ModelSwitch — AI Cost Router That Stops Your Automation Bill From Tripling
Your team just got told to cut AI spending and now everyone is manually babysitting API calls like it's 2019. ModelSwitch auto-routes every n8n and Make task to the cheapest model that can handle it, with real-time cost dashboards that prove the savings.
Difficulty
intermediate
Category
Business Automation
Market Demand
Very High
Revenue Score
8/10
Platform
Web App
Vibe Code Friendly
No
Hackathon Score
🏆 7/10
Validated by Real Pain
— sourced from real community discussions
Teams using AI-powered automations are seeing costs triple with no visibility into which workflows are responsible, forcing manual throttling or abandoning automations entirely.
What is it?
Teams running automations at scale are discovering that GPT-4o on every node is a budget-killer — Reddit is full of posts about AI costs tripling overnight with zero warning. ModelSwitch sits between your automation platform and your LLM providers, classifying each task by complexity and routing simple ones to GPT-4o-mini or Haiku while reserving Claude Sonnet only for tasks that actually need it. A live dashboard shows cost-per-workflow, projected monthly spend, and ROI versus naive routing. The product ships as a hosted proxy with drop-in n8n and Make HTTP node configs — no infra changes required. Buildable today because OpenAI, Anthropic, and Groq all have stable REST APIs, and task complexity classification is a solved problem with a single Claude Haiku call costing fractions of a cent.
Why now?
OpenAI pricing tiers and Claude model tiers (Haiku vs Sonnet vs Opus) finally make intelligent routing economically meaningful — the cost spread between cheapest and most expensive capable models is now 20x, making routing ROI undeniable in May 2026.
- ▸Complexity classifier routes each task to cheapest capable model (Implementation note: single Haiku call scores task 1-5 complexity)
- ▸Real-time cost dashboard with per-workflow and per-model spend breakdown
- ▸Drop-in n8n and Make HTTP node config snippets generated automatically
- ▸Monthly budget cap with Slack or email alert before you hit the ceiling
Target Audience
n8n and Make power users, automation agency owners, and SMB ops teams running 50+ AI-powered workflows monthly — roughly 80,000 active n8n cloud users plus Make's 500k+ user base.
Example Use Case
Maria runs a 40-workflow Make agency. ModelSwitch cuts her client AI bills by 38% in week one, she screenshots the dashboard and upsells every client on a managed automation retainer at $300/month more.
User Stories
- ▸As an automation agency owner, I want to route client workflow calls to the cheapest capable model automatically, so that I can cut AI costs by 30% without touching individual workflow configs.
- ▸As an SMB ops manager, I want a real-time dashboard showing AI spend per workflow, so that I can identify which automations are burning budget unnecessarily.
- ▸As a solo n8n power user, I want a budget cap alert before I hit my monthly limit, so that I never get a surprise invoice at month end.
Done When
- ✓Proxy routing: done when user pastes HTTP node config, runs workflow, and sees call logged in dashboard within 10 seconds.
- ✓Cost dashboard: done when user sees per-workflow spend breakdown with model used and tokens consumed for each call.
- ✓Budget alert: done when user sets a monthly cap and receives an email before 90% threshold is reached.
- ✓Billing gate: done when free tier user at 500 calls sees upgrade prompt and Stripe checkout completes granting continued access.
Is it worth building?
$49/month x 30 users = $1,470 MRR at month 2. $99/month x 150 users = $14,850 MRR at month 6. Conservative: 5% of 300 beta signups convert.
Unit Economics
CAC: $12 via community DMs and Reddit posts. LTV: $588 (12 months at $49/month). Payback: 1 month. Gross margin: 85%.
Business Model
SaaS subscription
Monetization Path
Free tier: 500 routed calls/month. Paid $49/month: 10k calls. Agency $199/month: unlimited + white-label config.
Revenue Timeline
First dollar: week 2 via beta upgrade. $1k MRR: month 2. $5k MRR: month 5. $10k MRR: month 9.
Estimated Monthly Cost
Claude Haiku for routing: $15, Vercel: $20, Supabase: $25, Upstash Redis: $10, Resend: $5. Total: ~$75/month at launch.
Profit Potential
Full-time viable at $8k–$20k MRR with zero marginal cost per new customer.
Scalability
High — add Gemini Flash, Mistral, local Ollama routing in V2. Team plans and agency white-label in V3.
Success Metrics
Week 1: 50 beta signups from Reddit. Week 3: 10 paying customers. Month 2: 85% retention, average $40 saved per user per month documented.
Launch & Validation Plan
Post in r/n8n and r/automation asking who has had AI costs spike — collect 20 DMs, offer free beta, measure if they complete setup without help.
Customer Acquisition Strategy
First customer: DM 15 active n8n community members on the official Discord who posted about cost issues, offer 3 months free for weekly feedback. Ongoing: SEO targeting 'reduce OpenAI API costs n8n', ProductHunt launch, Make and n8n community forums.
What's the competition?
Competition Level
Low
Similar Products
PortkeyAI does LLM gateway but targets developers not no-code users. Helicone does observability not routing. Neither integrates as a drop-in n8n node.
Competitive Advantage
No existing tool does cross-provider intelligent routing inside n8n or Make — LangChain Router is code-only, not a hosted drop-in.
Regulatory Risks
GDPR: prompt content passes through proxy — must document data retention policy and offer EU data residency or zero-logging mode for enterprise.
What's the roadmap?
Feature Roadmap
V1 (launch): proxy routing, cost dashboard, n8n config snippets, budget alerts. V2 (month 2-3): Make integration, Gemini Flash provider, team workspaces. V3 (month 4+): white-label for agencies, per-client cost reports, Slack cost digest.
Milestone Plan
Phase 1 (Week 1-2): proxy API + classifier + Supabase logging ships, tested with real n8n workflow. Phase 2 (Week 3-4): dashboard UI + Stripe billing + budget alerts live. Phase 3 (Month 2): 10 paying customers, Make integration shipped.
How do you build it?
Tech Stack
Next.js, Supabase, Claude API (Haiku for routing), OpenAI API, Groq API, Resend — build with Cursor for proxy logic, v0 for dashboard UI
Suggested Frameworks
Next.js API routes, Supabase Postgres, Upstash Redis
Time to Ship
2 weeks
Required Skills
API proxy middleware, Next.js, Supabase, basic LLM prompt classification.
Resources
OpenAI docs, Anthropic docs, Groq docs, n8n HTTP node docs, Upstash Redis quickstart.
MVP Scope
app/page.tsx (landing + dashboard shell), app/api/route/route.ts (proxy + classifier), app/api/usage/route.ts (cost aggregation), lib/classifier.ts (complexity scoring logic), lib/providers.ts (model registry), lib/db/schema.ts (Drizzle schema), components/CostChart.tsx (recharts spend visual), components/WorkflowTable.tsx (per-workflow breakdown), seed.ts (demo usage data), .env.example (API keys + Supabase URL)
Core User Journey
Sign up -> copy HTTP node config -> paste into n8n workflow -> see first cost breakdown on dashboard -> hit free tier limit -> upgrade.
Architecture Pattern
Automation HTTP node -> ModelSwitch proxy API -> Haiku classifier -> provider selector -> target LLM API -> response returned + usage logged to Postgres -> dashboard queries aggregated spend.
Data Model
User has many Workflows. Workflow has many RoutedCalls. RoutedCall stores input complexity score, model selected, tokens used, cost in cents, timestamp.
Integration Points
OpenAI API for GPT models, Anthropic API for Claude models, Groq API for fast cheap inference, Supabase for usage storage, Upstash Redis for rate limiting, Resend for budget alert emails, Stripe for billing.
V1 Scope Boundaries
V1: n8n HTTP node only, three providers (OpenAI, Anthropic, Groq), basic complexity routing. No Make integration, no custom model fine-tuning, no team accounts, no mobile app.
Success Definition
A paying automation agency owner routes all client workflows through ModelSwitch, sees documented 30%+ cost savings, and upgrades to agency plan without founder intervention.
Challenges
Convincing users to route traffic through a third-party proxy requires trust — SOC2 or at minimum a clear data-handling policy is needed before agency adoption. This is the real blocker, not the code.
Avoid These Pitfalls
Do not store prompt content by default — users will not trust a proxy that logs their data. Do not try to build a universal LLM gateway on day one — ship n8n only, Make in V2. Finding first 10 paying customers takes 3x longer than building — spend week one in communities, not coding.
Security Requirements
Supabase Auth with Google OAuth, RLS on all user tables, zero prompt content stored by default, rate limiting 200 req/min per API key via Upstash Redis middleware.
Infrastructure Plan
Vercel for Next.js hosting and API routes, Supabase for Postgres and auth, Upstash Redis for rate limiting, Sentry for error tracking, GitHub Actions for CI to Vercel preview and prod.
Performance Targets
100 DAU at launch, proxy endpoint target under 100ms added latency, dashboard load under 2s, Redis caches aggregated spend per user for 60 seconds.
Go-Live Checklist
- ☐Security audit complete.
- ☐Payment flow tested end-to-end.
- ☐Sentry error tracking live.
- ☐Vercel analytics dashboard configured.
- ☐Custom domain with SSL active.
- ☐Privacy policy and zero-logging note published.
- ☐5 beta n8n users signed off.
- ☐Rollback plan: revert to previous Vercel deployment.
- ☐Launch post drafted for r/n8n and ProductHunt.
First Run Experience
On first run: dashboard shows seeded demo workflow with 3 example calls across GPT-4o-mini, Haiku, and Groq Llama, with a fake $12.40 vs $34.20 comparison. User can immediately copy the n8n HTTP node snippet and see the routing logic without any API keys. No manual config required: demo mode works with no env vars set.
How to build it, step by step
1. Define schema: RoutedCall and UsageSummary tables in lib/db/schema.ts with Drizzle. 2. Run npx create-next-app with TypeScript and Tailwind. 3. Build /api/route proxy endpoint that accepts any LLM-format body and runs Haiku classifier. 4. Build lib/providers.ts registry mapping complexity scores 1-5 to model IDs and cost-per-token. 5. Store every call result in Supabase with model, tokens, cost, workflow_id. 6. Build /api/usage aggregation endpoint grouping spend by workflow and model. 7. Build CostChart.tsx with Recharts showing daily spend by model. 8. Build WorkflowTable.tsx listing top workflows by cost with projected monthly total. 9. Add Stripe billing with usage-based free tier gate at 500 calls. 10. Deploy to Vercel, verify full proxy round-trip and dashboard render with seeded demo data end-to-end.
Generated
May 30, 2026
Model
claude-sonnet-4-6
Disclaimer: Ideas on this site are AI-generated and may contain inaccuracies. Revenue estimates, market demand figures, and financial projections are illustrative assumptions only — not financial advice. Do your own research before making any business or investment decisions. Technology availability, pricing, and market conditions change rapidly; always verify details independently.