Ask questions in plain language, generate queries and dashboards
Estimated time: 2 min
Grafana Assistant
Grafana Assistant in Grafana Cloud adds the higher-order, agentic features that need the full backend:
Infrastructure memory: Assistant builds and retains context about your environment over time.
Hosted Cloud MCP connections: Zero-install, OAuth 2.1 MCP. Self-managed users run the local OSS MCP server with a manual service account token instead.
CLI auth tokens, anonymous access, and Slack integration: Cloud-native access and collaboration paths
What keeps you up at night?
Role / Worries
What you get with Grafana Assistant in Grafana Cloud
SRE, On-call Engineer
Getting paged with no context
Spending 20 minutes writing queries just to understand what’s happening
Context-switching between tools while the clock runs
Infrastructure memory pre-maps your services and dependencies; no time is lost rebuilding context when a page fires
@Grafana in Slack surfaces dashboards and answers without leaving the incident channel
Developer
Never sure you’re querying the right data source
No idea what your service depends on upstream
Infrastructure memory already knows your environment, dependencies, and which metrics matter
Senior Engineer, Resident Grafana Expert
Receiving all escalations for dashboards and queries
Spending more time unblocking others than doing your own work
Skills reference your actual dashboards and metric names; team gets environment-specific guidance, not generic advice
Engineering Manager, VP Engineering
Best engineers stuck on toil
Knowledge concentrated in one or two people
A growing stack most of the team can’t use
Infrastructure memory gives any on-call engineer immediate context on services and golden signals, not just seniors
Platform Engineer, DevOps
Runbooks that go stale
A gap between what the stack can do and what the team knows to do with it
Skills with MCP auto-approve pre-authorize tool calls to GitHub, Jira, and Slack in a remediation flow; no manual approval mid-incident
Grafana, Platform Admin
Controlling who can use AI features
Preventing Skills from triggering actions they shouldn’t
Managing the Cloud vs self-managed feature gap
Assistant-specific RBAC roles control feature access at a granular level
MCP auto-approve requires each Skill to declare pre-authorized tools; nothing runs without deliberate configuration
Self-managed connects with one click; Cloud-only features are hidden in its UI automatically
Security, Compliance Officer
AI models trained on sensitive production telemetry
Prompts leaving your control
No audit trail for what the AI did during an incident
Requests always route through Grafana Cloud, never directly to AI providers, and only minimal prompt context is sent
Assistant operates within the user’s RBAC scope; it can’t see data the user can’t see
Full tool use and reasoning history in every conversation gives a complete audit trail