Need AI FinOps
Organizations need to understand and control the total cost of ownership across AI models, MCP servers, and third-party services.
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Persona Story:
Laura, the head of AI, needs to understand and control the total cost of ownership across AI models, MCP servers, and the third-party services they consume.
Problem Context
- AI costs are multi-dimensional (usage-based, model-based, API call-based, input/output tokens, caching)
- No single vendor provides cross-platform cost visibility
- Teams lack ability to set budgets, thresholds, and alerts to prevent runaway costs
- Leadership is demanding AI FinOps as a 2026 priority
Problem Impact
- Unpredictable and uncontrollable AI-related expenses
- Unable to attribute costs to specific teams, projects, or use cases
- No proactive controls to stop runaway agent costs before they escalate
- Difficulty calculating ROI for AI investments without cost attribution
Naftiko Today
- OutputParameters normalization ensures only relevant data reaches the LLM, reducing token consumption by filtering out unnecessary API payload content
- Declarative capability specs make API consumption patterns visible and auditable, enabling cost attribution per capability
- Multi-source consumption with unique namespaces provides clear boundaries for tracking which external services each capability uses
- Lookup/JOIN operations perform server-side enrichment, reducing the number of round-trip API calls and associated costs
Naftiko Tomorrow
- HTTP cache control (May 2026) would reduce redundant API calls and associated costs by caching responses
- Control port (May 2026) would provide operational visibility into running capabilities, enabling usage monitoring
- Resiliency patterns including rate limiter and bulkhead (June 2026) would prevent runaway agent costs by throttling and isolating API consumption
- Cost Insights plugin for Backstage (GA) would provide direct cost visibility and attribution across capabilities and teams