Crest Data, an AI-first product engineering provider, has launched 15 new commercial plugins for Grafana, more than doubling its Grafana Marketplace portfolio to 25. The release coincides with Grafana Labs moving the Marketplace to general availability at ObservabilityCON 2026 in San Francisco on October 21, 2026. The new plugins cover cloud security, virtualisation and data protection platforms. IT, platform and security teams can monitor those systems in Grafana rather than switching between separate vendor consoles. The plugins are agentless and query source systems through their APIs, with no exporters, extra collection layer or data ingestion costs. Each ships with pre-built dashboards. "Crest Data has been developing integrations used by Fortune 100 customers for years," said Malhar Shah, co-founder and chief executive officer of Crest Data.

Grafana adds knowledge graph to link telemetry & users. Tue, 20th Oct 2026 (Today) Grafana Labs has added a Knowledge Graph to every Grafana Cloud stack, extending its observability tools from infrastructure to end users. The release also includes new digital experience monitoring features and updates to Grafana Cloud Incident Response Management, aimed at linking system telemetry, user experience and incident handling in one environment. Grafana said the Knowledge Graph is designed to give engineering teams and software agents a shared view of relationships across databases, applications, Kubernetes clusters, cloud services, browsers and mobile devices. That shared runtime context can help users trace a problem from a technical fault to the affected service, team and customer. Richard Lam, director of product at Grafana Labs, said many organisations already collect data from different parts of their technology stack but struggle to connect it. "Most teams do not have a monitoring problem. They have a technology debt problem: telemetry from every layer and no way to connect it to the customer it affects," Lam said. "Putting a Knowledge Graph in every Grafana Cloud stack, and extending it from the infrastructure all the way to the user, makes unified observability foundational instead of something every team has to assemble on its own, for people and for the agents working beside them." Grafana said the graph acts as a runtime context layer for Grafana Cloud. It is built from telemetry collected across Grafana's observability products and can be extended with custom entities defined by customers to reflect business-specific services or products. New functions include the general availability of custom entities, which let teams organise discovered infrastructure into business-relevant groupings. Grafana also introduced a service catalogue with ownership in private preview, bringing in service ownership, on-call schedules and runbooks, and made runtime context for agents generally available. The update also adds a Cypher query feature intended to let agents retrieve relationships from the graph rather than rebuild context from raw telemetry. User monitoring Alongside the Knowledge Graph, Grafana expanded its digital experience monitoring tools to cover browser and mobile interactions more directly. This is intended to close the gap between back-end system health and the customer's actual experience of a product. Among the additions is Session Replay, now in public preview, which records user sessions in a video-style format so teams can examine what happened when an issue occurred. Mobile Observability, in private preview, extends monitoring to iOS, Android, React Native and Flutter applications through OpenTelemetry software development kits. Grafana also introduced Critical User Journeys in private preview. The feature allows teams to define which user flows matter most and inspect them from a business metric such as conversion decline through to the underlying technical cause. Grafana pointed to findings from its survey of 300 IT and engineering leaders conducted with ETR, a Futurum company, which found that 30% were at a stage where they could explain unexpected system behaviour but not detect and prevent issues before users were affected. Incident response Grafana also updated its incident response tools as it seeks to address a growing mix of human responders and AI systems working on the same operational problems. Without a shared record, engineers and agents can duplicate investigations during an incident, it said. New IRM functions include Grafana Workflows, in public preview, described as event-driven automation that connects actions across Grafana Cloud and other tools. Grafana said prebuilt workflow templates cover major incident kickoff, on-call handoff reports and weekly service health summaries. Operational Context with Knowledge Graph, in private preview, is intended to record what previous responders tried, discovered and ruled out, so later investigations can begin with that information. Response Console, also in private preview, offers a single interface where people and agents can work on incidents using the same Knowledge Graph context. Neil Wilson, senior director of software engineering at LexisNexis Risk Solutions, said the challenge for operations teams lies less in collecting data than in tying it to ownership and impact. "The future of operations won't be defined by who collects the most telemetry, but by who can provide the most accurate context. Telemetry tells us what's happening. Context tells us why it matters," Wilson said. "We've found that the hardest part of an investigation is rarely a lack of data. The challenge is connecting service ownership, dependencies, operational knowledge, and customer impact across a complex ecosystem. Entity Graph relationships help make those connections visible, giving engineers and AI systems the context they need to move from data, to understanding, to action."

Grafana Labs boosts log search as AI telemetry surges. Wed, 21st Oct 2026 (Today) Grafana Labs has added faster full-text search to Grafana Cloud Logs and introduced Adaptive Logs Archive in public preview, as it looks to manage rising telemetry volumes linked to AI use. The search improvements for Grafana Cloud Logs are now generally available, while Adaptive Logs Archive has entered public preview. Grafana Labs also announced broader updates to its Adaptive Telemetry tools for metrics and traces. The changes are aimed at a familiar problem for engineering teams as software output rises and AI systems generate more operational data. Grafana Labs cited survey findings showing that 78% of 300 IT and engineering leaders increased observability spending in the past year, while 73% expect to do so again. The faster search in Grafana Cloud Logs is built on technology from Logline, a business Grafana Labs acquired earlier this year. The feature is designed to help users identify low-frequency values such as request IDs and customer IDs in object storage more quickly, at no extra charge. That matters during operational investigations, when engineers may need to locate a specific log line quickly after receiving an alert. Grafana Labs added that software agents are now sending more queries than human users, increasing pressure on search performance. Jen Villa, Senior Director of Product at Grafana Labs, outlined the company's view of the cost challenge facing observability teams. "The economics of observability only work if you keep the data worth keeping and can dial that up or down as your system's state changes, and that's more true than ever with the explosion in telemetry driven by AI," said Jen Villa, Senior Director of Product at Grafana Labs. "But even the data worth keeping is still massive, so you still need to be able to find what you're looking for in it, fast. Adaptive Telemetry handles the first, and the query performance enhancements we've made in Grafana Cloud Logs handle the second." Search speed Customer DATEV said it had seen a marked reduction in the amount of log data scanned after enabling the Logline-based function. "When I ran our first query on newly submitted logs after enabling Logline, the improvement was clear. We went from processing nearly a terabyte of logs in three minutes to around 100 gigabytes in under three seconds," said Timo Kirzdörfer, OpenTelemetry & Grafana Cloud Admin at DATEV eG. "That represents an 80-90 percent reduction in the volume of logs searched for newly submitted logs, and we saw the results immediately." Alongside the search changes, Grafana Labs expanded the scope of its Adaptive Telemetry software, which recommends which telemetry data should be sampled, aggregated or kept. Customers using several components of the suite have reduced telemetry spending by 30% to 50% on average, according to the company. Adaptive Logs Archive lets teams send logs to their own object storage bucket in an open format instead of storing that data in Grafana Cloud. The aim is to let companies retain infrequently needed logs at lower cost while preserving access for investigations, audits and compliance requests. Grafana Labs also moved Adaptive Metrics Auto-Apply to general availability. The tool automatically applies cardinality-reduction recommendations without requiring manual review of each one, while a setting called Cost Mode is intended to prevent recommendations from increasing the total number of active series. For traces, users can now query traces dropped by Adaptive Traces for up to 24 hours using a trace ID. This is intended to let teams sample more aggressively while still being able to retrieve a specific trace during an investigation. Cost pressure The broader backdrop is a sustained rise in observability spending as companies add more services, software and AI-driven workflows. Engineering teams have long faced a trade-off between retaining enough telemetry for troubleshooting and controlling storage and query costs. Rapid7 said the adaptive tools had reduced the amount of manual analysis needed to identify waste in logs and metrics. "Before, finding waste meant someone manually digging through metric cardinality and log volume, and the job never ended because new services kept adding more. Adaptive Metrics and Adaptive Logs now do that analysis for us," said Carl Eastman, Principal Software Engineer at Rapid7. "They show which metrics and log patterns are never queried and recommend what to aggregate or drop, and we apply those recommendations instead of hunting for them. Our engineers spend their time on the few rules that need human judgment, like dropping debug logs we know are noise, rather than the whole long tail."