Operations | Monitoring | ITSM | DevOps | Cloud

Coralogix Real User Monitoring has spent years collecting full-fidelity user sessions: every session and event processed in stream, without sampling and without prior indexing, at rest in cloud object storage you own. Today that data does a second job. Product Analytics brings heatmaps, funnels, and pathways to the RUM sessions you already send, with no second SDK to install. One dataset now answers what your users did and why it happened.

FinOps spent a decade making cloud spend predictable, and teams now point the same discipline at a workload that behaves nothing like a virtual machine. In the FinOps Foundation’s State of FinOps 2026 survey, drawn from 1,192 practitioners representing more than $83 billion in annual cloud spend, 98% now manage AI spend, up from 31% two years earlier. The main driver for this was agents.

Coralogix has spent years building metrics infrastructure that handles high cardinality and high dimensionality without flinching, with governance, usage visibility, and cost optimization built into the platform, and recognized industry delivery to show for it. Today that infrastructure takes its biggest step yet. We have rebuilt the metrics engine from the ground up, with a new pricing model, a set of new capabilities, and a tripled fair usage allowance to enjoy them in.

We are absolutely thrilled to share with you all that Coralogix has been recognized as a Leader in the Gartner Magic Quadrant for Observability Platforms. When we architected Coralogix around in-stream processing, open-format storage, and index-free query, we weren’t optimizing for where observability stood at the time. We were building for the world it was heading toward.

Coralogix and Sumo Logic are two different answers to the same observability platform decision. Where Coralogix processes telemetry in flight, stores it in your own Amazon Simple Storage Service (S3) bucket, and prices on data ingested, Sumo Logic keeps data in vendor-managed storage and, under its Flex model, bills for data scanned at query time. Both platforms have introduced pricing and artificial intelligence (AI) changes in the past year, and those changes have widened the difference between them.

Coralogix and New Relic both cover the full observability surface, but they charge for it and store it in different ways. One prices purely on data ingested and writes telemetry to a bucket you own, while the other combines ingest pricing with per-user licensing and retains data in its own backend. This guide covers how the two platforms compare on core features, pricing structure, AI observability, archiving and retention, security coverage, and support, then shows when each one is the stronger choice.

Most teams judge their AI coding agent on two things: the monthly bill and a feeling. The bill tells you what you spent and the feeling tells you whether it seems to be helping, but neither one tells you what the agent actually did. As these tools move into the critical path of how software ships, that gap is starting to matter. I wanted to replace the feeling with something I could measure and understand what shapes of work affects this bill, so I decided to run an experiment on myself.

There was a time when “Opus” meant a classical composition and “Sonnet” was fourteen lines of Shakespeare you definitely did not read before the test. Now they’re model tiers, and every new release rewrites the economics of your engineering org whether you’re ready or not. Currently, your monthly total hides the crucial information you need to control and justify AI spend.

OpenTelemetry won the architectural standards battle. At scale, though, telemetry breaks more like plumbing than code. It breaks quietly, across a graph, with a blast radius you don’t understand until it’s expensive. With over 65% of organizations now running more than 10 collectors in production, hybrid deployments across Kubernetes and VMs are accelerating fast. Telemetry standardization is no longer a project milestone. It is a baseline expectation.

The race to better AI-assisted observability has been a race for bigger and better models. But intelligence was never the real bottleneck. Structure and context were.