--- canonical: https://aidevhub.net/blog/google-antigravity-prompt-caching title: "Google Antigravity Token Cost: How to Cut Your API Bill up to 90% with Prompt Caching" description: "Cut Google Antigravity token costs by up to 90% using Gemini context (prompt) caching: how the cache works, TTL management, and when caching makes agentic coding dramatically cheaper on large repos." author: "AI Dev Hub" published: 2026-05-24 updated: 2026-09-15 license: CC BY 4.0 --- > Source: [Google Antigravity Token Cost: How to Cut Your API Bill up to 90% with Prompt Caching](https://aidevhub.net/blog/google-antigravity-prompt-caching) — AI Dev Hub. License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) — reuse permitted with attribution. # Google Antigravity Token Cost: How to Cut Your API Bill up to 90% with Prompt Caching If you have ever run an autonomous AI agent over a large codebase for several hours, you are probably familiar with **token shock**. Because agents need to continually read files, remember system rules, and parse compiler outputs, the input context grows exponentially with each turn of the conversation. In a large repository, a single simple bug-fixing task can consume millions of tokens, leading to high API bills. To keep AI-native engineering affordable, **Google Antigravity 2.0** leverages one of Gemini's most powerful native API features: **Context Caching** (also known as Prompt Caching). Let's dissect how this technology slashes agentic development costs by up to 90%. > **Quick Answer:** Antigravity 2.0 automatically registers a Gemini **context cache** (min 32k tokens, ~300s TTL) containing your static context — rules, file tree, unchanged files — when the CLI launches. Every subsequent agent turn references the pre-compiled cache instead of re-parsing source code from scratch, cutting input-token costs by **50–90%** and slashing time-to-first-token. You don't configure anything manually: the cache is created, TTL-refreshed on activity, and evicted after ~5 min idle. --- ## The Problem: The Exponential Context Tax In a typical multi-turn agent conversation, the agent's input context looks like this: ``` Turn 1: [System Rules + Codebase Directory + File A] -> Agent Response Turn 2: [System Rules + Codebase Directory + File A] + [File B + Compile Logs] -> Agent Response Turn 3: [System Rules + Codebase Directory + File A + File B + Compile Logs] + [Test Run Outputs] -> Agent Response ``` As the dialogue progresses, the static files (System Rules, Directory structure, unchanged codebase files) are **re-sent and re-processed** by the model on every single turn. You are billed full price for reading the exact same files repeatedly. --- ## The Solution: Gemini Context Caching Gemini's **Context Caching** allows the API to cache a large, static block of tokens (minimum 32k tokens) in memory. When subsequent API requests are made, the model references the pre-compiled cache rather than processing the files from scratch. ```mermaid sequenceDiagram participant CLI as Antigravity CLI participant Cache as Gemini Context Cache (SSD/RAM) participant Model as Gemini Core Model CLI->>Cache: Set static base (Rules, File Tree, Core Files) Note over Cache: Cache Compiled (Expires in 300s+) CLI->>Model: Send Turn 1 Query + Reference Cache Model->>CLI: Return Answer (Billed at Caching Discount) CLI->>Model: Send Turn 2 Query + Reference Cache Model->>CLI: Return Answer (Zero re-parse cost for static files!) ``` ### The Cost Savings Breakdown By caching the static codebase context: * **Input Token Pricing**: Reading cached tokens is priced at a **substantial discount** compared to fresh input tokens (often cutting input costs by 50% to 90% depending on cache hits). * **Processing Latency**: Because the model doesn't need to re-parse millions of characters of source code on every message, Time-to-First-Token (TTFT) drops from seconds to milliseconds. --- ## How Antigravity 2.0 Manages the Cache Antigravity optimizes the cache lifecycle automatically under the hood: 1. **Warmup Phase**: On launch, the CLI calculates the token size of your project's index, folder structures, and active rules. If it exceeds 32k tokens, it registers a new Gemini API Context Cache with a default Time-To-Live (TTL) of 300 seconds. 2. **TTL Auto-Renewal**: Every time you send a new command or the compiler outputs logs, Antigravity sends the delta while automatically pinging the API to extend the cache's TTL. 3. **Cache Eviction**: When you close the terminal or go idle for more than 5 minutes, the cache expires automatically, ensuring no stale tokens linger or inflate storage costs. --- ## Conclusion: Making Agents Practical Context caching shifts autonomous programming from a costly experiment to a practical daily tool. By minimizing repetitive token processing, Antigravity 2.0 allows you to run hours of autonomous coding, testing, and debugging loops for less than the cost of a cup of coffee. --- --- ## Explore the Complete Google Antigravity Guide Series - [Start here: install Antigravity, API keys & your first /goal task in 7 steps](/blog/google-antigravity-tutorial-guide) - [Cost & latency estimates: what a task actually costs on a Next.js codebase (and how to measure your own)](/blog/google-antigravity-benchmark-2026-09) - [Run parallel subagents safely on isolated branches](/blog/google-antigravity-subagent-orchestration) - [How Antigravity writes tests and fixes its own bugs](/blog/google-antigravity-tdd-self-correcting-loop) --- *© 2026 AI Dev Hub · Canonical: https://aidevhub.net/blog/google-antigravity-prompt-caching · License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) — reuse permitted with attribution.*