Vector
About Vector
Vector is a tool for building observability pipelines. It collects, transforms, and routes logs and metrics. It is built in Rust. It supports logs and metrics as data types. It is packaged as a single binary.
Used for
- Routing Datadog Agent logs to Datadog with sensitive data redaction.
- Moving data from a Kafka topic to Elasticsearch.
- Sending Kubernetes logs to AWS S3.
- Forwarding Splunk HEC data to Datadog.
From Vector's own website, read 2026-10-24. How we build listings.
How to evaluate Log Management & Analytics
This is how the CIOPages Research Team evaluates this category. It is not an assessment of Vector. It comes from our Log Management & Analysis buyer guide.
25%
Cost & Tiering Model at Volume
Unit of pricing (per-GB ingest, compute/SVC, credits, host, consumption) and how the bill behaves as volume grows; hot/warm/cold/frozen or archive tiers, index-free or low-cost retention tiers, rehydration cost and latency, and ingest controls or sampling to cap spend
20%
Search, Query & Analytics Power
Full-text vs. label-only vs. schema-on-read; query language depth (SPL, ES|QL/KQL, LogQL, DQL, SQL) and learning curve; performance on cold and high-cardinality data; aggregations, pattern detection, and live-tail for real-time troubleshooting
20%
Pipeline, Ingestion & OpenTelemetry
Agent footprint and collector strategy; native OpenTelemetry/OTLP ingest; parsing, enrichment, masking, routing, and reduction before storage; ability to fan data out to multiple destinations and to swap backends without re-instrumenting sources
15%
Security, Compliance & Retention
RBAC/SSO, field-level masking of sensitive data, immutable/WORM audit retention, and tamper-evident logging; SOC 2 / ISO 27001 / FedRAMP coverage and data residency; SIEM or threat-detection content if logs feed security
10%
Observability Correlation & Architecture
Whether logs sit standalone, inside an observability suite (correlated with metrics and traces), or inside the SIEM; trace-to-log pivots, unified context, and how cleanly it slots beside tooling you already run
10%
Operations, Scale & AI Assist
Self-managed vs. SaaS operational burden, multi-tenant and multi-cluster scale, ingest spikes and back-pressure handling; API/IaC coverage; and AI-assisted features such as anomaly detection, log clustering/patterns, and natural-language query
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Quick Facts
vector.devCategoryObservability & Monitoring
SubcategoryLog Management & Analytics
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