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Imply Launches Lumi Enterprise

Imply announced Lumi Enterprise, a new Bring-Your-Own-Cloud (BYOC) offering of Imply Lumi, an Observability Warehouse.

Purpose-built as a data layer for observability, Lumi Enterprise enables organizations to significantly reduce observability costs while improving query performance, without disrupting existing workflows or compromising data sovereignty requirements.

The enterprise edition introduces a Bring-Your-Own-Cloud (BYOC) architecture that runs Imply Lumi entirely inside an organization’s AWS environment. Data, encryption keys and infrastructure remain under the organization’s control, eliminating the traditional tradeoff between data sovereignty and operational simplicity.

“Enterprises need to keep sensitive operational data inside their own environments for governance and compliance,” said Eric Tschetter, Chief Architect at Imply. “But self-managed observability systems often create operational overhead, forcing teams to track releases, test updates, and patch security issues just to stay current.”

Lumi Enterprise was built to address these challenges, giving organizations full control of their data while eliminating the operational burden traditionally associated with running observability infrastructure.

Lumi Enterprise introduces a fully managed model that runs entirely within the customer’s AWS account, combining the control of self-hosted systems with the simplicity of a managed service.

Imply does not require direct IAM access to customer’s environments. Organizations retain full control of their infrastructure, security policies, and data access.

The architecture includes a lightweight Client deployed within the customer’s AWS environment and Amazon EKS cluster that:

  • Retrieves approved releases from Imply
  • Applies updates within the customer’s environment
  • Sends health and performance telemetry to Imply’s management plane

This model enables centralized monitoring and lifecycle management while preserving full visibility for governance, auditing, and compliance.

Lumi Enterprise is designed to align the needs of security, platform, and business stakeholders:

  • Security teams maintain full control over sensitive data and compliance boundaries
  • Platform teams eliminate the operational burden of managing observability infrastructure
  • Business leaders benefit from a more efficient observability model.

Imply Lumi’s compression technology enables organizations to store 1TB of raw data while using only one-third the storage capacity.

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Imply Launches Lumi Enterprise

Imply announced Lumi Enterprise, a new Bring-Your-Own-Cloud (BYOC) offering of Imply Lumi, an Observability Warehouse.

Purpose-built as a data layer for observability, Lumi Enterprise enables organizations to significantly reduce observability costs while improving query performance, without disrupting existing workflows or compromising data sovereignty requirements.

The enterprise edition introduces a Bring-Your-Own-Cloud (BYOC) architecture that runs Imply Lumi entirely inside an organization’s AWS environment. Data, encryption keys and infrastructure remain under the organization’s control, eliminating the traditional tradeoff between data sovereignty and operational simplicity.

“Enterprises need to keep sensitive operational data inside their own environments for governance and compliance,” said Eric Tschetter, Chief Architect at Imply. “But self-managed observability systems often create operational overhead, forcing teams to track releases, test updates, and patch security issues just to stay current.”

Lumi Enterprise was built to address these challenges, giving organizations full control of their data while eliminating the operational burden traditionally associated with running observability infrastructure.

Lumi Enterprise introduces a fully managed model that runs entirely within the customer’s AWS account, combining the control of self-hosted systems with the simplicity of a managed service.

Imply does not require direct IAM access to customer’s environments. Organizations retain full control of their infrastructure, security policies, and data access.

The architecture includes a lightweight Client deployed within the customer’s AWS environment and Amazon EKS cluster that:

  • Retrieves approved releases from Imply
  • Applies updates within the customer’s environment
  • Sends health and performance telemetry to Imply’s management plane

This model enables centralized monitoring and lifecycle management while preserving full visibility for governance, auditing, and compliance.

Lumi Enterprise is designed to align the needs of security, platform, and business stakeholders:

  • Security teams maintain full control over sensitive data and compliance boundaries
  • Platform teams eliminate the operational burden of managing observability infrastructure
  • Business leaders benefit from a more efficient observability model.

Imply Lumi’s compression technology enables organizations to store 1TB of raw data while using only one-third the storage capacity.

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Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...