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Observe Announces Hubble

Observe introduced Hubble to improve user productivity with a completely revamped Explorer interface, which includes numerous generative AI features, facilitating product help, coding, RegEx generation, incident workflow and more.

Also announced is $50 million in Series A3 financing led by Sutter Hill Ventures, bringing total funding raised to date to $164.5 million.

"We've always viewed observability - first and foremost - as a data problem. To troubleshoot unknown problems, users need immediate access to relevant contextual data. And they need to be able to afford to retain and analyze that data a year or more," said Jeremy Burton, CEO, Observe Inc. "Observe has a modern architecture. All the data is in one place, we derive relationships within it and because we separate storage and compute, it's stored economically for however long the customer needs it."

Hubble allows users to discover things they could never see before at a scale previously thought impossible. New Explorer interfaces to logs and metrics simplify the onboarding experience for new users and now feature a 'Live' mode enabling data to be queried in 20 seconds or less from the time it was created.

Embedded within the new explorers are generative AI features which improve productivity by 20-50% depending on the experience of the user. New AI features in the product now include:

- O11y GPT Help: An in-product chatbot assistant that responds to natural language inquiries from the user about Observe capabilities, 'how-to' tasks or error messages.

- O11y GPT Extract: A RegEx generation tool that parses data to add structure to logs on-the-fly. With O11y Extract, users never have to write a RegEx again.

- O11y GPT Slack Assistant: O11y GPT as an assistant embedded into Slack helping users troubleshoot issues and summarize threads for incident response.

- OPAL Co-Pilot: An assistant that generates OPAL code – Observe's query language – in response to natural language inputs e.g. // extract ip_address from log.

Almost all users currently analyze their observability data within Observe. Hubble expands the range data access options, including a supported Public API; a Command Line Interface (CLI); export to CSV; and data sharing to Snowflake so users can do further analysis using their Business Intelligence tool of choice.

Observe enables users to be up and running in minutes via Observe Apps - pre-built packages of content containing Observe's opinion on how to observe specific environments. With Hubble, users now have access to new apps for MySQL, PostgreSQL, MongoDB Atlas, Prometheus, OpenAI, Threat Intelligence, Security Onion and Orca Security.

"Observe has taken a unique approach to observability and we're very pleased at their progress in the last year," said Mike Speiser, managing partner, Sutter Hill Ventures. "Customers are starting to realize that old tools can't solve new problems and everyone will need to re-architect over the next few years. The market opportunity ahead for Observe is vast."

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Observe Announces Hubble

Observe introduced Hubble to improve user productivity with a completely revamped Explorer interface, which includes numerous generative AI features, facilitating product help, coding, RegEx generation, incident workflow and more.

Also announced is $50 million in Series A3 financing led by Sutter Hill Ventures, bringing total funding raised to date to $164.5 million.

"We've always viewed observability - first and foremost - as a data problem. To troubleshoot unknown problems, users need immediate access to relevant contextual data. And they need to be able to afford to retain and analyze that data a year or more," said Jeremy Burton, CEO, Observe Inc. "Observe has a modern architecture. All the data is in one place, we derive relationships within it and because we separate storage and compute, it's stored economically for however long the customer needs it."

Hubble allows users to discover things they could never see before at a scale previously thought impossible. New Explorer interfaces to logs and metrics simplify the onboarding experience for new users and now feature a 'Live' mode enabling data to be queried in 20 seconds or less from the time it was created.

Embedded within the new explorers are generative AI features which improve productivity by 20-50% depending on the experience of the user. New AI features in the product now include:

- O11y GPT Help: An in-product chatbot assistant that responds to natural language inquiries from the user about Observe capabilities, 'how-to' tasks or error messages.

- O11y GPT Extract: A RegEx generation tool that parses data to add structure to logs on-the-fly. With O11y Extract, users never have to write a RegEx again.

- O11y GPT Slack Assistant: O11y GPT as an assistant embedded into Slack helping users troubleshoot issues and summarize threads for incident response.

- OPAL Co-Pilot: An assistant that generates OPAL code – Observe's query language – in response to natural language inputs e.g. // extract ip_address from log.

Almost all users currently analyze their observability data within Observe. Hubble expands the range data access options, including a supported Public API; a Command Line Interface (CLI); export to CSV; and data sharing to Snowflake so users can do further analysis using their Business Intelligence tool of choice.

Observe enables users to be up and running in minutes via Observe Apps - pre-built packages of content containing Observe's opinion on how to observe specific environments. With Hubble, users now have access to new apps for MySQL, PostgreSQL, MongoDB Atlas, Prometheus, OpenAI, Threat Intelligence, Security Onion and Orca Security.

"Observe has taken a unique approach to observability and we're very pleased at their progress in the last year," said Mike Speiser, managing partner, Sutter Hill Ventures. "Customers are starting to realize that old tools can't solve new problems and everyone will need to re-architect over the next few years. The market opportunity ahead for Observe is vast."

The Latest

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 ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...