Grafana Assistant Expands to More Than 30 Data Sources

Grafana Labs has expanded Grafana Assistant to query and correlate data across more than 30 data sources through natural language, aiming to unify observability for operators, developers, and SREs. The update supports enterprise sources including Snowflake, Oracle, Elasticsearch, Dynatrace, Honeycomb, MongoDB, Zabbix, and Jira, allowing investigations to span operational, infrastructure, and business context without switching tools.

Grafana Labs https://grafana.com/ has expanded the capabilities of Grafana Assistant, https://grafana.com/blog/stop-switching-tools-to-find-answers-grafana-assistant-now-works-across-30-data-sources/ enabling its AI-powered observability assistant to query and correlate data across more than 30 different data sources through natural language. The company claims that this update is a step toward "unified observability", allowing operators, developers, and site reliability engineers SREs to investigate incidents, generate queries, and troubleshoot complex distributed systems without switching between multiple monitoring tools. The enhancement builds on Grafana Assistant's broader AI strategy unveiled at GrafanaCON 2026 https://www.infoq.com/news/2026/04/grafana-loki-ai-agents/ , where the company positioned AI as an operational partner rather than simply a chatbot. By expanding the number of supported data sources, including cloud platforms, databases, observability backends, issue trackers, and infrastructure monitoring systems, Grafana is attempting to eliminate a common challenge facing modern operations teams: fragmented operational data spread across dozens of disconnected platforms. Modern production environments rarely rely on a single monitoring platform. Instead, organizations typically collect metrics, traces, cloud telemetry, infrastructure events, business data, and operational context all from different tools and sources. Traditionally, investigating a production incident has required engineers to move between these tools, correlate timestamps manually, reconstruct service dependencies, and piece together a complete understanding of what happened. Grafana Assistant aims to simplify that process by allowing users to ask natural language questions that span multiple systems simultaneously. Instead of manually constructing queries in PromQL https://grafana.com/blog/introduction-to-promql-the-prometheus-query-language/ , LogQL https://grafana.com/docs/loki/latest/query/ , SQL https://en.wikipedia.org/wiki/SQL , or TraceQL https://grafana.com/docs/tempo/latest/traceql/ , engineers can simply describe the problem they are investigating and allow the assistant to retrieve and correlate the relevant information. Unlike general-purpose AI assistants, Grafana Labs claims that Grafana Assistant has been designed specifically for observability workflows. Beyond answering questions, it can generate dashboards, construct complex monitoring queries, explain unfamiliar metrics, navigate Grafana resources, and launch investigations using telemetry from across an organization's infrastructure. With the latest release, support has expanded to include additional enterprise data sources such as Snowflake https://www.snowflake.com/en/ , Oracle https://www.oracle.com/ , Elasticsearch https://www.elastic.co/elasticsearch , Dynatrace https://www.dynatrace.com/ , Honeycomb https://www.honeycomb.io/ , MongoDB https://www.mongodb.com/ , Zabbix https://www.zabbix.com/ , and Jira https://www.atlassian.com/software/jira , allowing investigations to incorporate operational, infrastructure, and business context within a single conversation. Rather than forcing users to learn multiple query languages, the idea is that the assistant translates natural language into the appropriate queries while respecting existing permissions and role-based access controls. The announcement reflects broader shifts occurring within observability itself. As distributed systems become more complex and AI-powered applications generate increasing volumes of telemetry, simply collecting data is no longer enough. Operations teams increasingly need intelligent systems capable of correlating information across logs, metrics, traces, profiles, infrastructure events, and application behavior. As a result, like its competitors, Grafana has steadily expanded its AI portfolio throughout 2026, introducing AI Observability for monitoring large language model applications, Model Context Protocol MCP https://modelcontextprotocol.io/docs/getting-started/intro support for integrating external AI agents, and making Grafana Assistant available to self-managed Grafana Enterprise and OSS deployments. Grafana's announcement comes as observability vendors increasingly compete on AI capabilities rather than telemetry collection alone. Datadog https://www.datadoghq.com/ has expanded its AI-powered Bits AI https://www.datadoghq.com/product/ai/bits-ai-agents/ assistant to perform automated investigations and root cause analysis, while Dynatrace's Davis AI https://www.dynatrace.com/news/blog/davis-ai-your-personal-interactive-troubleshooting-assistant/ combines causal reasoning with automation to accelerate incident response. Splunk https://www.splunk.com/ integrates AI Assistant into security and observability workflows, and New Relic https://newrelic.com/ has introduced Intelligent Observability https://newrelic.com/platform features that automatically correlate telemetry and recommend remediation actions. While natural-language interfaces can make investigations faster and more accessible, the quality and accuracy of an assistant's responses remain dependent on several factors, including the completeness of the underlying telemetry, appropriate access permissions, and the ability of AI models to reliably generate and execute queries across diverse and heterogeneous data sources.