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Lightstep Unveils New Observability Platform

Lightstep announced the release of its observability solution to help developers better understand the health of systems and services.

This includes integrating new metrics capabilities into their platform, enabling developers to have a one-stop shop for all their observability needs. New analysis features provide developers with the fastest and most effective way to investigate errors, understand service health issues, and predict the impact of new deployments.

The next evolution of Lightstep’s distributed tracing tool is an observability platform that provides automatic and rich analysis. Lightstep’s powerful capabilities help developers develop, deploy, and debug new service releases, regardless of the deployment methodology used (for example, canaries, blue/green deploys). Developers will then be able to identify and resolve the cause of performance degradations faster than any other tool on the market. The error and latency analysis feature simplifies the investigation of a service’s errors and latency by quickly highlighting the source of the problem and how it propagates through the call stack. It also helps developers understand what may have caused the error with a list of data-driven hypotheses.

And with new capabilities around runtime metrics, the solution accurately correlates metrics with problems in service performance. By making runtime metrics accessible with zero additional configuration and providing side-by-side visibility of performance metrics, developers are able to quickly identify if problems in their runtime (e.g. increased garbage collection, CPU, or memory) are causing service degradations.

“At our core, Lightstep is a company of developers, so we know first-hand the stress placed on DevOps teams to execute seamless deployments every time, and how it is only continuing to grow as tech stacks get more complex,” said Daniel Spoonhower, CTO, Lightstep. “With our new observability solution, developers can analyze errors in real time, at any point in the deployment, and have all the tools and data they need in one unified view - that integrate effortlessly into their existing workflows.”

With current market offerings, data that developers need quick access to is fragmented across many tools, making it difficult to swiftly understand what has changed in the performance of their services. Lightstep’s platform gives users a single place to answer real observability problems in real-time. These new updates include:

- Minimize the guessing: see insights based on metrics, traces, and logs all in one place, and at the right time

- Regression analysis: automatically see what is contributing the most to your errors, latency, and throughput

- Deployment monitoring: automatically see how different versions of your service are performing, and quickly diagnose regressions

- Seamless onboarding: guided onboarding across almost any language in order to enable immediate insights

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Lightstep Unveils New Observability Platform

Lightstep announced the release of its observability solution to help developers better understand the health of systems and services.

This includes integrating new metrics capabilities into their platform, enabling developers to have a one-stop shop for all their observability needs. New analysis features provide developers with the fastest and most effective way to investigate errors, understand service health issues, and predict the impact of new deployments.

The next evolution of Lightstep’s distributed tracing tool is an observability platform that provides automatic and rich analysis. Lightstep’s powerful capabilities help developers develop, deploy, and debug new service releases, regardless of the deployment methodology used (for example, canaries, blue/green deploys). Developers will then be able to identify and resolve the cause of performance degradations faster than any other tool on the market. The error and latency analysis feature simplifies the investigation of a service’s errors and latency by quickly highlighting the source of the problem and how it propagates through the call stack. It also helps developers understand what may have caused the error with a list of data-driven hypotheses.

And with new capabilities around runtime metrics, the solution accurately correlates metrics with problems in service performance. By making runtime metrics accessible with zero additional configuration and providing side-by-side visibility of performance metrics, developers are able to quickly identify if problems in their runtime (e.g. increased garbage collection, CPU, or memory) are causing service degradations.

“At our core, Lightstep is a company of developers, so we know first-hand the stress placed on DevOps teams to execute seamless deployments every time, and how it is only continuing to grow as tech stacks get more complex,” said Daniel Spoonhower, CTO, Lightstep. “With our new observability solution, developers can analyze errors in real time, at any point in the deployment, and have all the tools and data they need in one unified view - that integrate effortlessly into their existing workflows.”

With current market offerings, data that developers need quick access to is fragmented across many tools, making it difficult to swiftly understand what has changed in the performance of their services. Lightstep’s platform gives users a single place to answer real observability problems in real-time. These new updates include:

- Minimize the guessing: see insights based on metrics, traces, and logs all in one place, and at the right time

- Regression analysis: automatically see what is contributing the most to your errors, latency, and throughput

- Deployment monitoring: automatically see how different versions of your service are performing, and quickly diagnose regressions

- Seamless onboarding: guided onboarding across almost any language in order to enable immediate insights

The Latest

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...

In MEAN TIME TO INSIGHT Episode 21, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses AI-driven NetOps ... 

Enterprise IT has become increasingly complex and fragmented. Organizations are juggling dozens — sometimes hundreds — of different tools for endpoint management, security, app delivery, and employee experience. Each one needs its own license, its own maintenance, and its own integration. The result is a patchwork of overlapping tools, data stuck in silos, security vulnerabilities, and IT teams are spending more time managing software than actually getting work done ...

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