How to fix Ultra dramatic quota reduction after update in Antigravity?

Many Ultra tier users report that AI credits now drain far faster after a recent update. Workloads that previously ran for hours now exhaust quota in under 90 minutes.

Ultra tier quotas consumed in 90 minutes after update

Developers on the highest plan are seeing a 5 to 6 times effective reduction in throughput. Symptoms include quota dropping to zero rapidly, unclear credit accounting, no real time usage visibility, and identical workloads consuming far more credits than before. If this feels like a downgrade masked as a structural change, you are not alone. The fastest path to relief is to reduce concurrent calls, shrink prompt and response sizes, and route non critical tasks to a lighter model immediately. For context on patterns that trigger quota throttling, see quota trigger patterns.

Solution Overview

Aspect	Detail
Root Cause	Credit accounting and rate windows changed, so concurrent agents plus larger prompts now burn credits much faster and hit tighter per minute windows
Primary Fix	Cap concurrency, trim context and outputs, split traffic to lighter models for background tasks, and log usage metadata to verify savings
Complexity	Easy
Estimated Time	30 to 60 Minutes

Stabilize credits now by capping concurrency and shrinking tokens

Step-by-Step Solution

1. Verify your plan and credits in the console

• Open AI Studio or your project dashboard and confirm available credits and rate limits for your account.
• Compare current numbers to what you expect. Pricing and limits reference: Gemini API pricing and Gemini API docs.

2. Add a hard concurrency limit in code High parallelism multiplies credit burn in tighter windows. Cap concurrent requests. Node.js

npm install @google/generative-ai bottleneck
export GOOGLE_API_KEY="YOUR_KEY"

const { GoogleGenerativeAI } = require("@google/generative-ai");
const Bottleneck = require("bottleneck");

const genAI = new GoogleGenerativeAI(process.env.GOOGLE_API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-1.5-pro" });

// Allow only 2 concurrent calls, add small spacing between calls
const limiter = new Bottleneck({ maxConcurrent: 2, minTime: 200 });

async function generateOne(prompt) {
 const res = await model.generateContent({
 contents: [{ role: "user", parts: [{ text: prompt }]}],
 generationConfig: { maxOutputTokens: 512, temperature: 0.4 },
 });
 return res.response.text();
}

// Queue your tasks
async function runAll(prompts) {
 const queued = prompts.map(p => limiter.schedule(() => generateOne(p)));
 return Promise.all(queued);
}

Python

pip install google-generativeai
export GOOGLE_API_KEY="YOUR_KEY"

import asyncio, os, google.generativeai as genai
genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
model = genai.GenerativeModel("gemini-1.5-pro")

sem = asyncio.Semaphore(2) # two at a time

async def generate_one(prompt: str):
 async with sem:
 resp = await model.generate_content_async(
 prompt,
 generation_config={"max_output_tokens": 512, "temperature": 0.4},
 )
 return resp.text

async def run_all(prompts):
 tasks = [asyncio.create_task(generate_one(p)) for p in prompts]
 return await asyncio.gather(*tasks)

For failure patterns and error pressure under load, scan these notes on critical quota errors. 3. Trim prompt context and cap outputs Large context balloons credit use. Keep only the last few turns and cap outputs. Node.js

function buildPrompt(history, newUserMsg, keep=3) {
 const recent = history.slice(-keep);
 const sys = "You are a helpful assistant. Answer concisely.";
 const parts = [{role:"user", parts:[{text: sys}]}];
 for (const h of recent) parts.push(h);
 parts.push({role:"user", parts:[{text: newUserMsg}]});
 return parts;
}

const res = await model.generateContent({
 contents: buildPrompt(chatHistory, "Summarize the findings in 5 bullets."),
 generationConfig: {
 maxOutputTokens: 300, // lower cap
 temperature: 0.3,
 topP: 0.9
 }
});

Python

history = history[-3:] # keep last three turns
resp = await model.generate_content_async(
 [*history, {"role":"user", "parts":[{"text":"Return a 100 word summary."}]}],
 generation_config={"max_output_tokens": 300, "temperature": 0.3}
)

4. Route background tasks to a lighter model Keep heavy reasoning on the stronger model and shift bulk work to a lighter option. Node.js

function pickModel(task) {
 if (task.type === "routing" || task.type === "bulk_extraction") {
 return genAI.getGenerativeModel({ model: "gemini-1.5-flash" });
 }
 return genAI.getGenerativeModel({ model: "gemini-1.5-pro" });
}

5. Cache and de duplicate repeated prompts Many agents ask the same questions. Cache by prompt hash. Python

import hashlib, sqlite3, json, os, google.generativeai as genai
genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
db = sqlite3.connect("cache.sqlite")
db.execute("create table if not exists cache (k text primary key, v text)")
db.commit()

def key_for(prompt: str) -> str:
 return hashlib.sha256(prompt.encode("utf-8")).hexdigest()

def get_cached(prompt: str):
 row = db.execute("select v from cache where k=?", (key_for(prompt),)).fetchone()
 return None if not row else json.loads(row[0])

def put_cache(prompt: str, value: dict):
 db.execute("insert or replace into cache(k,v) values(?,?)", (key_for(prompt), json.dumps(value)))
 db.commit()

def cached_generate(prompt: str):
 if (hit := get_cached(prompt)):
 return hit
 model = genai.GenerativeModel("gemini-1.5-pro")
 resp = model.generate_content(prompt)
 payload = {"text": resp.text}
 put_cache(prompt, payload)
 return payload

6. Log usage metadata and stop early when credits spike The API returns usage metadata you can track. Log and alert when requests or tokens approach your threshold. cURL

curl -s -X POST \
 "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro:generateContent?key=$GOOGLE_API_KEY" \
 -H "Content-Type: application/json" \
 -d '{
 "contents": [{"role":"user","parts":[{"text":"Return a 50 word abstract of the topic artificial intelligence safety."}]}],
 "generationConfig": {"maxOutputTokens": 200, "temperature": 0.2}
 }' | jq '.usageMetadata'

Expected fields include total token counts you can monitor. Reference: Gemini API docs. 7. Stagger long runs If you have multi hour flows, spread the workload across small windows so per minute and per hour windows are not exceeded, keeping credit velocity under control. For more real world quota throttling cases, check these examples.

Alternative Fixes & Workarounds

Ask for a quota review or plan adjustment

• If you are an enterprise customer, open a support case and request clarification on new credit accounting and rate windows. Include before and after graphs.
• Cloud customers can start here: Google Cloud Support.

Use streaming and early cut off

• Switch to streaming responses for long generations, and stop early when you have enough content to reduce tokens.

Reduce attachments and large inputs

• Images and long files inflate tokens. Replace with short summaries if possible.

Schedule heavy jobs during quieter hours

• If you notice time based throttling, run batch jobs in off hours and keep interactive work responsive.

Troubleshooting Tips

• Confirm you are calling the intended endpoint and region. Mismatched regions can have different rate windows on some platforms.
• Ensure retry logic is not multiplying consumption. Add exponential backoff with a maximum attempt cap.
• Check that you are not repeatedly sending long system instructions in every turn. Send them once and keep turns short.
• Validate that you are not accidentally running multiple agents against the same task at once.
• Inspect logs for abnormally high max output tokens. Keep caps tight.

Best Practices

• Set app level budgets and alerts using usage metadata to fail fast before credits hit zero.
• Keep prompts short and focused. Prune history aggressively.
• Enforce concurrency limits in a single shared queue across all services.
• Split traffic by task type so only the small set of tasks that benefit from higher quality use the stronger model.
• Add caching for repeated questions and common utility prompts.

Final Thought

The update changed the economics of each request. By limiting concurrency, shrinking tokens, and routing background work to a lighter model, you can restore hours of useful throughput and avoid sudden zero credit events. Keep usage visible in logs and you will prevent this from catching you off guard next time. Read More: Antigravity Trigger Quota Limit Read More: Critical Quota Error Antigravity Read More: Antigravity Trigger Quota Limit 2