Optimizing OpenAI & Anthropic API Costs: How We Reduced LLM Burn by 42%

Production caching patterns, prompt compression, model tier routing, and token telemetry for AI-first SaaS products.

VentureBase Engineering

Full-Stack & AI Systems

#LLM Costs#OpenAI#Prompt Engineering#Semantic Cache

For AI legal platforms like LexOrbit, LLM inference is not a marginal cost—it is the core COGS (Cost of Goods Sold). Without strict token hygiene and architectural optimization, API bills can decimate your gross margins.

1. Implementing Redis-Based Semantic Caching

Over 35% of legal research and document queries in legal tech involve identical or semantically equivalent questions. By embedding user prompts and querying a vector index (e.g. pgvector or Redis), identical queries return cached results in sub-10ms with zero token cost.

Founder Strategy Tip:Set TTL (Time-To-Live) cache invalidation based on document updates so users always receive fresh context.

2. Intelligent Model Routing (GPT-4o vs. Mini vs. Claude 3.5 Haiku)

Never use flagship frontier models for classification, intent detection, or JSON extraction. Route user inputs through smaller, ultra-fast models (like GPT-4o-mini or Claude 3.5 Haiku) for preliminary parsing, reserving deep reasoning models only for synthesis.

3. Prompt Compression and Preamble Trimming

Bloated system prompts sent on every multi-turn conversation turn burn millions of tokens per week. Use prompt compression, structured JSON schematization, and prompt caching (OpenAI automatic prompt caching and Anthropic Prompt Caching) to achieve up to 90% discount on input tokens.

Executive Key Takeaways

•Implement semantic caching for repetitive user search queries.
•Structure multi-tiered LLM routing: mini models for intake, frontier models for analysis.
•Monitor your monthly AI token spend inside VentureBase to keep gross margins above 75%.

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