4 practical pillars of token engineering (beyond prompt compression)

As smaller, cheaper models become more capable, striking the right balance between the costs, speed, and the desired outcomes is what token engineering is all about. Early on, token management was mostly about prompt compression, squeezing text to fit into tight 4k context windows. Today, with massive context windows and autonomous agent loops, token engineering has shifted to systems-level architecture.

Here are the 4 practical pillars to optimize your costs at scale:

by @tweetpraveen

If you are running multi-step agent loops, conversation history grows on every turn. To avoid blowing your token budget, prune intermediate tool outputs and summarize past steps rather than sending the entire unedited history back to the model.

For instance, if a coding agent runs tests 10 times to fix a bug, resending thousands of lines of terminal output on every turn forces the model to re-read errors it already resolved. By trimming logs down to the active failure and summarizing past fixes into a single sentence, your prompt stays tiny and saves thousands of tokens per step. Similarly, let’s say an agent compares 5 different airlines and hotels across a 10-step trip planning loop. Instead of carrying the full raw html from every website that it visits,the agent should only save necessary data like the pricing and flight dates.

For repetitive tasks relying on large documents or codebases, context caching (or prompt caching) lets you upload reference material once, freeze it in memory, and query against it repeatedly with up to a 75% discount on input costs.

A mature agent harness (like Antigravity) will handle context caching for you under the hood. But keep in mind, if you are building directly against raw LLM APIs or running local models without a harness, it may lead to significantly more variable costs and unpredictable cache misses.

When it comes to workloads, not every task requires an instant sub-second response. This is where batch inference comes into play. Think of it like doing dishes. Running a full dishwasher overnight is more efficient than washing one coffee mug at a time. If you have tasks like summarizing hundreds/thousands of support tickets, generating weekly reports, or evaluating test datasets, you don’t need a live model answering in milliseconds. By grouping these tasks together and letting the Cloud process them asynchronously in the background, you get the same high-quality output for a flat 50% discount.

For example, if you have an accounting system that needs to extract data from vendor invoices submitted throughout the week, it doesn’t need instant responses during business hours. Grouping those PDFs into a scheduled midnight batch job cuts your processing bill in half without slowing anyone down.

Modern reasoning models generate dynamic ‘thinking tokens’ under the hood before delivering their final answer. While deep reasoning is incredible for complex math and architecture, it can be a massive silent cost driver. An unconstrained reasoning model might burn 3,000 hidden thinking tokens just to answer a simple categorization question that only needed 50 tokens. So set explicit thinking budgets based on query complexity, or route simple tasks away from reasoning models entirely. Don't use a heavy reasoning engine when a deterministic prompt will do.

Finally, how and where your requests are routed matters just as much as what you send.

The 90% rule: Use the efficient models and swap aggressively

“In reality 90% of modern token engineering comes down to the principle of using the smallest, cheapest models in the efficiency frontier that can reliably do the job for your at-scale workloads.”

High-volume tasks like classification, data extraction, and basic summarization do not need a costly, leading-frontier model. You can typically run these tasks with similar accuracy when you use efficient models like Gemini Flash or lightweight, hosted open models like Gemma, bringing your cost down to pennies per million tokens.

One more secret weapon for successful AI teams is continuous evaluation.Whenever a new lightweight model launches, make it a practice to run automated eval test suites against your benchmarks. If the model scores within a couple percentage points in range of your benchmark quality, you swap it into production and reduce the monthly inference bill immediately, without any end-user impact.

Using managed serverless model endpoints like Gemini 3.7 Flash means you pay strictly for the tokens you consume, rather than paying for expensive, 24/7 self-hosted GPU clusters that sit idle overnight. You can also take advantage of Gemini 3.7 Flash promotional pricing running through December 31, 2026 before standard rates take effect in 2027, giving you a massive runway to build and scale economically.

Centralized AI gateway and governance for enterprise scale

For organizations already operating at high-model usage across multiple engineering teams, managing that traffic centrally becomes the next critical lever. An enterprise gateway like Apigee AI Gateway acts as a central traffic controller, intelligently routing requests to the cheapest model or cache endpoint based on latency needs. Working alongside Model Armor, it protects both your budget and your data by intercepting prompt injection attacks, jailbreaks, and sensitive data leakage at the gateway level before the prompt ever hits the model.

The Takeaway

Token engineering is just building good architectural habits across your prompts, your workloads, and your routing layer:

What strategies are you using to keep AI costs under control? Let us discuss in the comments!