Coralogix
APICoralogix is a full-stack observability platform for logs, metrics, and traces with streaming analytics, anomaly detecti
coralogix.comLast updated: April 2026
Coralogix is a full-stack observability platform for logs, metrics, and traces with streaming analytics, anomaly detection, and cost-effective data tiering.
About
Coralogix is a full-stack observability platform that processes and analyzes logs, metrics, and traces in real time, providing engineering teams with the visibility needed to maintain reliable, performant applications and infrastructure. Distinguished by its streaming analytics architecture and intelligent data tiering, Coralogix delivers comprehensive observability capabilities at a cost structure that scales efficiently with data volume.
The streaming analytics architecture of Coralogix processes all incoming telemetry data in real time using a stateful stream processing engine, without requiring data to be indexed and stored before it can be queried. This streaming approach enables sub-second alerting, live log tailing, and real-time anomaly detection that respond to events as they happen rather than after a storage and indexing delay.
The data tiering model in Coralogix is one of its most commercially significant features. Not all logs and metrics have the same value or access frequency, and storing everything in a hot, fully indexed tier is expensive. Coralogix automatically classifies incoming data into Frequent (hot), Monitoring (warm), and Compliance (cold) tiers based on configurable policies. Frequent data is fully indexed and queryable in real time. Monitoring data is compressed and available for analysis. Compliance data is archived in the customer's own S3 bucket at minimal cost. This tiering can reduce observability costs significantly compared to single-tier platforms.
The machine learning-based Coralogix Loggregation feature automatically identifies, groups, and counts similar log messages, transforming noisy log streams into actionable insights. Instead of drowning in millions of individual log lines, teams see the distinct patterns, their frequencies, and their trends, making it much faster to identify emerging error patterns and anomalies.
APM (Application Performance Monitoring) in Coralogix ingests distributed traces from applications instrumented with OpenTelemetry, Jaeger, or Zipkin, providing service maps, request latency analysis, trace search, and correlation between traces and related logs and metrics. This three-pillar correlation is critical for efficient incident investigation.
The RUM (Real User Monitoring) capability captures performance data from real browser sessions, providing Core Web Vitals metrics, session replay, error tracking, and user journey analysis that complement server-side observability with a complete picture of the end-user experience.
Coralogix integrates with Kubernetes, AWS, Azure, Google Cloud, and popular applications through a comprehensive set of collectors, agents, and direct API integrations. The OpenTelemetry compatibility makes it straightforward to adopt alongside existing instrumentation.
Positioning
Coralogix is the full-stack observability platform that breaks the cost-quality tradeoff plaguing the monitoring industry. While Datadog and Splunk charge per GB of ingested data — incentivizing teams to drop logs and metrics they might need later — Coralogix's Streama technology analyzes data in-stream before storage, allowing teams to monitor and alert on everything while only storing what matters. This architectural innovation typically reduces observability costs by 70% compared to traditional platforms.
The platform combines logs, metrics, traces, and security data in a unified experience with real-time alerting, ML-powered anomaly detection, and a proprietary query engine. Coralogix offers three data storage tiers — hot, warm, and cold (backed by S3/GCS) — with the ability to query across all tiers and reindex archived data on demand. This means organizations can maintain full visibility into their systems without the agonizing choice of which data to throw away.
What You Get
- Streama Pipeline
In-stream data analysis that processes, alerts, and extracts insights from all telemetry data before it reaches storage — enabling monitoring without ingestion-based costs. - Log Analytics
Full log management with parsing, enrichment, querying, and visualization — with three storage tiers (hot, warm, archive) and the ability to reindex archived data on demand. - APM & Distributed Tracing
OpenTelemetry-native distributed tracing with service maps, latency analysis, error tracking, and automatic correlation with logs and metrics. - Custom Dashboards
Flexible visualization with support for logs, metrics, traces, and combined data sources in a single dashboard with templating and variable support. - Alerting & Anomaly Detection
ML-powered anomaly detection, ratio-based alerts, flow alerts for complex conditions, and integration with PagerDuty, Slack, OpsGenie, and webhooks. - TCO Optimizer
Automated data classification tool that recommends optimal storage tiers for each log source based on usage patterns, maximizing value per dollar spent.
Core Areas
Cost-Optimized Observability
Multi-tier architecture that separates data analysis from storage, allowing organizations to monitor everything while controlling costs through intelligent data tiering.
Full-Stack Monitoring
Unified platform for logs, metrics, traces, and security events with cross-signal correlation and a single query language across all data types.
Cloud Security Monitoring
Real-time security analytics with Kubernetes audit log analysis, cloud trail monitoring, and compliance dashboards built on the same observability platform.
Why It Matters
Observability costs have become unsustainable for many organizations, with some spending more on monitoring than on the infrastructure being monitored. The industry's per-GB pricing model creates a perverse incentive to reduce visibility — the exact opposite of what observability should provide. Coralogix's streaming architecture fundamentally changes this equation by decoupling analysis from storage.
For engineering teams, this means no more painful decisions about which logs to drop or which metrics to aggregate away. Every piece of telemetry can be analyzed and alerted on in real time, while storage costs are managed through intelligent tiering rather than data loss. The result is better visibility at a fraction of the cost.
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