; no commercial support agreement data is provided in the source material.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Vector runs with privileges needed to read log files and bind network ports; review file system permissions carefully in multi-tenant environments. Credentials for sinks (API keys, TLS certs) must be stored securely (env vars, K8s secrets, vault integration); no built-in credential encryption at rest. Network transit should use TLS for sensitive data. No public security audit or CVE history is mentioned in the README; review GitHub security advisories before production use. Input validation and injection risks depend on configured transforms; test custom VRL (Vector Remap Language) scripts for security.
Alternatives to consider
Filebeat (Elastic)
Lighter-weight log forwarder with native Elasticsearch integration; lacks aggregator mode and transformation engine, better for point-to-point forwarding than complex pipelines.
Fluentd
Mature, multi-language plugin ecosystem and strong Kubernetes adoption; lower throughput than Vector but more extensive transform library and broader vendor integrations out-of-box.
Telegraf (InfluxDB)
Unified agent for metrics, logs, and events with simpler config; better for metric-first use cases and InfluxDB ecosystem, weaker for complex log transformation and multi-vendor routing.
Build on vector with DEV.co software developers
Vector offers strong performance, vendor flexibility, and cost control for observability pipelines. Assess compatibility with your data model (logs ✓, metrics beta, traces roadmap), review deployment topology (agent vs. aggregator), and validate sink support before committing to a pilot.
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vector FAQ
Does Vector guarantee data delivery or support exactly-once semantics?
Can I use Vector without modifying my application code?
What is the typical resource overhead for a Vector agent?
Is MPL-2.0 compatible with my proprietary software?
Custom software development services
From first prototype to production, DEV.co delivers software development services around tools like vector. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across open-source observability and beyond.
Evaluate Vector for Your Observability Stack
Vector offers strong performance, vendor flexibility, and cost control for observability pipelines. Assess compatibility with your data model (logs ✓, metrics beta, traces roadmap), review deployment topology (agent vs. aggregator), and validate sink support before committing to a pilot.