AgentLedger
AI agents are racking up surprise bills across your team. AgentLedger tracks every model call, assigns costs to projects, and kills budget overruns before they hit your credit card.
The Idea
In 2026, the average engineering team runs 5-15 AI agents. Cursor for coding, Claude for document analysis, GPT-4 for customer pipelines, Gemini for internal search. Every one of them is burning API credits. Nobody knows how much. Finance gets the invoice at month-end and it's $8,000 instead of $800 — and nobody can tell them which team, which project, or which agent caused the spike. AgentLedger is a lightweight spend management layer that sits between your team and every AI provider: track every model call, assign costs to projects and teams, set hard budget caps, get Slack alerts before overruns, and pull a monthly cost breakdown that finance can actually use.
Why Now
AI API spend has become a serious line item almost overnight. OpenAI reported 2 million developer accounts in 2024 — that number is orders of magnitude higher now. Cursor alone has 4 million users burning Claude and GPT tokens constantly. The "agentic AI" wave means these aren't just occasional API calls anymore — they're persistent loops running 24/7 on company infrastructure. AWS has Cost Explorer. Google Cloud has billing dashboards. Vercel has spend alerts. But AI API costs? Scattered across 6 different provider dashboards, denominated in tokens instead of dollars, with no way to attribute spend to a team or product. The problem is acute, it's growing, and nobody's solved it for the SMB/startup tier.
How to Build
The architecture is a proxy layer: companies route their AI API calls through your endpoint (a simple base URL swap in their config). You log every request with metadata — model, token count, estimated cost, user ID, project tag — and persist it. The dashboard shows spend by team, by model, by project, over time. Budget caps fire webhooks or Slack notifications when thresholds are hit. Hard limits can block calls entirely. V1 supports OpenAI, Anthropic, and Google. The proxy approach means zero code changes for users beyond a base URL and API key swap — critical for adoption. Stack: Next.js, Vercel, Stripe, Postgres.
Revenue Model
Free: up to $500/month in tracked AI spend (plenty for individuals and small teams). Pro: $29/month for unlimited tracking, budget alerts, team attribution, and monthly reports. Team: $79/month for multi-project dashboards, hard budget caps, SSO, and finance exports. The pricing is easy to justify — if you're spending $5k/month on AI APIs, $79/month to see where it's going is a rounding error. Target 300 Pro users = $8,700 MRR.
Effort
1 week to a solid v1. Day 1-2: build the proxy endpoint and request logging pipeline. Day 3-4: dashboard with cost attribution and charts. Day 5: budget alerts and Slack integration. Day 6-7: Stripe billing and onboarding flow. The proxy architecture is the interesting engineering challenge — it needs to handle streaming responses (most LLM calls are streamed) without adding meaningful latency. Doable but requires care.
Risk
Two real risks. First, the proxy trust problem: companies routing production AI traffic through a third-party proxy is a security concern, especially for anything handling PII. You need airtight data handling, a clear "we don't log content, only metadata" architecture, and ideally a self-hosted option for larger customers. Second, provider-native solutions: OpenAI and Anthropic will eventually build this themselves. But enterprise features move slowly at providers — the SMB window is real and long enough to build a durable business.
The AI API cost problem is exploding in slow motion and nobody's solved it for teams under 200 people. The proxy model is elegant — zero friction to adopt, immediate value on day one. The trust/security question needs to be addressed head-on in your positioning: "we log metadata, never content." If you can nail that message, the sales cycle is short — anyone who got a surprise $5k API invoice last month is an instant buyer.