AI FinOps Runtime Layer for LLM Cost Attribution, Credential Governance, and Model Quality Verification
Teams running multiple LLM providers face three compounding problems: mystery AI bills with no per-project attribution, API key management nightmares across growing teams, and providers silently downgrading model quality during peak hours. AiKey positions itself as a runtime credential layer that sits between apps and AI providers, offering virtual key orchestration with spend limits, real-time model fingerprint verification to detect 'nerfing', and zero-config credential injection.
Problem Statement
AI-intensive teams pay aggregate bills with no attribution to specific projects, agents, or team members. A 40% spend spike may go undiagnosed for weeks because provider dashboards show totals but not per-workflow breakdowns. API key management across growing teams means sharing master keys, risking leaks through .env files, and requiring production-breaking key rotation when contractors leave. Worst, some providers silently route expensive model requests to cheaper distilled versions during peak hours, causing quality degradation that developers waste hours debugging as prompt issues.
The Idea
A runtime governance layer between applications and AI providers that attributes token spend per project, manages credentials without .env files, and detects when providers silently downgrade model quality.
Why Now
AI spend has become a top-3 infrastructure cost for growing teams in 2026. Multiple LLM providers are now used simultaneously (GPT-5, Claude 4, open-source models). Third-party model routing services have been caught silently downgrading requests to cheaper models during peak hours. API key rotation and team offboarding across 15+ team members and multiple providers creates security vulnerabilities. The cloud FinOps pattern (CloudHealth, Cloudability) is well-understood, but no equivalent exists for AI spend attribution.
Target User
Dev leads and engineering managers at AI-first startups managing multi-provider LLM infrastructure with teams of 5-50
Target Market
AI infrastructure governance and FinOps for teams using multiple LLM providers
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