This company offers a Kubernetes-native logging platform that efficiently processes and queries large volumes of log data within a single pod. Their solution helps organizations reduce network egress costs and avoid sending sensitive data to external providers by managing logs locally.
Funding
Funding not disclosed
Founders
Product
Problem
Existing observability solutions like Splunk, Elasticsearch, and Loki can be expensive, slow, and complex to manage, especially when dealing with large volumes of log data. These solutions often require significant engineering effort for tuning, maintenance, and building ingestion pipelines.
Solution
SigLens is an open-source, Kubernetes-native observability platform designed for efficient processing and querying of log data at scale. It utilizes a stream-first architecture with innovative MicroIndexing and AgileAggsTrees technologies to achieve high ingestion and query speeds, even with billions of logs. SigLens supports various ingestion protocols, including Open Telemetry, Elasticsearch, Splunk HEC, and Loki, and offers a single pane of glass for logs, metrics, and traces. By optimizing storage and retrieval, SigLens reduces infrastructure costs and eliminates the need for complex pre-processing or indexing.
Target Audience
SigLens targets DevOps engineers, SREs, and platform teams who need a fast, scalable, and cost-effective observability solution for managing large volumes of log data in Kubernetes environments.
Features
- MicroIndexing technology reduces index size to 1/100th of conventional indexes
- AgileAggsTrees accelerate aggregation queries
- Supports query languages including Splunk QL and Loki LogQL
- Full-text search on any field and sub-text of log lines with wildcard and regex support
- Query Time Field Extraction for dynamic data enrichment
- Single pane of glass UI for logs, metrics, and traces
- Wide variety of ingestion protocols supported: Open Telemetry, Elasticsearch, Splunk HEC, Loki, Vector, FluentD/FluentBit, Logstash, S3/SQS/SNS, Promtail
- Dynamic columnar compressions to achieve 90% compression across diverse data types