The Problem (Pain Level: 7/10)

“I didn’t know how many tokens my prompt was, and my API call failed” - A daily frustration for LLM developers.

Current pain points:

  • Token blindness: Can’t see real-time token count while writing prompts
  • Unpredictable costs: Hard to estimate costs until API call is made
  • Model confusion: GPT-4, Claude, Gemini all have different tokenizers
  • No version control: Difficult to track prompt change history
  • Team collaboration friction: Hard to share prompts and get feedback

Target Market

Primary Target: LLM app developers, AI engineers, prompt engineers

Market Size:

  • Prompt engineering market: CAGR 32.8% through 2030
  • LLM app developers surging, adopted from startups to enterprises
  • Hidden costs account for 20-40% of LLM operational expenses

Pain Frequency: Recurring problem for developers writing prompts daily

What is CTxStudio?

An integrated development environment with real-time token counting and visual prompt composition.

Core Concept:

┌─────────────────────────────────────────────────────┐
│  CTxStudio                                    [≡]   │
├─────────────────────────────────────────────────────┤
│  ┌───────────────────────┬───────────────────────┐  │
│  │  System Prompt        │  Tokens: 847 / 8,192  │  │
│  │  ─────────────────    │  Cost: ~$0.025        │  │
│  │  You are a helpful    │  Model: Claude 3.5    │  │
│  │  assistant that...    │  ─────────────────    │  │
│  │                       │  [GPT-4] [Gemini]     │  │
│  └───────────────────────┴───────────────────────┘  │
│                                                     │
│  ┌─────────────────────────────────────────────┐    │
│  │  + Add Variable  │  + Add Example  │  Test  │    │
│  └─────────────────────────────────────────────┘    │
│                                                     │
│  Version: v1.3.2  │  Last edit: 2 min ago          │
└─────────────────────────────────────────────────────┘

Differentiation:

  • Real-Time Multi-Model Tokens: Simultaneous counting for GPT-4, Claude, Gemini
  • Cost Estimator: Real-time display of estimated API costs
  • Visual Block Editing: Drag-and-drop prompt composition
  • Variable System: Dynamic prompt template management
  • Version History: Track prompt changes like Git

Competitive Analysis

CompetitorFeaturesWeakness
ChainForgeOpen-source, visualComplex setup, local install required
PromptfooCLI-based, testing focusNo GUI, developers only
LangfuseObservability focusedMonitoring tool, not an editor
PromptLayerVersion control, cost trackingWeak visual editor

Opportunity: Missing combination of “real-time feedback + visual editing + multi-model support”

Competition Intensity: HIGH - Many open-source alternatives exist

MVP Development

Timeline: 4-6 weeks

Tech Stack:

  • Frontend: React + Monaco Editor
  • Tokenizer: tiktoken (OpenAI), @anthropic-ai/tokenizer
  • Backend: Next.js API Routes
  • Storage: Supabase (PostgreSQL + Auth)
  • Deployment: Vercel

MVP Features:

  1. Prompt editor (Monaco-based)
  2. Real-time token counting (GPT-4, Claude)
  3. Cost estimator
  4. Prompt saving and version control
  5. Basic sharing links

Future Features:

  • A/B testing framework
  • Team workspaces
  • API integration (production prompt management)
  • Prompt performance analytics

Revenue Model

Model: Freemium

Pricing Structure:

  • Free: 10 prompts, basic token counting, community support
  • Pro ($19/mo): Unlimited prompts, multi-model support, version history
  • Team ($49/mo/seat): Team workspace, role-based access, API access

Revenue Projections:

  • 6 months: $2K-5K MRR (with Product Hunt launch)
  • 12 months: $10K-20K MRR (with team plan conversions)

Risk Analysis

RiskLevelMitigation
TechnicalLOWTokenizer libraries are stable
MarketHIGHMany free alternatives, differentiation required
ExecutionLOWRelatively simple MVP scope

Key Risks: ChainForge, Langfuse improvements, LLM providers strengthening native tools

Who Should Build This

  • Full-stack developers with frontend development skills
  • Those who experienced token management difficulties while building LLM apps
  • Interested in developer tools market and DevEx
  • Able to design monetizable differentiation against open-source
  • Prefer fast MVP launch and feedback loops

If you’re building this idea or have thoughts to share, drop a comment below!