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Model A/B Test Orchestrator: Multi-LLM Performance Comparison Platform

Gemini 3.0 just dropped via A/B test, GPT-5 rumors swirl, and Claude keeps shipping new models - but how do you safely test them in production? This platform orchestrates multi-model A/B tests across your entire LLM stack, automatically measuring quality, cost, and latency to identify the optimal model for each use case.

App Concept

  • Drop-in proxy layer that intercepts LLM API calls and routes to different models for testing
  • Define test cohorts: route 10% traffic to Gemini 3.0, 10% to Claude Opus 4, 80% to current model
  • Automatic evaluation using multiple signals: user feedback, task completion rates, human review samples
  • Real-time dashboard showing cost per request, P95 latency, and quality scores across models
  • Smart traffic ramping that automatically increases allocation to better-performing models
  • Budget controls that pause expensive model tests if costs exceed thresholds
  • Historical archive of every model test with full reproducibility

Core Mechanism

  • Developers wrap LLM API calls with orchestrator SDK or use transparent proxy
  • Configure test: models to compare, traffic split, success metrics, budget limits
  • System routes production traffic across models while maintaining consistent user experience
  • Evaluation pipeline combines automated metrics (latency, cost, output length) with quality signals
  • LLM-as-judge evaluates output quality across models for sample of requests
  • Statistical engine detects significant differences and recommends winners
  • One-click rollout migrates all traffic to winning model with automatic rollback if quality drops
  • Continuous testing detects model degradation over time (model drift monitoring)

Monetization Strategy

  • Free tier: Up to 1,000 LLM calls/month with basic A/B testing (2 models)
  • Starter tier ($149/month): 50K calls/month, up to 5 models, automated evaluation
  • Growth tier ($499/month): 500K calls/month, unlimited models, LLM-as-judge evaluation
  • Enterprise tier ($2,500+/month): Unlimited calls, custom evaluation criteria, dedicated infrastructure
  • Usage-based overage: $3 per 1,000 additional calls above plan limits

Viral Growth Angle

  • Public model leaderboard showing which models perform best for different tasks
  • Case studies: "We saved $50K/month by switching from GPT-4 to Claude for this use case"
  • Benchmark dataset sharing creates community-driven model evaluation standard
  • Integration with LangChain, LlamaIndex drives ecosystem adoption
  • Developer blog sharing A/B test results drives SEO traffic
  • Twitter threads comparing newly released models generate viral discussions
  • Open-source evaluation framework builds trust and brand recognition

Existing projects

  • PromptLayer - LLM observability, limited A/B testing
  • Helicone - LLM monitoring and caching, no orchestration
  • LangSmith - LLM debugging and testing, not production-focused
  • Braintrust - AI product evaluation platform, complex setup
  • Weights & Biases - ML experiment tracking, not LLM-specific
  • No existing solution provides turnkey production A/B testing across multiple LLM providers

Evaluation Criteria

  • Emotional Trigger: Be prescient (know which model is best before committing), limit risk (avoid expensive model mistakes)
  • Idea Quality: Rank: 9/10 - Critical need as model landscape fragments, clear ROI from cost optimization, strong network effects
  • Need Category: ROI & Recognition Needs - Measurable cost savings, demonstrating technical sophistication, data-driven decision making
  • Market Size: $800M+ (every company using LLMs needs this, 50K+ AI-powered apps × $5K-50K annual spend)
  • Build Complexity: High - Requires low-latency proxy infrastructure, statistical testing engine, LLM evaluation pipeline, multi-provider API management
  • Time to MVP: 2-3 months with AI agents (basic proxy + simple metrics), 4-6 months without
  • Key Differentiator: Only platform combining production traffic routing, automated quality evaluation, and cost optimization specifically for LLM A/B testing - positioned as "Optimizely for AI models" with technical differentiation through LLM-as-judge evaluation