Part 1: Grafana Cloud's own AI governance capabilities. Part 2: How Grafana and Snowflake combine to close the full governance loop from data access to runtime quality.
What Grafana Cloud brings to the AI governance table — out-of-band observability, evaluation, and cost control for AI workloads.
Grafana Cloud's AI governance story sits within Agent Observability (GA as of AI Week, July 2026) and the broader Grafana Assistant ecosystem. The key design principle: Grafana operates out-of-band (observe, evaluate, measure) rather than in-band (block, intercept).
Grafana is the observability and quality layer for AI — it doesn't want to be a firewall, proxy, or DLP engine. It complements inline security vendors, not replaces them.
The primary AI governance product. GA since July 2026.
Programmatic evaluators run on top of agent traces to score for hallucination, toxicity, bias, and drift. Triggers alerts when model quality degrades over time.
Enforce customer-configured policies and block unwanted behavior in real time — catching hallucinations, drift, or policy violations before they reach end users.
Tracks latency, token usage, agent decision-making behavior, and cost-per-query alongside infrastructure telemetry (CPU, memory, GPU).
Traces the full request path (prompt → security proxy → vector DB → LLM → output guardrail) so failures are pinned to a specific component, not left ambiguous.
Surfaces every tool call an agent makes — what it accessed, what it returned — creating unexpected governance value beyond the original observability use case.
Five key areas: GenAI observability, GenAI evaluations, vector database observability, MCP observability, and GPU monitoring.
Grafana Assistant respects existing Grafana RBAC — it only accesses data the logged-in user can already see. No data centralization or copying. Admins set max unique active users per stack per month.
Routes AI inference through EU-region infrastructure to support GDPR and data residency requirements. Available now for EU customers.
Grafana traces the full request path of AI workloads using OpenTelemetry standards:
| Trace Segment | What It Shows |
|---|---|
| Prompt Ingestion | User input, preprocessing, tokenization timing |
| Security Proxy | Guardrail scan latency, blocked/allowed decisions |
| Vector DB Retrieval | Query latency, resource utilization, protocol health (RAG context fetch) |
| LLM Inference | Model call latency, token counts (input/output), model version |
| Output Guardrail | Post-hoc evaluation scores, policy enforcement decisions |
| Infrastructure | GPU usage, CPU, memory — correlated with the agent behavior above |
This means a failure can be attributed to a specific component — a security denial, a database timeout, or a model error — not left ambiguous across siloed dashboards.
Grafana tracks token burn and cost-per-query alongside operational events, answering questions like:
This is a cross-provider, cross-stack view — not scoped to a single AI vendor or hosting platform.
Grafana is complementary (~90%) to inline AI security vendors, with a real ~10% functional overlap on output quality scoring.
| Group | Key Players | Their Approach | Why Grafana Is Different |
|---|---|---|---|
| AI Security & Governance | Prompt Security (SentinelOne), HiddenLayer, Wiz, Cyera | Sit inline — blocking prompt injections, PII leaks, scanning model files | Complementary. These tools export alerts into Grafana dashboards. Grafana adds full-stack correlation + cost visibility. |
| Agent-First Point Solutions | Langfuse, LangSmith, Braintrust, Arize Phoenix | Track prompts, tool calls, token usage inside the LLM layer only | Grafana correlates agent behavior with the infrastructure running it — one investigation, not a silo. |
| AI Orchestration | LangGraph, LangChain, CrewAI, AutoGen | Define agent logic and sequencing, but don't observe production behavior | Complementary. Grafana ingests OTel traces these frameworks emit and adds cost, infra context, and correlation. |
| Direct Observability | Datadog, Dynatrace, New Relic | AI investigates within each vendor's own instrumentation boundary | Cross-source investigations across 150+ data sources and any OTel-native stack, in a single workflow. |
| Frontier AI (Coding) | Claude Code, Cursor, Copilot | Write code without a live connection to production telemetry | Complementary. gcx feeds live production telemetry into the coding workflow — accurate diagnosis, not a best guess. |
How the two platforms combine to close the full governance loop from data access to runtime quality.
Snowflake and Grafana occupy different layers of the AI governance stack. Snowflake governs the data and model access plane — who can touch what, is the data classified, are prompts safe. Grafana governs the operational and runtime observability plane — is the agent actually producing quality output, what's happening across the full request path, and what's it costing you end-to-end.
Snowflake governs the inputs. Grafana governs the outputs and operations. Together they create a closed governance loop.
Data access control, PII classification, masking policies, prompt-injection blocking, agent identity verification, data quality & lineage
Full-stack OTel tracing, agent output evaluations, runtime guards, cost-per-query tracking, cross-source incident correlation
Snowflake's governance story (as of Summit '26) centers on Horizon Catalog — the universal AI catalog with context and governance for all data.
Shared semantic definitions so humans, BI tools, and AI agents use the same business logic. Includes Semantic Studio and Autopilot.
Single governed endpoint for AI inference — model access control, cost attribution by user/team, usage logging with OTel traces.
Inline prompt-injection & jailbreak detection for CoCo, CoWork, and Cortex Agents. Zero-day protection via contextual reasoning.
Natural-language governance — classify PII (150+ categories), create masking policies, audit access, trace lineage, all without SQL.
Verified identity for agents before they touch enterprise data, with full audit trail and role-based permission enforcement.
Continuous security posture monitoring — misconfigured roles, unprotected columns, with AI-guided investigation and risk response.
Key design principle: Governance policies execute at the query engine layer — every caller (human, BI tool, or AI agent) gets the same policies automatically.
| Governance Dimension | Snowflake Horizon | Grafana Cloud | Together |
|---|---|---|---|
| Data access control | ✅ RBAC, masking, row-access policies, Agent Identity | — | Snowflake controls who sees what |
| Data quality & lineage | ✅ DMFs, column-level lineage, trust scoring | — | Snowflake owns the data layer |
| PII / sensitive data | ✅ Classification (150+ categories), masking policies | — | Snowflake classifies and masks |
| Inline prompt security | ✅ Cortex AI Guardrails (injection, jailbreak, zero-day) | — (out-of-band) | Snowflake blocks; Grafana measures downstream impact |
| Agent output quality | — | ✅ Agent Observability evals (hallucination, toxicity, bias, drift) | Grafana scores what got through |
| Runtime Guards | — | ✅ Customer-configured policy enforcement, blocks unwanted behavior | Grafana catches quality/policy violations at the output layer |
| Full-stack tracing | Gateway logs metadata + optional payloads | ✅ OTel traces: prompt → proxy → vector DB → LLM → output | Grafana correlates Snowflake gateway traces with infra & agent behavior |
| Cost governance | ✅ Gateway cost attribution per user/team | ✅ Token burn + cost-per-query across all providers + infra | Snowflake tracks Cortex spend; Grafana tracks total AI cost |
| Operational observability | Cortex Agent monitoring (Snowsight) | ✅ Dashboards, alerts, SLOs, incident workflows across 150+ sources | Grafana is the cross-stack operational hub |
| Security posture | ✅ Trust Center monitors misconfig, audit logs | Ingests security telemetry, correlates with SRE workflows | Security alerts flow into PagerDuty/runbooks SREs already use |
"Snowflake Horizon is the governance layer for your data and AI access — who can touch what, is the data classified, are prompts safe. Grafana Cloud is the governance layer for your AI runtime — is the agent actually producing quality output, what's happening across the full request path, and what's it costing you end-to-end.
Snowflake governs the inputs; Grafana governs the outputs and operations. Together you get a closed loop: Snowflake ensures agents only see what they should, and Grafana ensures what they produce is actually good — and catches it when it isn't."
Grafana is complementary (~90%) to inline AI security vendors, not a replacement. Important to understand these boundaries in a partner conversation:
| Gap | Detail |
|---|---|
| No inline blocking | Cannot intercept or scrub a payload mid-flight — that's Snowflake Guardrails, Prompt Security, Lakera, etc. |
| No threat-intelligence DB | No jailbreak / prompt-injection pattern database |
| No model-file inspection | Cannot red-team or scan static model files for backdoors (HiddenLayer's domain) |
| No fine-grained DSPM / identity governance | Guards enforces customer-configured rules — it doesn't decide company-wide data-access policy (Cyera / Snowflake's domain) |
Internal: Grafana AI Sales Playbook (knowledge library, Aug 2026 competitive research) · Grafana AI — Source of Truth · Agent Observability Messaging & Positioning · Agent Observability Customer Stories · Grafana Assistant FAQ & Competitive Playbook · Grafana Assistant Capabilities & Roadmap
External: How to monitor LLMs in production with Grafana Cloud (Mar 2026) · AI Week announcement (Jul 2026) · Snowflake Horizon Catalog docs · Snowflake Summit '26 press release (Jun 2, 2026) · Cortex AI Gateway docs · Cortex AI Guardrails docs · Cortex Code Governance Skills blog (Mar 2026)