Coralogix is an observability platform that helps software teams watch logs, metrics, traces, and now AI systems in one place. The Coralogix observability platform has raised $200 million in a Series F round as companies scramble to keep autonomous software from breaking in ways humans can’t quickly see. That’s the core pain here. The more AI agents write code, investigate incidents, and take action on their own, the less useful old-school monitoring dashboards start to feel. Founded in 2014 by Ariel Assaraf and Yoni Farin, the company was built in Israel and now runs from Boston. The new capital will speed up AI product work, security features, and international expansion.
The round lands just 11 months after Coralogix’s $115 million Series E. This new financing values the company at $1.6 billion post-money and brings total funding to $550 million. Advent and the Canada Pension Plan Investment Board led the round, with Greenfield Partners and Brighton Park Capital also joining. That’s not a bridge round. It’s a statement that investors think observability is getting rewritten by AI, not just lightly upgraded.
What is Coralogix and how does its observability platform work?
At a practical level, Coralogix takes in telemetry from applications and infrastructure, stores that data in the customer’s cloud, and lets teams query logs, metrics, and traces through one syntax layer called DataPrime. It also supports OpenTelemetry. That matters because large engineering teams don’t want to rip out existing instrumentation just to adopt a new monitoring stack. The pitch is simple: bring the data in once, analyze it fast, and stop paying premium rates to keep every low-value log hot forever.
That workflow has become more AI-first. Coralogix’s agent, Olly, lets engineers ask plain-language questions instead of stitching together dashboards and hand-written queries. Olly can pull from logs, metrics, traces, and alerts. It surfaces anomalies, explains what happened in simple language, and in tougher incidents pushes the investigation forward itself rather than waiting for one prompt at a time. That’s a real change in how observability tools are used. It turns the product from a place you browse into something you interrogate.
Coralogix has gone further than chat on top of telemetry. Its AI Center adds model and agent monitoring and guardrails. It also includes tooling around performance, security posture, and tracing AI behavior across the application stack. A lot of that capability accelerated after Coralogix bought AI observability startup Aporia in December 2024. That gave it more depth in issues like hallucinations, prompt injection, drift, and other failure modes regular APM tools weren’t designed for.
Cost is still central to the product story. Its Cost Optimizer routes high-value data to real-time analysis while pushing lower-value logs to cloud storage. That’s a direct shot at one of the biggest customer complaints in observability: bills that grow faster than software traffic. For buyers, the before-and-after is pretty clear. Before, they’re juggling dashboards, index choices, and storage tradeoffs. After, they’re trying to query one system and let AI do more of the digging.
Who founded the Coralogix observability platform and how fast is it growing?
The founding story
Coralogix started in 2014 after Assaraf spent years dealing with messy log data on large software projects and got tired of how slow and painful debugging had become. In earlier interviews, he described working after his Israeli army service on big multinational deployments, where finding bugs and shipping fixes across sprawling systems was a constant headache. That frustration became the company. The original problem wasn’t glamorous. It was operational chaos.
Why the founders fit this market
Assaraf is still CEO and remains the commercial face of the company. Farin, Coralogix’s CTO and co-founder, brings more than 25 years of experience in software development, big data, and distributed systems. That split makes sense for this market. One founder lived the pain of incident response and tooling sprawl. The other built the technical backbone for a system that has to process huge volumes of machine data in real time.
Traction and the new Series F
Coralogix serves more than 5,000 customers worldwide, including IBM, Tradeweb, and JFrog. Revenue grew more than 60% over the past year. About 30 customers now spend over $1 million annually, and Coralogix passed $100 million in annualized revenue more than a year ago. It employs more than 600 people globally, with roughly 100 in India, which has become its third-largest office after the U.S. and Israel.
The fresh $200 million round comes with some urgency behind it. Coralogix didn’t raise because it needed runway. It raised because it wanted speed. Assaraf put it bluntly: “In the AI era, execution and speed matter more than any point-in-time valuation.” That’s the logic behind stacking a Series F on top of a large Series E less than a year later.
Where Coralogix sits against Datadog, New Relic, and Splunk
This is still a heavyweight market. Datadog ended 2025 with $3.43 billion in annual revenue. New Relic is pushing AI monitoring inside its broader observability suite. Dynatrace markets end-to-end AI and LLM observability, and Splunk has Cisco’s balance sheet behind it after the March 18, 2024 acquisition. Coralogix isn’t the biggest player in the category. It’s trying to be the one that feels more native to an AI-agent world.
Its differentiation is pretty focused. Coralogix argues that customers shouldn’t have to choose between cost and coverage. It pairs that with unified querying, in-stream analytics, AI-native workflows through Olly, and tighter AI observability plus security controls in one stack. Investors are backing that combination — not just another logs company, but a vendor betting that software operations will be queried by agents as much as by humans.
Why does the Coralogix funding round matter?
Because it changes the company’s ambition level.
Late-stage rounds often look defensive. This one looks offensive. Coralogix will put the money into AI-focused products, security offerings, and global expansion, while the business works toward profitability over the next few years and starts operating with the discipline of a public company. Assaraf didn’t commit to an IPO date. Promising a listing timeline this early would be noise, not strategy.
There’s also a product signal buried in the customer behavior. More than half of Coralogix’s enterprise customers now use either Olly or their own AI models through CLI and agentic interfaces to investigate incidents and query operational data. That’s why Assaraf’s line about “The interface layer is slowly getting eroded” matters. If that shift holds, the winner in observability might be the company that becomes easiest for machines to use, not just for humans to click through.
For customers, the bet is that Coralogix can keep incident response understandable even as systems get less deterministic. For investors, the thesis is sharper: if AI agents create more software actions, they also create more failure points, more telemetry, and more demand for tooling that can explain weird behavior fast. That doesn’t guarantee Coralogix wins. But it does explain why this round arrived now, and why it arrived at this size.
How big is the observability market for AI agents?
It’s already a real market, and it’s growing fast enough to attract serious capital. Grand View Research estimates the global observability tools and platforms market was worth $2.7 billion in 2023 and could reach about $5.4 billion by 2030. North America held the biggest regional share in 2023, which fits Coralogix’s Boston-centered enterprise push even though the company’s roots are in Israel.
But the better signal is adoption, not just top-down TAM math. Gartner said in May 2026 that 40% of organizations deploying AI will use dedicated AI observability tools by 2028 to monitor model performance, bias, and outputs. That’s the category shift Coralogix is chasing. Observability used to mean keeping distributed apps alive. Now it also means figuring out why an AI agent made a strange choice, burned too many tokens, leaked risk, or quietly drifted off course.
That’s why this round feels bigger than one startup financing. It suggests observability is being recast as a control layer for autonomous software. The next thing to watch isn’t just revenue growth. It’s whether AI-native incident workflows become standard enough to make the Coralogix observability platform look early rather than merely timely.
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FAQ
- What is the Coralogix funding round announced in 2026? Coralogix raised $200 million in a Series F round announced on June 3, 2026. The financing valued the company at $1.6 billion post-money and Advent and the Canada Pension Plan Investment Board led it, with Greenfield Partners and Brighton Park Capital also participating.
- How does Coralogix work for engineering teams? Coralogix ingests telemetry like logs, metrics, traces, and AI signals, then lets teams query and investigate that data through a unified platform. Its Olly agent adds natural-language incident investigation. The broader platform includes features like DataPrime querying, cost controls, and AI observability tooling for model and agent behavior.
- Who founded Coralogix? Coralogix was founded in 2014 by Ariel Assaraf and Yoni Farin. Assaraf is the CEO and came to the idea after years of wrestling with unmanageable log data, while Farin, the CTO, brought deep experience in distributed systems, big data, and software architecture.
- Is Coralogix an AI company or an observability company? It’s primarily an observability company, but one that’s increasingly building for AI-heavy software environments. That distinction matters because Coralogix still competes with vendors like Datadog, New Relic, Dynatrace, and Splunk, even as it adds AI observability, guardrails, and agent-focused investigation workflows on top of classic monitoring.
