AI Cost & Efficiency
Fewer tokens, cheaper APIs, local alternatives with real numbers
4 min readWhat is GPT-5.5 Codex reasoning-token clustering may be leading to degraded performance? A Practical Overview
Recent reports on GitHub suggest GPT-5.5 Codex's reasoning-token clustering causes degraded code quality. This post explains the mechanism, shows concrete examples, and offers practical mitigations.
4 min readTokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering
Tokenomics quantifies token usage per step in agentic software engineering. This post breaks down the numbers, tradeoffs, and practical tips for cost optimization.
4 min readWhy Does MCP Use So Many Tokens? (And How to Fix It)
MCP tool definitions can eat half your context window before you prompt. Here is why — and six fixes that actually work in Claude Code and Cursor.
14 min readLLM Token Optimization in 2026: Model Routing, Caching, and Tool Budgets
100-turn coding session: $31 on Opus-only vs $2.65 routed. Six strategies with dollar math: caching, Sonnet default, MCP audit, structured output.
8 min readPrompt Caching: How to Cut Your LLM API Bills by 50%
Understanding how provider-side prompt caching caches system instructions and history files to drop latency and input token pricing.
10 min readBuilding a Custom LLM Gateway for Rate-Limiting, Fallbacks, and Auditing
How to engineer a centralized routing layer that handles API keys, logs usage, automatically retries on rate limits, and falls back to backup models.
7 min readHelicone: Track LLM Costs, Cache Responses, and Rate-Limit Users
Helicone sits between your app and LLM APIs as a one-line proxy - giving you per-user cost attribution, response caching, and rate limiting without changing your application logic.
9 min readModel Distillation: Creating Lightweight LLMs for Domain-Specific Tasks
How to use a large frontier model (like GPT-4o) to generate training datasets that fine-tune a much cheaper model (like Llama 3B) to equivalent accuracy.
7 min readOptimizing Context Window Usage: Context Pruning and Summarization Techniques
Avoid pay-per-token overheads. Learn algorithms for summarizing historical messages and pruning irrelevant tokens from input payloads.
9 min readLocal LLM vs. API: When Running Models Yourself Actually Saves Money
A GPU server costs $300-800/month. At low query volume, API access is cheaper. At high volume, local wins. Here is the break-even analysis with real numbers.
9 min readModel Routing: How to Cut LLM Costs 50-70% Without Sacrificing Quality
Model routing automatically sends simple queries to cheap models and complex ones to expensive models. With GPT-4o-mini at $0.15/1M tokens vs GPT-4o at $2.50/1M, the savings are substantial.
8 min readHow to Reduce LLM Output Tokens by 40-60% Without Losing Quality
Output tokens cost 3-6x more than input tokens. Specific prompt instructions and format choices can cut output length by 40-60% for the same information, with a direct impact on your bill.
9 min readLLM Rate Limiting and Cost Controls: How to Prevent Runaway API Bills
Runaway LLM bills happen without rate limits and budget alerts. Here is how to implement per-user limits, global budget controls, and circuit breakers that protect your margins.
16 min readCutting LLM API Costs by 50%+: Every Technique That Works in 2026
Six proven techniques to reduce your LLM API spend. Real pricing numbers, a startup case study reducing from $800 to $320/month, and specific implementation guidance.
9 min readThe Cost-Per-Task Framework: How to Actually Measure AI ROI
Tracking API spend alone tells you nothing about ROI. The right metric is cost per meaningful task - and comparing it to the non-AI cost of doing the same work.
8 min readLLM API Pricing Comparison 2026: Every Major Model, Real Numbers
Complete LLM API pricing table with per-request cost calculations. Which model is cheapest for coding, summarization, and classification? Real numbers, no estimates.
8 min readSemantic Caching: How to Serve LLM Responses Without Calling the API
Semantic caching stores LLM responses and returns them when a new query is semantically similar to a cached one. In customer support applications, hit rates of 15-40% are realistic.
5 min readGroq vs. Together AI vs. Fireworks AI: Fast LLM Inference Compared
Three fast, cheap inference platforms for open source LLMs. Groq is the fastest, Together AI has the broadest model selection, Fireworks specializes in production-grade function calling.
5 min readOpenAI Batch API: Get 50% Off for Non-Real-Time Requests
OpenAI's Batch API cuts costs by 50% for any request that can wait up to 24 hours. If you have data labeling, nightly analysis, or content moderation workloads, you should be using it.
5 min readAnthropic Message Batches API: 50% Off Claude for Async Workloads
Anthropic's Message Batches API gives you 50% off Claude pricing for requests that can wait up to 24 hours. Here is how to use it and which Claude workloads benefit most.
8 min readHow Much to Budget for LLM API Costs at Each Startup Stage
LLM costs scale from $0-50/month at pre-product to $500-5,000/month at growth stage. Here is what to expect, where to optimize, and the rule of thumb that keeps AI spend sustainable.
8 min readQuantization Explained: How to Run LLMs 4x Cheaper With Minimal Quality Loss
Quantization reduces model weight precision from FP32 to INT4, cutting memory and compute by 4-8x. Q4_K_M is the sweet spot for most use cases - near full quality at a fraction of the size.
7 min readPrompt Caching With Anthropic and OpenAI: How to Cut Costs by Up to 90%
How prompt caching works on Anthropic and OpenAI, when it saves money, and how to implement it. Real cost reduction numbers with code examples.
9 min readFlash Attention Explained: The Engineering Trick Behind Long-Context LLMs
Flash Attention rewrites transformer attention to be IO-aware, reducing memory from O(n²) to O(n). It enables 128k context windows and cuts training costs by 2-4x. Here is how it works.