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By Chris Cooney
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.
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By Chris Cooney
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.
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By Annie Freeman
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.
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By Lily Waldorf
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.
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By Jonny Steiner
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.
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By Micha Duman
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.
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By Annie Freeman
Recently, someone talked Chipotle’s customer support agent into reversing a linked list – a task completely unrelated to burritos in any way. Screenshots circulated, people laughed, but underneath the joke sat a sharper question. If a production support agent will do that on a public channel, what else will it do that nobody is screenshotting? The bug is funny. The trust gap behind it is not.
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By Jonny Steiner
Kubernetes dashboards often mask a systemic infrastructure failure. When a critical Java service fluctuates and restarts, the post-mortem often confirms an Out-of-Memory (OOM) event. While CPU metrics appear healthy, memory has silently hit a ceiling, forcing the kernel to terminate the process.
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By Micha Duman
TCO policies have always been one of the most impactful cost levers in Coralogix. Route business-critical data to High, push monitoring data to Medium, archive compliance logs to Low. With the addition of DataPrime expressions (DPXL) – a subset of the DataPrime query language designed for inline filtering at ingest – that routing became even more precise, matching on any field in the event payload, not just application, subsystem, and severity.
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By Jonny Steiner
It’s 20 minutes into a P0 incident, and you have already switched between four different tools, re-authenticated twice, and translated queries across three incompatible syntax languages. The root cause you are searching for. Well, that is still out there somewhere. The reality of investigative latency is that most engineering teams face navigation problems, not data problems. During high-pressure incidents, teams lose cognitive momentum due to context switching between disconnected telemetry silos.
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By Coralogix
Anthropic have just released Sonnet 5, their cost effective alternative to Opus 4.8. The token cost is much lower, but when we analyse the telemetry, we find something surprising. It turns out, it's not all about token cost!
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By Coralogix
Agent mode in the Coralogix CLI cuts token consumption by nearly 50%, without sacrificing the context your agents need to actually do their job.
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By Coralogix
Dataspaces and Datasets | The Structured Data Layer for Teams and AI | Coralogix Dataspaces and Datasets from Coralogix: the structured data layer teams and AI were waiting for. Turn a single query into a reusable dataset, share it across teams, and keep dashboards fast as your data scales. In this video: Timestamps: Dataspaces and Datasets are available now in Coralogix. Whether you're building dashboards, running background queries, or powering AI agents with telemetry data, Dataspaces give your organization a governed, high-performance data architecture that scales with your teams.
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By Coralogix
Learn how to create a user-defined dataset in Coralogix and route telemetry data into it using TCO policies with granular DataPrime expressions. In this walkthrough, you'll learn how to:• Create a new dataset with its own schema, permissions, retention, and cost visibility• Configure PBAC settings for governed access control• Route data using DataPrime expressions in TCO policies• Fan out events to multiple datasets from a single source.
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By Coralogix
I tested the new Anthropic model Fable 5 truly is using data, not vibes. Why does it feel faster? Does it actually cost double? Is it better at coding?
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By Coralogix
Stop editing raw YAML by hand. Discover how to build, validate, and scale complex OTel pipelines visually with the Visual Builder for Coralogix Fleet Management.
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By Coralogix
There are numerous types of logs in AWS, and the more applications and services you run in AWS, the more complex your logging needs are bound to be. Learn how to manage AWS log data that originates from various sources across every layer of the application stack, is varied in format, frequency, and importance.
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Coralogix helps software companies avoid getting lost in their log data by automatically figuring out their production problems:
- Know when your flows break: Coralogix maps your software flows, automatically detects production problems and delivers pinpoint insights.
- Make your Big Data small: Coralogix’s Loggregation automatically clusters your log data back into its original patterns so you can view hours of data in seconds.
- All your information at a glance: Use Coralogix or our hosted Kibana to query your data, view your live log stream, and define your dashboard widgets for maximum control over your data.
Our machine learning powered platform turns your cluttered log data into a meaningful set of templates and flows. View patterns and trends, and gain valuable insights to stay one step ahead at all times!