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Sumo Logic Releases HELM Chart V4 Feature

Sumo Logic announced the availability of its HELM Chart V4 feature to fully unify data collection as part of its continued commitment to OpenTelemetry (OTel).

Organizations can now package, configure and deploy applications and services on Kubernetes clusters with OpenTelemetry as a default to simplify the collection of metrics, events, logs and traces.

Sumo Logic HELM Chart V4 removes dependencies on disparate third-party solutions like Fluentbit, Fluentd and Prometheus to reduce data collection complexity and cost. By fully unifying collection for all logs, metrics and traces, organizations can save the cost of managing multiple agents. Organizations can also optimize their deployment lifecycle while minimizing required updates and potential security risks. OTel collection also provides significant performance with less CPU consumption for additional cost efficiencies.

“Sumo Logic is continuing to deliver on our commitment to OpenTelemetry data collection to customers and the community,” said Tej Redkar, Chief Product Officer for Sumo Logic. “Sumo Logic HELM Chart V4 evolves the collection experience for Kubernetes by using OpenTelemetry as its standard collector, and will help our customers get the insights they need to take action to uncover and resolve performance issues quickly, so DevOps teams can spend less time troubleshooting issues, and do what they do best - deploy code.”

Sumo Logic HELM Chart V4 fully unifies the OpenTelemetry pipeline to provide real-time operations insights for digital business through:

- Unified collection - unified Kubernetes monitoring is now available through a single agent for all signals - logs, metrics, traces and events.

- Auto-instrumentation - correlated telemetry and auto-instrumentation provide a simplified collection process to reduce the chaos of managing disparate third-party collection agents to process monitoring signals.

- Pre-canned configurations - running a single agent for all data types allows for a smaller, more efficient data collection footprint, giving customers quicker application infrastructure setup and a smoother experience to help drive adoption with developers and DevOps teams.

The Latest

IT organizations have historically measured success by how quickly they can respond when something goes wrong. The entire discipline of Incident Management has been optimized around mean time to resolution, first-response SLAs and ticket closure rates. But new research suggests that even though this is a well-executed playbook, it's no longer enough to retain customers ...

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Sumo Logic Releases HELM Chart V4 Feature

Sumo Logic announced the availability of its HELM Chart V4 feature to fully unify data collection as part of its continued commitment to OpenTelemetry (OTel).

Organizations can now package, configure and deploy applications and services on Kubernetes clusters with OpenTelemetry as a default to simplify the collection of metrics, events, logs and traces.

Sumo Logic HELM Chart V4 removes dependencies on disparate third-party solutions like Fluentbit, Fluentd and Prometheus to reduce data collection complexity and cost. By fully unifying collection for all logs, metrics and traces, organizations can save the cost of managing multiple agents. Organizations can also optimize their deployment lifecycle while minimizing required updates and potential security risks. OTel collection also provides significant performance with less CPU consumption for additional cost efficiencies.

“Sumo Logic is continuing to deliver on our commitment to OpenTelemetry data collection to customers and the community,” said Tej Redkar, Chief Product Officer for Sumo Logic. “Sumo Logic HELM Chart V4 evolves the collection experience for Kubernetes by using OpenTelemetry as its standard collector, and will help our customers get the insights they need to take action to uncover and resolve performance issues quickly, so DevOps teams can spend less time troubleshooting issues, and do what they do best - deploy code.”

Sumo Logic HELM Chart V4 fully unifies the OpenTelemetry pipeline to provide real-time operations insights for digital business through:

- Unified collection - unified Kubernetes monitoring is now available through a single agent for all signals - logs, metrics, traces and events.

- Auto-instrumentation - correlated telemetry and auto-instrumentation provide a simplified collection process to reduce the chaos of managing disparate third-party collection agents to process monitoring signals.

- Pre-canned configurations - running a single agent for all data types allows for a smaller, more efficient data collection footprint, giving customers quicker application infrastructure setup and a smoother experience to help drive adoption with developers and DevOps teams.

The Latest

IT organizations have historically measured success by how quickly they can respond when something goes wrong. The entire discipline of Incident Management has been optimized around mean time to resolution, first-response SLAs and ticket closure rates. But new research suggests that even though this is a well-executed playbook, it's no longer enough to retain customers ...

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...