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CONCEPT Cited by 3 sources

Centralized AI governance

Centralized AI governance is the pattern-concept of routing all of an organisation's AI traffic (LLM calls + tool calls + MCP traffic + coding-agent activity) through one policy surface that owns security/audit, cost controls, and observability — rather than delegating any of those concerns to individual tools or teams.

The three pillars (named in the 2026-04-17 Databricks post)

Databricks frames Unity AI Gateway's value proposition around three pillars. The framing is general, not Databricks-specific, and matches the same-era Cloudflare internal-stack instance:

  1. Centralised security and audit.
  2. One identity substrate for all tools (single SSO across coding agents, MCP servers, internal services).
  3. Every request audit-logged in a single store (Unity Catalog on Databricks' instance; Workers KV / D1 + AI Gateway logs on Cloudflare's).
  4. Per-provider credentials injected server-side — no keys on user laptops (BYOK).
  5. Single bill + cost controls.
  6. Inference capacity available from a first-party source (Databricks Foundation Model API; Cloudflare Workers AI) with external capacity bring-your-own supported.
  7. One bill, one budget model, budgets enforced per identity, not per tool — patterns/unified-billing-across-providers.
  8. Gateway sees all traffic → quotas are portable across whichever tool the developer picks.
  9. Unified observability.
  10. All tool telemetry flows into one observability store (Unity-Catalog-managed Delta tables via OpenTelemetry on Databricks; AI Gateway logs + Workers Analytics on Cloudflare).
  11. Joinable with business data (HR / PR-velocity / rate-limit capacity-planning signals).
  12. patterns/telemetry-to-lakehouse — Databricks' specialised shape.

Why one surface beats three surfaces

  • Security review is O(1), not O(N tools). Each new tool adoption doesn't open a new vendor-security review — it connects to the gateway.
  • Policy migration cost is constant. When frontier models ship weekly, repointing the gateway is one config change, not N per-tool config changes.
  • Cross-tool analytics become possible. "Who's using AI?" has a tractable answer when all tools report through one telemetry plane.

Two ingested instances

Both converge on the same three-pillar shape. Different substrates; same architecture; all three validate the pattern. The Cloudflare customer-facing instance sharpens the framing with the reliability tier — failover + resumability as gateway-owned concerns, not application concerns.

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