Budget AI Deployment: Production LLM Infrastructure for $100/Month
NanoChat proves you can build powerful AI for $100. Most companies overspend 10-50x on AI infrastructure because they lack tools to optimize costs without sacrificing quality.
ai-devops¶
LLM Instruction Debugger: Why Your AI Can't Follow Simple Instructions
AI developers are pushing toward agentic systems while models still struggle with basic instruction-following. This creates a critical gap between ambition and capability that wastes hours of debugging time.
Model Version Time Machine: Reproduce Any LLM Behavior from History
Models improve constantly, but this breaks reproducibility. Debugging production issues requires reproducing exact model behavior from weeks ago—currently impossible with API-based LLMs.
AI Cost Optimizer: Real-Time Budget Control for LLM Operations
AI teams consistently blow through budgets during experimentation phases, with unexpected API costs from OpenAI, Anthropic, and other providers. There's no single dashboard to track spending, predict overruns, or automatically enforce limits across providers.
AI Testing Copilot: Automated Test Generation for AI Systems
AI applications are notoriously hard to test due to non-deterministic outputs. Teams lack systematic approaches to test coverage, miss edge cases, and struggle to catch regressions when prompts or models change.
Inference Cache Manager: Smart Caching Layer for LLM APIs
Teams repeatedly call expensive LLM APIs for nearly identical queries, wasting 40-60% of their budget on redundant inference. Traditional caching fails because prompts are rarely character-for-character identical, even when semantically equivalent.
Model Performance Arena: Live Benchmarking Across LLM Providers
Developers waste hours manually testing prompts across different models, with no systematic way to compare quality, cost, and speed. Model capabilities evolve weekly, making yesterday's benchmarks obsolete for production decisions.
Prompt Version Control: Git for AI Prompts with A/B Testing
AI teams struggle to track prompt changes across experiments, losing track of what worked and why. There's no standard way to collaborate on prompts, test changes systematically, or roll back when new prompts underperform.