PMM What's Shipping: MCP server enterprise readiness

Customer Problem

Enterprise customers cannot adopt MCP due to missing governance, visibility, and compliance capabilities. Regulated industries (financial services, healthcare, government) are blocked from enabling MCP because they lack controls over external AI access to their GitLab data, have no visibility into usage patterns or costs, cannot demonstrate compliance for auditors, and face unpredictable expenses from uncontrolled API consumption. Additionally, foundational authentication reliability issues prevent consistent daily usage.

Solution

Deliver five enterprise-readiness capabilities that address governance, observability, compliance, cost control, and operational reliability:

  1. Instance-level MCP server controls - Admins can disable external AI access completely
  2. Usage analytics and transparency dashboard - Real-time visibility into MCP consumption, costs, and usage patterns
  3. Audit logging and compliance reporting - Complete audit trails for SOC2, ISO, HIPAA requirements
  4. Tiered rate limiting - Admin-controlled usage quotas by plan tier with predictable cost protection
  5. Authentication reliability - Eliminate PKCE errors, OAuth failures, and daily workarounds

Why This Matters

Without these capabilities, we cannot sell MCP to enterprise customers. Current state:

  • Barclays and similar regulated customers are completely blocked from adoption
  • Sales has no answer to "how do we control costs?" or "what about compliance?"
  • Community alternatives (Florian's gitlab-mcp) are more attractive because enterprises can self-host with their own controls
  • Bill won't approve GA without enterprise monetization, which requires these foundational capabilities

With these capabilities:

  • Unlock entire enterprise segment currently blocked (financial services, healthcare, government)
  • Enable consumption-based pricing model (visibility enables billing)
  • Competitive positioning as "enterprise-grade MCP" vs community alternatives
  • Foundation for Bill's agent invocation monetization strategy

Example Workflows

Enterprise Security Team - Control & Compliance:

  1. CISO evaluates MCP for developer productivity initiative
  2. Security team disables MCP server at instance level while evaluating
  3. Pilot with single group, monitor usage via dashboard
  4. Export audit logs showing no unauthorized external AI access
  5. Present compliance evidence to auditors for SOC2 certification
  6. Approve company-wide rollout with group-level rate limits

Finance Team - Cost Management:

  1. Finance asks "what is MCP costing us this quarter?"
  2. Access usage dashboard showing consumption by group/project
  3. See tool call volumes, token usage, cost attribution by team
  4. Set rate limits for teams approaching budget thresholds
  5. Export usage reports for budget planning and chargeback

Platform Engineering - Operational Reliability:

  1. Developers report MCP authentication failures
  2. Platform team investigates using audit logs
  3. Identify pattern of PKCE errors with specific OAuth clients
  4. Apply authentication reliability fixes
  5. Monitor dashboard confirms error rate drops to <1%
  6. Set up alerts for authentication failure spikes

Enterprise Customer - Adoption at Scale:

  1. Start with MCP server disabled (security default)
  2. Enable for Platform Engineering pilot group only
  3. Monitor usage dashboard: 50 developers, 1000 tool calls/day
  4. Review audit logs: no policy violations detected
  5. Set rate limits: 500 calls/user/day to control costs
  6. Expand to 5 more groups with confidence in governance

Value

For Security/Compliance Teams:

  • Sleep at night knowing external AI access is controlled
  • Demonstrate compliance with audit trails for regulators
  • Granular controls match organizational security policies
  • Evidence-based risk assessment (not theoretical)

For Finance/Procurement:

  • Predictable costs through rate limits and visibility
  • Cost attribution by team/project for chargeback
  • Budget planning based on actual usage data
  • No surprise bills from runaway usage

For Engineering Leadership:

  • Adopt AI productivity tools without governance blockers
  • Data-driven decisions on MCP value (usage analytics)
  • Operational reliability for developer workflows
  • Competitive advantage from faster AI adoption than peers

For GitLab:

  • Unlock $XXM enterprise TAM currently blocked
  • Enable consumption-based pricing model
  • Competitive moat vs community alternatives
  • Foundation for agent invocation monetization
Edited by Amanda Rueda