Users have no visibility into how much AI capacity third-party tools consume on their behalf — one commenter noted OpenClaw was 'constantly consuming tokens, every single hour' without their awareness.
A lightweight agent/proxy that sits between AI tools and provider APIs, tracking token usage per tool, per task, and per session with alerts, budgets, and cost projections.
Freemium — free for individual use with one provider, paid tiers ($10-30/mo) for teams, multi-provider support, and budget enforcement
The pain is real but intermittent. Developers feel it when they get a surprise bill or notice background token consumption (as in the HN thread). However, many developers currently check their provider dashboard monthly and shrug. The pain intensifies for teams with shared API keys and for heavy agent users (Claude Code, Cursor) where consumption is opaque. Not yet a hair-on-fire problem for most — more of a 'I should really look into that' problem. Score would be 9 if AI spend were 10x current levels per developer.
TAM is constrained. Target users are developers actively using AI coding tools with their own API keys — a subset of a subset. Estimated: ~2-5M developers globally using AI coding tools by 2026, but only ~500K-1M bring their own API keys (vs. using bundled subscriptions like Copilot Pro). At $15/mo average, that's $90M-180M TAM. Decent for a bootstrapped business, but not venture-scale without expanding scope to enterprise AI spend management.
This is the weakest link. Developers are notoriously resistant to paying for monitoring/observability tools for personal use. The people spending $100+/mo on AI APIs might pay $10-15/mo for visibility, but many will expect this to be free or use the provider's own dashboard. Enterprise/team use case is stronger — managers want cost attribution — but selling to teams requires a harder GTM motion. The free tier must be genuinely useful to build habit before conversion.
The core proxy/logging layer is straightforward — a solo dev can build this in 4-8 weeks. However, the hard part is the zero-config experience. Getting coding assistants to route through your proxy requires: (a) tools supporting custom base URLs (Claude Code does via env vars, Cursor partially, Copilot does not), (b) SSL/TLS handling for HTTPS interception, (c) staying compatible as tools update their API calling patterns. A DNS-level or system proxy approach is fragile. The 'virtual API key' approach (you give users a proxy key that forwards to real providers) is more robust but means you're handling API traffic, adding latency and liability.
Clear gap. No existing tool is purpose-built to answer 'how much is each of my AI coding tools costing me?' LiteLLM is the closest but requires significant setup and is not marketed for this use case. Helicone, Portkey, and Langfuse all target app developers instrumenting their own code, not end-users monitoring third-party tools. The positioning as 'per-tool visibility for AI coding assistants' is genuinely unoccupied.
Natural subscription — monitoring is inherently ongoing. As long as users are spending on AI APIs, they need visibility. Usage-based pricing (free up to X tracked spend, paid for higher volume) aligns well with customer value. Low churn potential once integrated into workflow. Budget alerts and reports create daily/weekly engagement loops.
- +Clear, unoccupied market gap — no tool is built specifically for per-tool AI coding assistant cost attribution
- +Strong signal from HN thread (875 upvotes) that developers care about opaque token consumption
- +Natural recurring/subscription model with usage-based pricing that scales with customer value
- +Problem will worsen as agentic AI tools become more autonomous and consume more tokens in the background
- +Can start simple (proxy + dashboard) and expand into budget enforcement, team management, and efficiency analytics
- !Willingness to pay is uncertain — developers may expect this to be free or use provider dashboards that improve over time
- !Distribution is hard: intercepting third-party tool traffic requires per-tool configuration, and not all tools support custom base URLs (Copilot notably does not)
- !AI providers (Anthropic, OpenAI) could add per-application usage breakdowns to their own dashboards, killing the core value prop overnight
- !Handling API traffic through a proxy adds latency and creates liability — one outage and developers lose access to all their AI tools
- !Market may be too small at $10-30/mo to sustain a venture-backed business; best suited as a bootstrapped/indie product
Open-source proxy/SDK providing a unified OpenAI-compatible interface to 100+ LLM providers. Can run as a local proxy server with spend tracking, virtual key management, rate limiting, and budget controls.
Open-source AI observability platform. Acts as a proxy or async logger between your app and LLM providers. Logs requests, tracks token usage, latency, and cost with dashboards.
AI gateway + observability platform. Routes LLM calls through its gateway providing logging, fallbacks, load balancing, cost tracking, guardrails, and virtual key management across 200+ LLMs.
AI API router/aggregator providing a single API endpoint to access models from OpenAI, Anthropic, Google, Meta, Mistral, and others. Built-in usage history and cost dashboard.
Open-source LLM observability platform offering tracing, cost tracking, prompt management, and evaluation tools. Strong alternative to Helicone with good community traction.
A lightweight local CLI tool + web dashboard. User installs via npm/brew, runs a local proxy on localhost:4000, and sets their AI tools' base URLs to point at it. The proxy forwards requests to real providers, logs token usage per tool (identified by User-Agent or separate virtual keys), and pushes data to a simple hosted dashboard showing per-tool spend, daily trends, and budget alerts. Start with Claude Code + Cursor support only (both support custom base URLs). Ship within 6 weeks.
Free: 1 provider, 1 tool, basic dashboard, 30-day history. Paid ($12/mo): unlimited providers/tools, budget alerts, cost projections, CSV export. Team ($25/user/mo): shared dashboard, per-developer attribution, Slack alerts, admin budget controls. Long-term: enterprise AI spend management platform with SSO, approval workflows, and chargeback reports.
8-12 weeks. ~6 weeks to build MVP (local proxy + dashboard), ~2 weeks for landing page + launch (Product Hunt, HN 'Show HN', r/ChatGPTPro, dev Twitter). First paying users likely within 2-4 weeks of launch given the demonstrated HN interest. Revenue will be modest initially ($500-2K MRR in first 3 months) — the real test is whether paid conversion exceeds 3-5% of free users.
- “constantly consuming tokens, every single hour during the day”
- “outsized strain on our systems”
- “no visibility into what tools consume on your behalf”