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Coralogix Releases Tracing TCO Optimizer

Coralogix launched the Tracing TCO Optimizer, enabling Coralogix users to assign use cases to their tracing data, and realize up to 90% savings on their ingestion costs.

"Traces are typically employed to provide a detailed operational view, but this is just one part of their value. Coralogix is now the only platform that allows users to define the use case for their data: whether the data be queried frequently, drive dashboards or be retained for historical analysis," said Yoni Farin, Coralogix CTO and co-founder. "This, combined with the power of Coralogix Remote Query and the Streama architecture, enables customers to infinitely retain and analyze their traces over years, while still delivering the remarkable cost optimizations that make Coralogix stand out in the industry."

New features and benefits of Tracing TCO Optimizer include:

- Significant Tracing Cost Savings - Users can now assign use cases to their Tracing data. High priority data is indexed and queried frequently and enjoys access to every feature, Medium priority data (75% cost savings) drives dashboards, machine learning models, in-stream alerting and more, while Low priority data (90% cost savings) is retained for historical analysis and regulatory reasons within the Coralogix Archive.

- Fine-grained Control - Data can be matched on whatever fields are attached to the trace, everything from language, to library version and error details.

- Infinite Retention. Instant Access - Combined with Coralogix Remote Query, traces can be retained indefinitely and still accessed in seconds at no extra costs, without the need to reindex.

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Coralogix Releases Tracing TCO Optimizer

Coralogix launched the Tracing TCO Optimizer, enabling Coralogix users to assign use cases to their tracing data, and realize up to 90% savings on their ingestion costs.

"Traces are typically employed to provide a detailed operational view, but this is just one part of their value. Coralogix is now the only platform that allows users to define the use case for their data: whether the data be queried frequently, drive dashboards or be retained for historical analysis," said Yoni Farin, Coralogix CTO and co-founder. "This, combined with the power of Coralogix Remote Query and the Streama architecture, enables customers to infinitely retain and analyze their traces over years, while still delivering the remarkable cost optimizations that make Coralogix stand out in the industry."

New features and benefits of Tracing TCO Optimizer include:

- Significant Tracing Cost Savings - Users can now assign use cases to their Tracing data. High priority data is indexed and queried frequently and enjoys access to every feature, Medium priority data (75% cost savings) drives dashboards, machine learning models, in-stream alerting and more, while Low priority data (90% cost savings) is retained for historical analysis and regulatory reasons within the Coralogix Archive.

- Fine-grained Control - Data can be matched on whatever fields are attached to the trace, everything from language, to library version and error details.

- Infinite Retention. Instant Access - Combined with Coralogix Remote Query, traces can be retained indefinitely and still accessed in seconds at no extra costs, without the need to reindex.

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Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

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