Developer video series
Practical engineering patterns for reducing LLM costs in production apps.
AI features are getting easier to build, but harder to operate profitably. Once you move beyond a prototype, every model choice, repeated request, oversized context window, retry, tool definition, test run, reasoning setting, and real-time call can quietly turn into margin leakage.
Most AI cost problems are not just model pricing problems. They come from engineering choices that compound as usage grows.
First batch
Watch the first batch of videos and start applying practical cost controls to prompts, model routing, duplicate request handling, context management, caching strategy, reasoning settings, and batch workflows.
Every prompt in your AI application does not need to go to your strongest frontier model. Simple classification, field extraction, sentiment checks, short summaries, and fixed-format responses are often better handled by lighter models that are cheaper and faster.
Key idea
Route by task, not by habit.
Repeated identical LLM calls are one of the easiest cost leaks to miss because they often look like normal usage. Use idempotency keys, request hashing, short-lived response caching, in-flight request deduplication, replay protection, rate limits, and stored generated results.
Key idea
The cheapest LLM call is the one you do not make.
As teams add longer system prompts, chat history, retrieved documents, tool definitions, safety policies, JSON schemas, and runtime application data, every model call gets larger, slower, and more expensive.
Key idea
Long context is not a database.
Prompt caching can reduce AI application costs, but only if your prompts are structured consistently. Stable content should come first, variable content should come last, and shared templates should keep prompt composition predictable.
Key idea
Stable prefix. Variable tail.
Reasoning models are powerful, but simple classification, extraction, formatting, and short summarization tasks should use the lowest reasoning level that still produces reliable results.
Key idea
Match reasoning effort to task risk.
Batch APIs trade real-time response guarantees for lower input and output token costs, making them a strong fit for nightly reports, CRM enrichment, document tagging, summarization, analysis, and backfills.
Key idea
Use real-time AI only when someone is waiting.
This series is for developers, founders, and engineering teams building AI features into real applications, especially if you are starting to see costs grow as usage increases.
calling LLMs from production code
embedding AI features into a SaaS product
managing multiple prompts, models, or providers
dealing with rising token usage
trying to control usage by customer or plan
adding AI features but worried about margin
moving from prototype to production
These videos use examples from tools we are building at PredictabilityAtScale.
Open-source prompt management for AI applications. Keep prompts, schemas, metadata, environments, and tests closer to your code.
promptopskit.com
AI usage metering, limits, entitlements, billing, and cost visibility for applications using LLMs and other usage-based features.
usagetap.com
Routing, caching, batching, context control, and model operations for production AI apps.
llmasaservice.io
Cost control should feel like an engineering discipline, not a panic exercise after the bill arrives.