One of the most hyped releases recently has been Google’s Antigravity Integrated Development Environment (IDE). Unfortunately, it has many limitations in the form of rate limits, and hitting them is very easy to do.
I pay for Google AI Pro, and I still hit the rate limit when only doing two or three prompts. Luckily, through trial and error, I’ve found a way to use Antigravity without hitting those limits as often.
Optimize your cost vs speed
If you want to get the most efficiency out of Google Antigravity, you need to be smart about picking which agent you use. You have to keep in mind that the usage quotas are incredibly tight, and you can go over during a prompt, which means you wasted it. The easiest and most immediate thing you can do to keep your tasks from stopping abruptly is to move most of your daily coding work over to the Gemini 3 (Low) model. Don’t believe Google’s wording of “generous,” because it only refreshes once every five hours.
If you rely too much on the computationally intense Gemini 3 (High) thinking level for deep, parallel reasoning exploration, you are probably going to run out of resources right in the middle of a task. This has happened to me twice, and it was because I gave it too much to do in the end. That resource consumption happens because the model is generating hidden “Thinking Tokens” while it is doing its internal deliberation. These tokens count directly against your overall cost and quota usage for the LLMs.
The Low Thinking mode is designed to limit the model’s search space, which means you get really low latency and much faster performance. This makes it perfect for taking care of most of those routine coding tasks without blowing through the quota that is set during this free preview period.
I try to avoid using High until Low messes up the job at least three times, which happens, and that way I can use High for a much more precise query that will bypass all the issues Low has. It may be some time before High gives you more tokens to use, so you have to keep these things in mind.
Prioritize autonomous over interactive tasks
If you want to keep your usage quota safe in Google Antigravity, the key is to prioritize autonomous workflows instead of intense, real-time synchronous interaction. The platform breaks operations down into two separate models: the synchronous Editor View and the asynchronous Manager View.
The Editor View is the one that handles those back-and-forth, in-line commands and gives you real-time assistance. Since it is so highly interactive, this mode just naturally eats up tokens pretty quickly. If you are trying to save your quota, your best bet is to use the Agent Manager, which they call Mission Control, and delegate those big, multistep tasks to agents. This lets the agents run autonomously in the background.
For the best conservation, you should know that large, long-running agent tasks delegated in the Manager View need to use AIs other than the Gemini 3 (Low) model. Doing this keeps your limited quota from burning out fast. It also makes sure you get greater overall throughput. This approach frees you up so you can act more like an architect, orchestrating parallel work instead of having to focus on coding line by line.
You should also utilize Artifacts, which are those verifiable deliverables that the agent generates while it is working through the process. Artifacts are basically structured outputs. This includes things like task lists, implementation plans, screenshots, and even browser recordings.
They are key for building essential user trust because they document both what the agent plans to do and how it verifies the code it executes. For example, an agent uses Artifacts to verify its own work. It might do this by taking screenshots or videos of the application running right there in the browser. Artifacts also make a continuous feedback loop possible.
Developers can drop Google Docs-style comments directly onto an Artifact, and the agent automatically takes that feedback and works it into its ongoing task execution. This is great because it means you do not have to force a complete restart, which means you’re wasting fewer tokens.
The best way to use Google Antigravity without running out of tokens comes down to realizing that the specific LLMs it supports aren’t all the same. In other words, you need to match the model type to what the task actually requires. Antigravity handles this by giving you access to many AIs right within its system, like Gemini 3 Pro, Claude Sonnet 4.5, and OpenAI’s GPT-OSS.
Claude Sonnet 4.5 shows real muscle in detailed reasoning and creating documentation. On the other hand, GPT-OSS is valuable if you’re doing quick prototyping tasks. You need to use them where they excel, or you’ll run out of tokens using them in areas they aren’t as good at as the other agents you can use. So don’t try to use the best AI every single time.
When you’re managing usage quotas, you need to actively move tasks that don’t absolutely need Antigravity’s agent capabilities off the platform. Things that aren’t coding operations, like complex commands, data handling, or even simple debugging, should probably be done in your local development environment or using external tools like a standalone terminal. This prevents the IDE from using up too many resources and keeps you from hitting those tight rate limits too soon.
Antigravity has so many restrictions right now that I wouldn’t say it can be used by itself all the time. Even with my AI Pro account, I seem to hit that quota more often than I’d like.
By using these disciplined approaches, you are making sure that you get the maximum power, speed, and parallel capability out of Google Antigravity. Since this is in preview, we can assume that there will be some updates in the future, but we haven’t seen an indication of that yet.
These rate limits may ease off with time, but it will likely be easier for those who pay. So if this preview convinced you that Google’s VS Code clone was what you wanted, then you should think about getting a paid AI plan.