Skip to main content

Lightstep Releases Change Intelligence

Lightstep announced the general availability of Change Intelligence.

While AIOps is frequently advertised as a way to dramatically improve IT operations, it often fails to deliver, in part because today’s software applications are too dynamic: software, infrastructure, and user behavior are all changing faster than ever before. Lightstep has embraced those changes and put them at the center of analyzing application performance with Change Intelligence, inspired by work done by the Lightstep CEO Ben Sigelman on Google’s Monarch project.

The X-factor that makes Change Intelligence possible is Lightstep’s time-series database, which can process over a trillion events each day and is built by the same engineers that worked on the Monarch project at Google. Monarch is the globally-distributed in-memory time series database system in Google, that is used internally to monitor the availability, correctness, performance, load, and other aspects of billion-users scale applications and systems at Google. By tightly integrating these metrics with Lightstep’s existing distributed tracing data, engineers can connect cause and effect faster than what was previously possible.

“We took inspiration from the technology we built at Google, took it to the next level, and made it generally available to all Lightstep users,” said Ben Sigelman, Co-Founder and CEO of Lightstep. “With Change Intelligence, any developer, operator, or SRE can instantly understand changes in their service’s health and – most importantly – what caused those changes. In this way, we’re able to actually deliver on the promise of AIOps: to automate the process of investigating changes within complex systems.”

“The truth is that companies are already drowning in data from dashboards, alerts, endless logs – as an industry we’re so afraid of missing data that we collect all of it,” said Daniel ‘Spoons’ Spoonhower, Co-Founder and Chief Architect of Lightstep. “But from talking with customers, we’ve learned that this can create just as many problems as it solves. We’re not looking to add more data or noise – we’re looking to find root causes and resolve issues faster.”

The Latest

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

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 ...

Lightstep Releases Change Intelligence

Lightstep announced the general availability of Change Intelligence.

While AIOps is frequently advertised as a way to dramatically improve IT operations, it often fails to deliver, in part because today’s software applications are too dynamic: software, infrastructure, and user behavior are all changing faster than ever before. Lightstep has embraced those changes and put them at the center of analyzing application performance with Change Intelligence, inspired by work done by the Lightstep CEO Ben Sigelman on Google’s Monarch project.

The X-factor that makes Change Intelligence possible is Lightstep’s time-series database, which can process over a trillion events each day and is built by the same engineers that worked on the Monarch project at Google. Monarch is the globally-distributed in-memory time series database system in Google, that is used internally to monitor the availability, correctness, performance, load, and other aspects of billion-users scale applications and systems at Google. By tightly integrating these metrics with Lightstep’s existing distributed tracing data, engineers can connect cause and effect faster than what was previously possible.

“We took inspiration from the technology we built at Google, took it to the next level, and made it generally available to all Lightstep users,” said Ben Sigelman, Co-Founder and CEO of Lightstep. “With Change Intelligence, any developer, operator, or SRE can instantly understand changes in their service’s health and – most importantly – what caused those changes. In this way, we’re able to actually deliver on the promise of AIOps: to automate the process of investigating changes within complex systems.”

“The truth is that companies are already drowning in data from dashboards, alerts, endless logs – as an industry we’re so afraid of missing data that we collect all of it,” said Daniel ‘Spoons’ Spoonhower, Co-Founder and Chief Architect of Lightstep. “But from talking with customers, we’ve learned that this can create just as many problems as it solves. We’re not looking to add more data or noise – we’re looking to find root causes and resolve issues faster.”

The Latest

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

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 ...