Developers and AI power users are burning through expensive cloud API tokens unnecessarily when many tasks could be handled by local models.
A smart router that sits between your apps and AI providers, automatically routing simple tasks to local models and only sending complex ones to cloud APIs. Shows real-time cost savings dashboard.
Freemium - free for personal use with basic routing, $15/mo for teams with advanced routing rules, analytics, and budget alerts
Real pain, but unevenly distributed. Power users and teams running agents/coding assistants feel this acutely — $100-500+/mo API bills are common. However, casual users on $20/mo subscriptions don't feel it. The Claude token-burning bug signal is real but episodic. Pain intensifies as agentic workflows scale up token usage 10-100x.
TAM is meaningful but narrower than it appears. Target is developers paying for API access directly — maybe 2-5M globally today. At $15/mo, that's a $360-900M theoretical TAM. But willingness to adopt another tool in the chain is a filter. Realistic serviceable market is probably $50-150M. Growing fast though.
This is the weak spot. Developers are notoriously resistant to paying for dev tools, especially cost-optimization tools (ironic). The value prop is 'save money by spending money.' At $15/mo, you need to demonstrably save >$15/mo — which means the tool only works for users already spending $50+/mo on APIs. Free/open-source alternatives (LiteLLM) set expectations. Teams are more likely to pay than individuals.
Core proxy/router is straightforward — intercept API calls, classify complexity, route accordingly. The HARD part is the 'smart' routing: reliably classifying which tasks can go local without quality degradation. This is a machine learning problem in itself. MVP with rule-based routing (regex, token count, model hints) is buildable in 4-8 weeks. Truly intelligent routing is a 6-12 month R&D effort.
Clear gap exists: nobody combines cost monitoring + smart routing + local model support in a desktop-first tool. Portkey/Helicone do monitoring, Martian does routing, Ollama does local — nobody does all three. But this gap is visible to well-funded competitors who could add these features. LiteLLM is closest open-source threat and could absorb this value prop.
Natural subscription fit for teams (budget alerts, analytics, multi-user). Individual developers are harder — once routing rules are set, ongoing value is mostly the dashboard. Risk of 'set and forget' churn. Usage-based pricing tied to tokens routed might be more natural than flat subscription.
- +Clear gap in market — no tool combines monitoring + intelligent routing + local models in one package
- +Timing is excellent: local models are now good enough for many tasks, and API costs are a growing pain point as agentic workflows explode
- +Desktop-first approach appeals to privacy-conscious developers who don't want another cloud service in their stack
- +Strong viral loop potential — developers share cost-saving tools and the 'savings dashboard' is inherently shareable/screenshot-worthy
- !LLM providers themselves (OpenAI, Anthropic) may add built-in cost optimization, tiered routing, or aggressive price cuts that eliminate the pain
- !Open-source risk is high — LiteLLM or a new OSS project could add smart routing and undercut the paid offering entirely
- !The 'smart routing' classifier is the core IP, but getting it wrong (sending a complex task to a weak local model) destroys user trust fast
- !Market could bifurcate: enterprises use Portkey/custom solutions, individuals use free tools — leaving the $15/mo tier in no-man's land
Unified API gateway for 200+ LLMs with cost tracking, caching, fallbacks, load balancing, and observability. Production-grade proxy layer.
Open-source proxy that provides a unified OpenAI-compatible API across 100+ LLM providers. Includes spend tracking, budgets, and rate limiting.
AI-powered model router that automatically selects the cheapest/fastest model capable of handling each request based on prompt analysis.
Unified API providing access to hundreds of models from multiple providers with a single API key, price comparison, and fallback routing.
LLM observability platform — tracks costs, latency, usage patterns across providers. One-line integration.
Desktop app (Electron or Tauri) that runs as a local proxy. Supports OpenAI/Anthropic/Ollama endpoints. Rule-based routing (user-defined: 'if task is translation/summarization/formatting, use local model X'). Real-time dashboard showing: total spend, tokens routed locally vs cloud, estimated savings. Budget alerts via desktop notifications. Skip ML-based classification for MVP — let users define their own routing rules and learn from their patterns.
Free: single user, 3 routing rules, basic dashboard, 1 local model -> $15/mo Pro: unlimited rules, full analytics, multiple local models, prompt caching, export reports -> $49/mo Team: shared budgets, team analytics, centralized routing policies, SSO -> Usage-based enterprise tier for high-volume API traffic
8-12 weeks to MVP with first free users. 4-6 months to first paying customers. The challenge is proving measurable savings before asking for money — expect a long free tier period to build trust and collect routing pattern data.
- “With the Claude bug burning through tokens at record speed”
- “I gave alternative models a try and they're mostly interchangeable”
- “I don't know how easy switching and low brand loyalty and fast markets will play out”