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Coralogix

Stop Guessing Why Your Pods Are Crashing DataPrime at ingest (DPXL): See the impact of any routing decision New Explore: Faster answers, less friction, and a better way to investigate your data Explore for Spans: One View with Infinite Depth What Is Log Monitoring? Pipeline, Pitfalls, and Practices for 2026 What Is APM? A Guide to Application Performance Monitoring What Is an Incident Commander? Role, Skills, and Best Practices Managing OpenTelemetry at Scale: Why OTel Pipelines Need a Control Plane The cost of knowledge Introducing the Coralogix CLI: Headless Observability for Every Agent How the Coralogix CLI Adds Production Intelligence to Any Agent for Any Use Case Real-Time Database Monitoring: Solving Database Latency with Zero-Code eBPF Tracing Coralogix and Atlassian: Full-Stack Observability Inside the Incident Workflow Your Team is Using Claude Code. Do You Know What It’s Costing You? How Kotak811 Revolutionized Digital Banking Observability with Coralogix The Security Trifecta: Operationalizing API Protection with AWS, Wallarm, and Coralogix From Vibes to Signals: Observing Your AI Coding Workflow What “AI-Ready Data” actually means for observability teams Code Agents Need Observability DataPrime at Ingest: Fine-Grained TCO Routing with DPXL Agent-First Observability: Dynamic Data, High Cardinality, and the Business Impact Building Audit-Ready Observability for Digital Banking Debug frontend issues with AI: Real user monitoring meets the Coralogix MCP server The End of Manual Instrumentation: Scaling Observability with OTel OBI & Coralogix Evil Token: AI-Enabled Device Code Phishing Campaign Spending More, Seeing Less: How Indexing Limits Capital Markets Visibility Digital Trading: Why “Healthy Systems” Still Lose Trades From Trace to Root Cause: Mastering the new Trace Drilldown Coralogix Earns 196 Badges in G2 Spring 2026 Reports Across 15 Categories Bridging the gap between mobile experience and technical reality Monitor schema health with engine.schema_fields: Structure, Drift, and Volatility AWS GuardDuty Modules Explained: Features, Coverage, and How Customers Benefit with Coralogix The AWS logs you miss during an incident Slack, Teams & Google Chat in Your SIEM: Why Collaboration Audit Logs Matter
Coralogix Raises $200M to Scale the Observability Backbone for the Age of AI
Micha Duman · 2026-06-03 · via Coralogix

New funding accelerates a platform built for a world where AI agents and human engineers analyze, manage, and operationalize data together

Boston, MA – June 3, 2026 – Coralogix, the data and AI platform for observability, today announced it has raised $200 million in Series F funding. The round was co-led by Advent, CPPIB, and Greenfield, with participation from Brighton Park Capital, bringing total funding in Coralogix to $550M.

“AI is fundamentally changing the way enterprises operate, and observability is quickly evolving into a core layer of business intelligence,” said Alek Ferro of Advent. “Coralogix has consistently stayed ahead of that transformation, building a platform designed for the scale, speed and complexity of the agentic era. Since our initial investment, the company has continued to accelerate innovation and market adoption, reinforcing our belief that Coralogix is positioned to define the future of AI-powered observability.”

Legacy Observability Was Built for a World That No Longer Exists

The funding comes as AI is reshaping the observability market. AI-powered applications are generating telemetry data at volumes, speeds and levels of complexity far beyond what traditional monitoring platforms were designed to handle. At the same time, AI agents are becoming the primary interface for enterprise users and engineering teams: answering production questions, investigating incidents, explaining anomalies and increasingly operating production systems directly.

This shift is exposing the limitations of legacy observability tools. Platforms built for static, sampled workloads often struggle under agentic-scale telemetry volumes, generate runaway ingestion costs as data explodes, and leave engineering teams with blind spots no dashboard or alert rule was designed to close.

Coralogix was built for that shift, with an observability architecture designed for full-fidelity ingestion, real-time streaming analytics, open formats and customer-owned storage at massive scale. In the dashboard era, that architecture helped customers retain more telemetry data, reduce cost and maintain deeper operational visibility. In the AI era, it provides the data foundation intelligent systems need to investigate, reason about and operate complex production environments in real time.

“The architecture was already there,” said Ariel Assaraf, CEO and co-founder of Coralogix. “Long before the industry started talking about AI agents, we built around the idea that observability needed complete data, open access and infrastructure customers could truly control. What’s changing now is the interface layer on top of that foundation. Engineers are no longer the only consumers of observability data. AI systems are becoming operational participants themselves. This funding allows us to accelerate that transition and build the intelligence layer required for the next generation of production operations. In the AI era, dashboards are no longer the starting point for observability. Intelligence is. We didn’t reposition for this shift. We were built for it.”

The New Era of Observability

Observability workflows are evolving alongside modern infrastructure. Today, engineers still investigate incidents through dashboards, alerts and manual analysis. Increasingly, however, AI systems are handling the early stages of investigation, surfacing likely root causes, analyzing production behavior and automating operational tasks before human intervention is needed.

Coralogix is designed for that transition. Its streaming engine and schema-free architecture allow enterprises to retain and analyze vastly larger volumes of production data without the traditional tradeoff between visibility and cost. Olly, Coralogix’s built-in AI agent, along with MCP and CLI interfaces for automated workflows, all operate on the same underlying data foundation, enabling teams to move from human-led operations toward increasingly autonomous observability without changing platforms or architectures.

How will Coralogix use this funding?

The Series F builds on Coralogix’s $115 million Series E in 2025 and comes at a period of strong commercial growth and customer adoption. Trusted by more than 5,000 customers worldwide, like IBM, Tradeweb, and Jfrog, the platform processes petabytes of production data daily across eight regions including GovCloud for public sector and regulated industries, and is continuing to expand across fintech, AI infrastructure, cybersecurity and cloud-native enterprises.

The company will use this funding to accelerate growth across three core areas:

AI-Native Observability: accelerating development of agentic AI capabilities across Olly, MCP and CLI to help enterprises investigate, automate and operate at machine speed across increasingly complex AI-driven environments.

Telemetry Data Infrastructure: expanding Coralogix’s schema-free telemetry data lake architecture to support real-time processing, long-term data retention and open-format analytics at enterprise and AI scale.

Global Enterprise Expansion: scaling platform adoption among organizations modernizing beyond legacy observability tools as demand grows for AI-ready architectures, complete telemetry fidelity and customer-owned data infrastructure.

Observe the future by exploring the Coralogix AI Center to discover, monitor, and safeguard your entire ecosystem. Book a demo