About
Vector is a lightweight, ultra-fast observability pipeline tool designed to give engineering teams full control over their observability data. Built in Rust for maximum performance and memory safety, Vector can ingest logs and metrics from 46+ sources—including Kafka, Kubernetes, AWS Kinesis, Splunk HEC, and Datadog Agent—transform them using the powerful Vector Remap Language (VRL), and route them to any downstream sink such as Elasticsearch, AWS S3, Datadog, and more. Vector supports three core deployment topologies: distributed (agent on each host), centralized (single aggregation layer), and stream-based (Kafka-style pipelines). This flexibility makes it suitable for startups running on a single server and large enterprises managing complex multi-region infrastructure alike. Configuration is expressed in simple YAML, TOML, or JSON, making pipelines easy to read, version-control, and iterate on. The Vector Remap Language provides a safe, purpose-built scripting environment for complex transformations—like redacting PII, parsing JSON, enriching events with metadata—without the overhead of a general-purpose runtime. Because Vector ships as a single static binary with no external runtime or dependencies, it installs instantly on x86_64 and ARM64/v7 architectures. It is fully open source, vendor-neutral, and designed to be lock-in free, ensuring your observability stack remains future-proof regardless of which backend vendors you choose.
Key Features
- Ultra-Fast Rust Performance: Built in Rust, Vector is memory-efficient and blistering fast, designed to handle the most demanding observability workloads without bottlenecks.
- Unified Logs & Metrics: Supports both logs and metrics in a single pipeline, eliminating the need for separate collection agents and reducing operational complexity.
- 46+ Sources and Sinks: Integrates with a wide ecosystem including Kafka, Kubernetes, AWS Kinesis, Splunk HEC, Datadog, Elasticsearch, AWS S3, and many more out of the box.
- Vector Remap Language (VRL): A purpose-built, safe scripting language for transforming observability data—parse JSON, redact PII, enrich events—without a heavy general-purpose runtime.
- Flexible Deployment Topologies: Deploy as a daemon on every host, a centralized aggregator, or a stream-based pipeline—Vector adapts to your infrastructure architecture.
Use Cases
- Collecting Kubernetes pod logs and routing them to AWS S3 for long-term archival with gzip compression and JSON encoding.
- Ingesting Kafka topic data, parsing JSON payloads, and forwarding enriched events to Elasticsearch for full-text search and analysis.
- Redacting personally identifiable information (PII) such as Social Security Numbers from Datadog Agent logs before they reach any downstream storage.
- Replacing a fragmented set of per-vendor log agents with a single Vector deployment that fans out to multiple sinks simultaneously.
- Building a centralized log aggregation layer that normalizes log formats from heterogeneous microservices before indexing them in a unified observability platform.
Pros
- Truly Open Source & Vendor Neutral: Vector is fully open source with no vendor lock-in, meaning you can freely switch backends and avoid being tied to proprietary observability platforms.
- Zero-Dependency Single Binary: Ships as a single static binary for x86_64 and ARM64/v7, making installation trivial and eliminating dependency hell across environments.
- High Configurability: YAML, TOML, or JSON configuration combined with VRL transforms gives teams full control over complex pipeline logic without writing custom code.
- Battle-Tested Performance: Rust's memory safety guarantees and zero-cost abstractions make Vector reliable under high-throughput production loads with minimal resource overhead.
Cons
- Self-Hosted Responsibility: As an open-source tool, Vector requires users to handle deployment, scaling, upgrades, and operational maintenance themselves with no managed service option.
- Learning Curve for VRL: The Vector Remap Language is powerful but has its own syntax and concepts that require time to learn, especially for teams new to custom pipeline transforms.
- Configuration Complexity at Scale: Managing large, multi-topology Vector deployments with many sources and sinks can become complex without proper configuration management tooling in place.
Frequently Asked Questions
What is Vector and what problem does it solve?
Vector is an open-source observability pipeline tool that collects, transforms, and routes logs and metrics. It solves the problem of managing multiple fragile, vendor-specific agents by replacing them with a single, high-performance, vendor-neutral binary.
Is Vector free to use?
Yes, Vector is completely free and open source under the MPL-2.0 license. There are no licensing fees, and you can use it in production without cost.
What sources and destinations does Vector support?
Vector supports 46+ sources including Kafka, Kubernetes logs, AWS Kinesis, Splunk HEC, Datadog Agent, AMQP, and more. Sinks include Elasticsearch, AWS S3, Datadog, and many other popular observability backends.
How do I install Vector?
Vector can be installed with a single curl command: `curl --proto '=https' --tlsv1.2 -sSfL https://sh.vector.dev | bash`. It also supports package managers and manual installation for various platforms and architectures.
What is the Vector Remap Language (VRL)?
VRL is a purpose-built, safe scripting language included with Vector for transforming observability data. It lets you parse JSON, redact sensitive fields like SSNs, enrich events, and handle complex routing logic directly within your pipeline configuration.
