Skip to main content

Honeycomb Launches New Private Cloud Offering

Honeycomb announced a series of new product advancements: Honeycomb Private Cloud, Honeycomb Metrics, and Canvas, a part of Honeycomb Intelligence.

Together, these new capabilities bolster Honeycomb's enterprise portfolio, combining security, performance, and agentic experiences to help organizations deliver resilient software at scale.

With Honeycomb Private Cloud, organizations get a dedicated AWS infrastructure built to meet stringent security, data residency, and regulatory requirements—all without sacrificing performance or developer experience. By providing complete management over data and environment isolation, this offering is especially well-suited for customers in highly regulated industries like finance and healthcare, who need to confidently access the lighting-fast query performance and intuitive workflows that define Honeycomb's platform.

Customers are able to select self-managed or Honeycomb-managed options. Additionally, this new Bring Your Own Cloud (BYOC) option offers a cost-effective solution for organizations that want to keep their data in their own cloud accounts and control costs over time using their existing AWS discounts.

With the introduction of overhauled and improved Metrics, Honeycomb is expanding its observability platform to include native support for standard OpenTelemetry metrics. This builds on Honeycomb's longstanding support for custom metrics through events, giving teams a unified way to explore both high-level signals and detailed event data in one place.

With Metrics, users can now bring in gauges, counters, and histograms to track trends, monitor system health, and detect performance changes over time. All of this happens within the same intuitive Honeycomb experience. Unlike traditional monitoring tools that separate system data from application behavior, Honeycomb's unified model allows engineers to seamlessly connect what is happening in their infrastructure to why it is happening in their applications.

This new capability gives teams faster insights, deeper context, and a clearer path from detection to resolution, making it easier than ever to understand how their systems and users interact.

"Full time-series metrics are the industry standard for most developers and site reliability engineers," said Graham Siener, VP of Product at Honeycomb. "Our new Metrics capabilities meet customers where they are, combining open-standard metrics methodologies with our industry-leading tracing data to better determine the context around critical issues. The Honeycomb Intelligence platform, which includes our improved Metrics, is purpose-built for teams of every size and provides a platform for success that organizations can use to navigate their AI journey."

Also launching for General Availability is Canvas, Honeycomb's AI-guided dashboard that blends natural language investigation with interactive notebooks for collaborative debugging.

With Canvas, engineers can ask questions in plain English and watch as Honeycomb autonomously explores telemetry data, surfaces anomalies, and visualizes findings in real time. Canvas runs multiple queries, comparisons, and BubbleUp analyses automatically, producing dynamic charts and trace visualizations that evolve as the investigation progresses.

As part of Honeycomb Intelligence, Canvas works with MCP Server and Anomaly Detection, bringing AI-driven investigation directly into the developer workflow rather than bolting it on as an afterthought. With these new offerings, Honeycomb further extends its leadership in helping enterprises achieve faster resolution, tighter control, and deeper understanding across distributed systems.

The Latest

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 ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...

Honeycomb Launches New Private Cloud Offering

Honeycomb announced a series of new product advancements: Honeycomb Private Cloud, Honeycomb Metrics, and Canvas, a part of Honeycomb Intelligence.

Together, these new capabilities bolster Honeycomb's enterprise portfolio, combining security, performance, and agentic experiences to help organizations deliver resilient software at scale.

With Honeycomb Private Cloud, organizations get a dedicated AWS infrastructure built to meet stringent security, data residency, and regulatory requirements—all without sacrificing performance or developer experience. By providing complete management over data and environment isolation, this offering is especially well-suited for customers in highly regulated industries like finance and healthcare, who need to confidently access the lighting-fast query performance and intuitive workflows that define Honeycomb's platform.

Customers are able to select self-managed or Honeycomb-managed options. Additionally, this new Bring Your Own Cloud (BYOC) option offers a cost-effective solution for organizations that want to keep their data in their own cloud accounts and control costs over time using their existing AWS discounts.

With the introduction of overhauled and improved Metrics, Honeycomb is expanding its observability platform to include native support for standard OpenTelemetry metrics. This builds on Honeycomb's longstanding support for custom metrics through events, giving teams a unified way to explore both high-level signals and detailed event data in one place.

With Metrics, users can now bring in gauges, counters, and histograms to track trends, monitor system health, and detect performance changes over time. All of this happens within the same intuitive Honeycomb experience. Unlike traditional monitoring tools that separate system data from application behavior, Honeycomb's unified model allows engineers to seamlessly connect what is happening in their infrastructure to why it is happening in their applications.

This new capability gives teams faster insights, deeper context, and a clearer path from detection to resolution, making it easier than ever to understand how their systems and users interact.

"Full time-series metrics are the industry standard for most developers and site reliability engineers," said Graham Siener, VP of Product at Honeycomb. "Our new Metrics capabilities meet customers where they are, combining open-standard metrics methodologies with our industry-leading tracing data to better determine the context around critical issues. The Honeycomb Intelligence platform, which includes our improved Metrics, is purpose-built for teams of every size and provides a platform for success that organizations can use to navigate their AI journey."

Also launching for General Availability is Canvas, Honeycomb's AI-guided dashboard that blends natural language investigation with interactive notebooks for collaborative debugging.

With Canvas, engineers can ask questions in plain English and watch as Honeycomb autonomously explores telemetry data, surfaces anomalies, and visualizes findings in real time. Canvas runs multiple queries, comparisons, and BubbleUp analyses automatically, producing dynamic charts and trace visualizations that evolve as the investigation progresses.

As part of Honeycomb Intelligence, Canvas works with MCP Server and Anomaly Detection, bringing AI-driven investigation directly into the developer workflow rather than bolting it on as an afterthought. With these new offerings, Honeycomb further extends its leadership in helping enterprises achieve faster resolution, tighter control, and deeper understanding across distributed systems.

The Latest

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 ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...