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Neebula Previews SaaS-Optimized ServiceWatch

Neebula Systems invites customers to preview the Neebula ServiceWatch solution in the cloud.

IT managers will be able to use a Software-as-a-Service (SaaS)-based product to quickly and effectively discover and map IT resources – hardware and software – that make up a specific business service. This eliminates the long, labor-intensive process of installing on-premise software and then manually discovering and mapping IT resources.

Neebula ServiceWatch is a top-down, business-level discovery and dependency mapping product which leverages patented technology to automate the entire service modeling process, requiring no manual intervention.

Neebula ServiceWatch employs the service models to assist IT administrators in managing the availability of business services. Legacy solutions can take months or years to do what Neebula’s automated approach accomplishes in days.

Providing all the benefits of modern SaaS offerings, including self-service configuration, low entry costs, reduced infrastructure investments, increased accessibility, ease-of-navigability, and straight-forward implementation, Neebula ServiceWatch starts the discovery process with the entry point to the business service (e.g. URL, MQ request, Citrix client etc.), automatically discovers and maps all IT infrastructure components - hardware and software - upon which the business service depends, and provides IT administrators with a single-pane dashboard view into the health of critical services. The Neebula top-down, service-centric approach frees IT managers from the need to possess detailed knowledge of server, storage, network and application infrastructure, as well as the manual, cost-intensive, and oftentimes fruitless effort to map and maintain dependencies between these items.

“Neebula ServiceWatch empowers IT administrators by allowing them to focus first on the business service, which is what their end-user consumes and cares about most,” said Ariel Gordon, VP Products and co-founder of Neebula. “No more frustrating ‘boil the ocean’ activities to discover and manually create relationships between IT assets that are immediately out-of-date. The SaaS-delivered nature of ServiceWatch reinforces Neebula’s ability to deliver immediate and actionable results by removing the onus of upfront capital expenditures, complex installations, services-assisted configurations, and the headaches of managing yet another tool on-premise.”

Neebula ServiceWatch fills a major IT management gap by discovering and matching IT resources, such as hardware and software, with the business service. The latter represents what end-users in an IT environment actually use, as opposed to the individual hardware and software that make those services possible. For example: an inventory management system may be made up of myriad applications, databases , servers and routers, but the person using it only sees what’s on the screen - the particular business service. Defining these relationships provides IT managers with the ability to make decisions and take actions that will decrease time-to-implement, enhance productivity, increase the efficiency of the datacenter and reduce overall costs by a substantial amount.

The traditional approach to business service modeling involves leveraging the output of discovery tools to manually construct relationships between hardware and software in the datacenter. Creating this model and keeping it up to date is an enormous task requiring extensive manual labor. Add to this the fact that datacenters are becoming more complex through the adoption of virtualization and private, public, and hybrid cloud architectures, making the task of manually building and maintaining the service map even more difficult since it is constantly changing.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Neebula Previews SaaS-Optimized ServiceWatch

Neebula Systems invites customers to preview the Neebula ServiceWatch solution in the cloud.

IT managers will be able to use a Software-as-a-Service (SaaS)-based product to quickly and effectively discover and map IT resources – hardware and software – that make up a specific business service. This eliminates the long, labor-intensive process of installing on-premise software and then manually discovering and mapping IT resources.

Neebula ServiceWatch is a top-down, business-level discovery and dependency mapping product which leverages patented technology to automate the entire service modeling process, requiring no manual intervention.

Neebula ServiceWatch employs the service models to assist IT administrators in managing the availability of business services. Legacy solutions can take months or years to do what Neebula’s automated approach accomplishes in days.

Providing all the benefits of modern SaaS offerings, including self-service configuration, low entry costs, reduced infrastructure investments, increased accessibility, ease-of-navigability, and straight-forward implementation, Neebula ServiceWatch starts the discovery process with the entry point to the business service (e.g. URL, MQ request, Citrix client etc.), automatically discovers and maps all IT infrastructure components - hardware and software - upon which the business service depends, and provides IT administrators with a single-pane dashboard view into the health of critical services. The Neebula top-down, service-centric approach frees IT managers from the need to possess detailed knowledge of server, storage, network and application infrastructure, as well as the manual, cost-intensive, and oftentimes fruitless effort to map and maintain dependencies between these items.

“Neebula ServiceWatch empowers IT administrators by allowing them to focus first on the business service, which is what their end-user consumes and cares about most,” said Ariel Gordon, VP Products and co-founder of Neebula. “No more frustrating ‘boil the ocean’ activities to discover and manually create relationships between IT assets that are immediately out-of-date. The SaaS-delivered nature of ServiceWatch reinforces Neebula’s ability to deliver immediate and actionable results by removing the onus of upfront capital expenditures, complex installations, services-assisted configurations, and the headaches of managing yet another tool on-premise.”

Neebula ServiceWatch fills a major IT management gap by discovering and matching IT resources, such as hardware and software, with the business service. The latter represents what end-users in an IT environment actually use, as opposed to the individual hardware and software that make those services possible. For example: an inventory management system may be made up of myriad applications, databases , servers and routers, but the person using it only sees what’s on the screen - the particular business service. Defining these relationships provides IT managers with the ability to make decisions and take actions that will decrease time-to-implement, enhance productivity, increase the efficiency of the datacenter and reduce overall costs by a substantial amount.

The traditional approach to business service modeling involves leveraging the output of discovery tools to manually construct relationships between hardware and software in the datacenter. Creating this model and keeping it up to date is an enormous task requiring extensive manual labor. Add to this the fact that datacenters are becoming more complex through the adoption of virtualization and private, public, and hybrid cloud architectures, making the task of manually building and maintaining the service map even more difficult since it is constantly changing.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...