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Nobl9 Introduces Composite SLO 2.0

Nobl9 announced Nobl9 Composite SLO 2.0 to allow for the aggregation of multiple Service Level Objectives (SLOs) within a single SLO to provide an overall reliability performance view of your service.

The addition of composites allows users to capture an end-to-end user journey for a complex service such as an ecommerce website or a digital banking platform.

Composite SLO 2.0 unlocks SLOs at scale, differentiated by business units or use cases, enabling IT leaders to link their strategy directly to business impact. Teams at different levels of an organization can combine the disparate elements of their projects into composite SLOs. In turn, managers can aggregate these composites into a unified overarching hierarchy — a composite built of composites — to view the system’s performance holistically, with rich context of each component’s behavior easily accessible in a single dashboard.

Before Composite SLO 2.0, engineers could create SLOs to track the reliability of system components they were directly responsible for; for example, an infrastructure engineer might build an SLO for their servers. While traditional SLOs offer important insight, they lack information on how an element affects the total reliability of a complex system. Composite SLO 2.0 brings this critical context, allowing teams to link individual elements into a dynamic system-wide SLO and see how their small portion of the system is impacting end-user experience. Providing such perspective not only informs IT decision-making but shows engineers, even those building small pieces of the backend, how meaningful their work is to overall customer experience.

Nobl9 is partnering with a growing ecosystem of data sources such as Datadog, Splunk, Google BigQuery, and Amazon CloudWatch to offer maximum value to its customers, who often utilize a wide variety of monitoring and observability tools. Nobl9 collects and normalizes data from these various systems, and contextualizes it in a user-friendly dashboard, so customers can simply log into Nobl9 and build SLOs with relevant data from anywhere.

Nobl9 Composite SLO 2.0 allows users to:

- Create composites from a large number of SLOs — Aggregate SLOs from many components of a system into a single hierarchy, giving teams and executives a reliability metric for their entire product or service.

- Combine data from different data sources and projects — Unite SLOs based on data from Dynatrace, Amazon CloudWatch, and other sources for enhanced flexibility, enabling more management-oriented SLOs tailored to each ongoing project.

- Easily identify the biggest sources of error budget burn — Observe the behavior of complex systems and drill down into the error budget consumption of each component to understand root causes of issues.

- Assign weights to component SLOs — Adjust the “weight” of each SLO to ensure that SLOs with larger impact on the user experience contribute more to error budget burn; and,

- Build composites out of other composites — Receive unified reliability metrics by building composite SLOs of near-infinite size and complexity out of any existing composites.

“Composite SLO 2.0 is the culmination of a years-long effort to support anyone responsible for reliable products with the most advanced SLO capabilities possible. Anyone who uses SLOs can now understand how they are steering the ship that stakeholders care about,” said Brian Singer, co-founder and Chief Product Officer, Nobl9. “Composite SLOs behave just like normal SLOs, but provide management with a detailed overview of reliability cascading down to the smallest element. We hope this will give engineers pride in their work, because they will understand how their efforts directly impact end-users, and by extension the health of their company.”

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Nobl9 Introduces Composite SLO 2.0

Nobl9 announced Nobl9 Composite SLO 2.0 to allow for the aggregation of multiple Service Level Objectives (SLOs) within a single SLO to provide an overall reliability performance view of your service.

The addition of composites allows users to capture an end-to-end user journey for a complex service such as an ecommerce website or a digital banking platform.

Composite SLO 2.0 unlocks SLOs at scale, differentiated by business units or use cases, enabling IT leaders to link their strategy directly to business impact. Teams at different levels of an organization can combine the disparate elements of their projects into composite SLOs. In turn, managers can aggregate these composites into a unified overarching hierarchy — a composite built of composites — to view the system’s performance holistically, with rich context of each component’s behavior easily accessible in a single dashboard.

Before Composite SLO 2.0, engineers could create SLOs to track the reliability of system components they were directly responsible for; for example, an infrastructure engineer might build an SLO for their servers. While traditional SLOs offer important insight, they lack information on how an element affects the total reliability of a complex system. Composite SLO 2.0 brings this critical context, allowing teams to link individual elements into a dynamic system-wide SLO and see how their small portion of the system is impacting end-user experience. Providing such perspective not only informs IT decision-making but shows engineers, even those building small pieces of the backend, how meaningful their work is to overall customer experience.

Nobl9 is partnering with a growing ecosystem of data sources such as Datadog, Splunk, Google BigQuery, and Amazon CloudWatch to offer maximum value to its customers, who often utilize a wide variety of monitoring and observability tools. Nobl9 collects and normalizes data from these various systems, and contextualizes it in a user-friendly dashboard, so customers can simply log into Nobl9 and build SLOs with relevant data from anywhere.

Nobl9 Composite SLO 2.0 allows users to:

- Create composites from a large number of SLOs — Aggregate SLOs from many components of a system into a single hierarchy, giving teams and executives a reliability metric for their entire product or service.

- Combine data from different data sources and projects — Unite SLOs based on data from Dynatrace, Amazon CloudWatch, and other sources for enhanced flexibility, enabling more management-oriented SLOs tailored to each ongoing project.

- Easily identify the biggest sources of error budget burn — Observe the behavior of complex systems and drill down into the error budget consumption of each component to understand root causes of issues.

- Assign weights to component SLOs — Adjust the “weight” of each SLO to ensure that SLOs with larger impact on the user experience contribute more to error budget burn; and,

- Build composites out of other composites — Receive unified reliability metrics by building composite SLOs of near-infinite size and complexity out of any existing composites.

“Composite SLO 2.0 is the culmination of a years-long effort to support anyone responsible for reliable products with the most advanced SLO capabilities possible. Anyone who uses SLOs can now understand how they are steering the ship that stakeholders care about,” said Brian Singer, co-founder and Chief Product Officer, Nobl9. “Composite SLOs behave just like normal SLOs, but provide management with a detailed overview of reliability cascading down to the smallest element. We hope this will give engineers pride in their work, because they will understand how their efforts directly impact end-users, and by extension the health of their company.”

The Latest

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

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 ...