AI Cost Engineering
cut your LLM bill 60-95%.

Cache reads cost 0.1x base input price across all current Anthropic models — a 90% discount. The 5-minute TTL write costs 1.25x and pays off after one read. The 1-hour TTL costs 2x and pays off after two reads. Combined with the Batch API 50% discount, total input savings can reach 95%. For Claude Sonnet 4.6 at $3 per million input tokens, cached input drops to 30 cents per million in Standard mode and 15 cents in batch mode.

Gemini 2.5 Flash-Lite at $0.10 per million input tokens and $0.40 per million output tokens is the cheapest production-grade model. DeepSeek V4 at $0.30 / $0.50 beats every Western flagship on price with frontier-class quality. For Anthropic users, Claude Haiku 4.5 at $1 / $5 is the cheap tier and supports the same caching and batching as Opus. For OpenAI, gpt-5.4-nano at $0.20 / $1.25 is the price floor.

Yes. The Batch API discount of 50% stacks with prompt caching multipliers. A cached read on Sonnet 4.6 in batch mode costs $0.15 per million tokens — 95% off the standard $3 input rate. Anthropic documents 30 to 98% cache hit rates inside batches when content is structured for sharing. Add identical cache_control on every request in the batch and use 1-hour TTL because batches process serially over up to 24 hours.

The Sovereign Orchestrator Pro V5.0 is the DDS production architecture that routes per-agent across Ollama and Google Gemini. Each agent (Veritas, Scribe, Atlas, Swarm Orchestrator, Tweet Generator) gets an Ollama model and a Gemini counterpart with an independent enable toggle. The Hybrid Defaults mode runs local-first with cloud counterpart on each call. Cloud-Only Test Mode forces all agents to Gemini for benchmarking. The Gemini Cloud Bypass option in the Ollama column routes through Ollama's API surface to Gemini models, eliminating client rewrites.

Break-even depends on hardware amortization and query volume. On a used RTX 3090 ($700) plus electricity (~$30/month at full load), the break-even vs gpt-4o is roughly 1,056 queries per day for a 70B-class model. Versus Claude Haiku 4.5 the break-even is around 2,500 per day. If hardware is already owned (gaming PC), break-even drops to roughly 480 queries per day vs gpt-4o. For coding autocomplete workloads, local wins by 60 to 80% of cloud spend.

Semantic caching converts queries to embeddings, searches a vector store for similar prior queries within a cosine similarity threshold (typically 0.95), and returns cached responses on match. Production deployments report 70-90% cache hit rates and 60-90% cost reduction on chatbot or support workloads. The published GPT Semantic Cache paper documents 68.8% reduction in API calls with 97% positive hit rates. The recommended stack is Redis Vector with BGE-M3 embeddings at 512 dimensions.

Model-tier routing classifies each request by complexity and routes to the cheapest adequate model. Production teams report 40-60% savings versus uniform Sonnet deployment and 51% versus uniform Opus. One documented case study saved $30,000/month with a one-week LiteLLM deployment. The pattern: 70% of requests to Haiku, 20% to Sonnet, 10% to Opus. Quality often improves because previously-undertier work gets correctly escalated to Opus.

Gemini 3.1 Pro doubles input price to $4 and output to $18 per million tokens above 200K context. OpenAI gpt-5.4 charges 2x input and 1.5x output for prompts above 272K. Anthropic uniquely keeps long context flat — Opus 4.7, Opus 4.6, and Sonnet 4.6 charge the same per-token rate at 1M context as at 1K. For long-document workloads, Anthropic is the only provider with predictable long-context economics.

For solo founders, Helicone offers proxy-based logging in 30 seconds (one URL change) with a 10K request per month free tier. For deeper application tracing, Langfuse is the most complete open-source platform, MIT licensed and self-hostable. The recommended pattern is to combine both: Helicone as the gateway for cost tracking and provider routing, Langfuse for application logic tracing and evals. Module 8 walks through the full stack.

Free. The DDS Vibe Academy is funded independently by the Design Delight Studio Shopify revenue. No paywall, no email gate, no subscription. The 8-module curriculum, 50 paste-ready prompts, decision matrices, and full code examples are all free. Gatekeeping enterprise-grade AI education behind four-figure bootcamp pricing is how we got here.

Most paid courses ($297-$1,997) cover one provider in isolation, omit semantic caching, and lack a sovereign-stack chapter. This masterclass covers all three major providers (Anthropic, OpenAI, Gemini) plus DeepSeek and open-source paths, includes the production Sovereign Orchestrator Pro pattern from a system automating $11.1M+ of annual labor, and provides 50 paste-ready prompts. Free, with no upsell.

Cost engineering is one of the five pillars of the DDS Vibe Coding methodology. The architect defines cost constraints (budget, latency tolerance, quality threshold) and AI handles implementation. Five practices: measure first (observability before optimization), cache aggressively (every reusable prefix), batch when latency permits (50% off), route by complexity (cheap-first cascade), keep sovereign fallback (Ollama for fail-open scenarios). This is the architecture behind the DDS AGI Suite.