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Nobl9 Introduces Replay

Nobl9 announced Replay, providing new capabilities to its Service Level Objective (SLO) platform that significantly increase and optimize how enterprises can deploy, use and benefit from SLOs.

The new features allow customers to create SLOs with historical data from the last 30 days and pre-calculates error budgets.

“Our customers run mission-critical platforms that let enterprise developers quickly deliver software applications. Nobl9 makes the handshake-agreement between platform teams and application teams explicit in code,” said Brian Singer, co-founder and CPO at Nobl9. “Now with Replay, our customers can ask ‘what if we had set different goals for our service?’ and see the results based on accurate past data so they don’t have to guess or wait for new data to accumulate.”

With Replay, customers can instantly view their last 30 days of reliability data in context when they create their SLOs. Their historical Service Level Indicator (SLI) data will be retrieved alongside real time observability metrics and merged together to see an error budget within the time window they select.

Some roadblocks for customers starting with SLOs are knowing how they did previously and what their realistic expectations should be for reliability. Replay provides a window into that by retrieving and graphing their reliability up to 30 days before the creation of the SLO. Customers can then immediately see if their historical data met the reliability criteria they set and see what error budget is remaining once their SLO is created.

Instead of exporting data and reviewing the last 30 days of metrics in a spreadsheet, with Replay, Nobl9 enables automation to capture historical data, merge them with real time SLI data, and allows customers to view SLO charts and generate reports shortly after creating an SLO.

Replay supports Datadog, Prometheus, Amazon Managed Prometheus, Splunk, and Graphite metrics. Additional data sources will be added soon.

Replay is now available in beta to all existing Nobl9 customers.

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Nobl9 Introduces Replay

Nobl9 announced Replay, providing new capabilities to its Service Level Objective (SLO) platform that significantly increase and optimize how enterprises can deploy, use and benefit from SLOs.

The new features allow customers to create SLOs with historical data from the last 30 days and pre-calculates error budgets.

“Our customers run mission-critical platforms that let enterprise developers quickly deliver software applications. Nobl9 makes the handshake-agreement between platform teams and application teams explicit in code,” said Brian Singer, co-founder and CPO at Nobl9. “Now with Replay, our customers can ask ‘what if we had set different goals for our service?’ and see the results based on accurate past data so they don’t have to guess or wait for new data to accumulate.”

With Replay, customers can instantly view their last 30 days of reliability data in context when they create their SLOs. Their historical Service Level Indicator (SLI) data will be retrieved alongside real time observability metrics and merged together to see an error budget within the time window they select.

Some roadblocks for customers starting with SLOs are knowing how they did previously and what their realistic expectations should be for reliability. Replay provides a window into that by retrieving and graphing their reliability up to 30 days before the creation of the SLO. Customers can then immediately see if their historical data met the reliability criteria they set and see what error budget is remaining once their SLO is created.

Instead of exporting data and reviewing the last 30 days of metrics in a spreadsheet, with Replay, Nobl9 enables automation to capture historical data, merge them with real time SLI data, and allows customers to view SLO charts and generate reports shortly after creating an SLO.

Replay supports Datadog, Prometheus, Amazon Managed Prometheus, Splunk, and Graphite metrics. Additional data sources will be added soon.

Replay is now available in beta to all existing Nobl9 customers.

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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