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Honeycomb Introduces Refinery

Honeycomb announced Refinery to help enterprises refine their observability data at scale.

Managing large, complex applications means dealing with potentially hundreds of billions of events per month. Refinery collects 100 percent of telemetry data and provides multiple ways to observe only the events that best represent important system changes, resulting in a high-fidelity debugging experience while also controlling costs.

With the complexity of modern software architectures, enterprises need observability to identify and resolve issues in their production apps faster and to build more resilient services. Refinery is the latest addition to Honeycomb's Enterprise offerings that give developers, site reliability engineers (SREs), and DevOps engineers the tools they need to quickly troubleshoot some of the world's largest and most complex software applications.

In addition to Refinery, Honeycomb's enterprise offering includes:

- Service-Level Objectives (SLOs) that help technical and business stakeholders align on critical customer experience thresholds. Unlike other SLO solutions that only provide alerts and force teams to debug in other systems, Honeycomb's SLOs are debuggable, so that teams know what is wrong and how to fix issues within a single interface.

- Secure Tenancy, Honeycomb's patented approach that enables organizations to protect sensitive data and comply with their off-premises data security policies

- Exclusive training and onboarding support needed at enterprise scale. Customized onboarding packages and top-level support improve an organization's production service management skills and accelerate their ability to resolve issues.

"At large enough scale, there's a tradeoff between providing engineers the data they need to maintain the kind of digital experiences customers expect and the resource costs of sending all that data to Honeycomb," said Christine Yen, CEO of Honeycomb. "Honeycomb is always looking for ways to democratize the tools and expertise that used to be reserved for only the world's most elite companies. Previously, solutions like Refinery have been proprietary and closed-source. Now, Honeycomb makes it easy for anyone to only keep the most important data they need to debug their production services and stop paying for the rest."

Refinery delivers significant benefits for the enterprise, including:

- Cost predictability and control without sacrificing fidelity. Capture 100% of your observability data and use a variety of out-of-the-box configuration options to determine which events best represent important system changes. Honeycomb uses those samples to rebuild data accurately, providing a high-fidelity observability experience for a fraction of the cost.

- The control customers need, backed entirely by Honeycomb support. Refinery runs within a customer's infrastructure, giving them full control over what they choose to send to Honeycomb. Honeycomb supports every aspect of using Refinery, including installation, configuration, usage, troubleshooting, maintenance, and more.

- Works with Secure Tenancy and OpenTelemetry. It's critical that all components of an enterprise stack work together seamlessly. Refinery simply just works with our patented approach to data privacy and with vendor-neutral instrumentation options like the OpenTelemetry project.

"Modern applications and microservices require more data than ever for effective troubleshooting based on system observability, which can become prohibitively expensive," said James Governor, RedMonk co-founder. "With Refinery, Honeycomb is addressing these cost concerns to encourage wider enterprise adoption of its web scale technologies."

Honeycomb is offering a free 30-day Enterprise trial for a limited time.

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Honeycomb Introduces Refinery

Honeycomb announced Refinery to help enterprises refine their observability data at scale.

Managing large, complex applications means dealing with potentially hundreds of billions of events per month. Refinery collects 100 percent of telemetry data and provides multiple ways to observe only the events that best represent important system changes, resulting in a high-fidelity debugging experience while also controlling costs.

With the complexity of modern software architectures, enterprises need observability to identify and resolve issues in their production apps faster and to build more resilient services. Refinery is the latest addition to Honeycomb's Enterprise offerings that give developers, site reliability engineers (SREs), and DevOps engineers the tools they need to quickly troubleshoot some of the world's largest and most complex software applications.

In addition to Refinery, Honeycomb's enterprise offering includes:

- Service-Level Objectives (SLOs) that help technical and business stakeholders align on critical customer experience thresholds. Unlike other SLO solutions that only provide alerts and force teams to debug in other systems, Honeycomb's SLOs are debuggable, so that teams know what is wrong and how to fix issues within a single interface.

- Secure Tenancy, Honeycomb's patented approach that enables organizations to protect sensitive data and comply with their off-premises data security policies

- Exclusive training and onboarding support needed at enterprise scale. Customized onboarding packages and top-level support improve an organization's production service management skills and accelerate their ability to resolve issues.

"At large enough scale, there's a tradeoff between providing engineers the data they need to maintain the kind of digital experiences customers expect and the resource costs of sending all that data to Honeycomb," said Christine Yen, CEO of Honeycomb. "Honeycomb is always looking for ways to democratize the tools and expertise that used to be reserved for only the world's most elite companies. Previously, solutions like Refinery have been proprietary and closed-source. Now, Honeycomb makes it easy for anyone to only keep the most important data they need to debug their production services and stop paying for the rest."

Refinery delivers significant benefits for the enterprise, including:

- Cost predictability and control without sacrificing fidelity. Capture 100% of your observability data and use a variety of out-of-the-box configuration options to determine which events best represent important system changes. Honeycomb uses those samples to rebuild data accurately, providing a high-fidelity observability experience for a fraction of the cost.

- The control customers need, backed entirely by Honeycomb support. Refinery runs within a customer's infrastructure, giving them full control over what they choose to send to Honeycomb. Honeycomb supports every aspect of using Refinery, including installation, configuration, usage, troubleshooting, maintenance, and more.

- Works with Secure Tenancy and OpenTelemetry. It's critical that all components of an enterprise stack work together seamlessly. Refinery simply just works with our patented approach to data privacy and with vendor-neutral instrumentation options like the OpenTelemetry project.

"Modern applications and microservices require more data than ever for effective troubleshooting based on system observability, which can become prohibitively expensive," said James Governor, RedMonk co-founder. "With Refinery, Honeycomb is addressing these cost concerns to encourage wider enterprise adoption of its web scale technologies."

Honeycomb is offering a free 30-day Enterprise trial for a limited time.

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Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...