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5 Key Service Capabilities for Cloud Providers

Service providers have been running their operations like a cloud for years, with infrastructure managed as shared resources, multitenancy, chargeback based upon resource usage and more. This makes them well-positioned to make the transition to cloud and take advantage of a golden opportunity to offer differentiated services and increase revenue.

However, recent survey results suggest that users of public cloud lack confidence in the service provider industry to adequately monitor and manage their resources in the cloud. Of more than 100 IT directors and above in North America surveyed, 79 percent indicated they are running some production applications in the cloud, but only 17 percent expect to rely solely on their cloud service provider to provide performance metrics.

Service providers who are making the transition to cloud services or already offer them need to not only deploy the right processes and technologies for monitoring and management, but also be transparent with customers about the quality of service and insight they are able to deliver as a result.

The following are five key capabilities and offerings that help successful cloud service providers deliver the highest quality service, provide customers insight into usage and performance, and increase revenue. These resources ensure expectations are set and met, and that customers have the utmost confidence in the service provider’s cloud management capabilities.

1. Centralized Management

Service providers need tools that enable them to manage their traditional and cloud infrastructures as one entity. The ability to consolidate critical IT operations and dynamic cloud management functions such as performance, fault, availability, service desk, automation and event management is critical to delivering high-quality services across distributed resources. Having a consistent, correlated set of metrics across both on-premises and cloud infrastructures speeds troubleshooting. It also simplifies and optimizes management workflows and minimizes integration work to increase efficiency and reduce costs.

2. Service-Level Management with Integrated Events, Notification Workflow and Ticketing

Centralized management supports the ability to set more effective and consistent policies for events, alerts, notifications, escalation paths, IT ticketing and problem resolution workflow to ensure fast troubleshooting and consistent service delivery. It also facilitates proactive service-level management as defined by each customer’s SLAs to ensure both service provider and customer are on the same page. Service providers can also include customers in notification and escalation policies so they receive automatically designated alerts and messages that can be used for on-demand customer upgrades.

3. Customer Self-Service Portals

Service providers are offering customers their own web-based portals for views into system and application availability and performance. With multi-tenant views to see each customer’s data separately, service providers can give each customer secure views into their own data. Giving customers views into the same data they use – such as service levels and availability, performance levels, ticket queues and bandwidth usage for billing and analysis – enables service providers to be more responsive and efficient as well as quickly add revenue-generating services across virtual and cloud resources.

4. Automated Provisioning

Customer expectations of cloud service quality require a heightened level of speed and scale on top of traditional service provider operations, making automation essential. This includes the ability to provision service monitoring automatically. Successful cloud service providers are adding automation into their customer self-service portals, so that the setup of new management services can be done without requiring human intervention.

5. Bandwidth Reports and Billing Calculation

Successful cloud providers also generate standardized and customizable reports on bandwidth usage and availability per customer to ensure complete visibility into cloud deployments. This includes the use of built-in reporting tools to perform automated billing calculations according to each customer’s actual usage and billing terms.

In a world where end users expect always-on, on-demand cloud services, just deploying the infrastructure is not enough. For cloud adoption to increase, enterprise customers with critical business applications need to have faith that their service providers deliver secure, high-quality services and transparency into service levels. While not an exhaustive list, using these five cloud management technologies and service offerings – and clearly communicating the value – will help boost service levels and give customers an increased level of confidence in cloud computing. This will better enable service providers to take advantage of the massive opportunities cloud computing brings and capture more market share.

About Steve Harriman

Steve Harriman is the Senior Vice President of Marketing for ScienceLogic. For more than twenty years, Harriman has led technology marketing in startups, privately-held mid-sized ventures and publicly traded corporations. Before joining ScienceLogic, he spent five years as Senior Vice President of Marketing at NetQoS . Prior to NetQoS, Steve worked for Sterling Software and Candle Corporation. Harriman began his career in data center operations and systems programming, working in the United Kingdom before moving to the United States.

Related Links:

www.sciencelogic.com

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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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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 ...

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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.

5 Key Service Capabilities for Cloud Providers

Service providers have been running their operations like a cloud for years, with infrastructure managed as shared resources, multitenancy, chargeback based upon resource usage and more. This makes them well-positioned to make the transition to cloud and take advantage of a golden opportunity to offer differentiated services and increase revenue.

However, recent survey results suggest that users of public cloud lack confidence in the service provider industry to adequately monitor and manage their resources in the cloud. Of more than 100 IT directors and above in North America surveyed, 79 percent indicated they are running some production applications in the cloud, but only 17 percent expect to rely solely on their cloud service provider to provide performance metrics.

Service providers who are making the transition to cloud services or already offer them need to not only deploy the right processes and technologies for monitoring and management, but also be transparent with customers about the quality of service and insight they are able to deliver as a result.

The following are five key capabilities and offerings that help successful cloud service providers deliver the highest quality service, provide customers insight into usage and performance, and increase revenue. These resources ensure expectations are set and met, and that customers have the utmost confidence in the service provider’s cloud management capabilities.

1. Centralized Management

Service providers need tools that enable them to manage their traditional and cloud infrastructures as one entity. The ability to consolidate critical IT operations and dynamic cloud management functions such as performance, fault, availability, service desk, automation and event management is critical to delivering high-quality services across distributed resources. Having a consistent, correlated set of metrics across both on-premises and cloud infrastructures speeds troubleshooting. It also simplifies and optimizes management workflows and minimizes integration work to increase efficiency and reduce costs.

2. Service-Level Management with Integrated Events, Notification Workflow and Ticketing

Centralized management supports the ability to set more effective and consistent policies for events, alerts, notifications, escalation paths, IT ticketing and problem resolution workflow to ensure fast troubleshooting and consistent service delivery. It also facilitates proactive service-level management as defined by each customer’s SLAs to ensure both service provider and customer are on the same page. Service providers can also include customers in notification and escalation policies so they receive automatically designated alerts and messages that can be used for on-demand customer upgrades.

3. Customer Self-Service Portals

Service providers are offering customers their own web-based portals for views into system and application availability and performance. With multi-tenant views to see each customer’s data separately, service providers can give each customer secure views into their own data. Giving customers views into the same data they use – such as service levels and availability, performance levels, ticket queues and bandwidth usage for billing and analysis – enables service providers to be more responsive and efficient as well as quickly add revenue-generating services across virtual and cloud resources.

4. Automated Provisioning

Customer expectations of cloud service quality require a heightened level of speed and scale on top of traditional service provider operations, making automation essential. This includes the ability to provision service monitoring automatically. Successful cloud service providers are adding automation into their customer self-service portals, so that the setup of new management services can be done without requiring human intervention.

5. Bandwidth Reports and Billing Calculation

Successful cloud providers also generate standardized and customizable reports on bandwidth usage and availability per customer to ensure complete visibility into cloud deployments. This includes the use of built-in reporting tools to perform automated billing calculations according to each customer’s actual usage and billing terms.

In a world where end users expect always-on, on-demand cloud services, just deploying the infrastructure is not enough. For cloud adoption to increase, enterprise customers with critical business applications need to have faith that their service providers deliver secure, high-quality services and transparency into service levels. While not an exhaustive list, using these five cloud management technologies and service offerings – and clearly communicating the value – will help boost service levels and give customers an increased level of confidence in cloud computing. This will better enable service providers to take advantage of the massive opportunities cloud computing brings and capture more market share.

About Steve Harriman

Steve Harriman is the Senior Vice President of Marketing for ScienceLogic. For more than twenty years, Harriman has led technology marketing in startups, privately-held mid-sized ventures and publicly traded corporations. Before joining ScienceLogic, he spent five years as Senior Vice President of Marketing at NetQoS . Prior to NetQoS, Steve worked for Sterling Software and Candle Corporation. Harriman began his career in data center operations and systems programming, working in the United Kingdom before moving to the United States.

Related Links:

www.sciencelogic.com

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.