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

Ultra subscribers reported that after the latest Antigravity update, quota now drains 5–6x faster. Workflows that used to run for hours hit 0% in under 90 minutes, and users are prompted for overages without clear visibility.

This guide shows the fastest way to stop the bleed, regain headroom, and prove any mis-metering.

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

Teams on Ultra are seeing a sudden, steep drop in remaining credits during normal workloads. Symptoms include: quota falling to zero within 60–90 minutes, “overage” prompts on Ultra, and no reliable real‑time view of usage. Several users mention the Claude quota emptying mid‑day while running agents in Antigravity.

The change aligns with a platform update that likely altered how credits are accounted (per‑minute agent runtime, tool calls, streaming, and larger default context windows can all compound usage). Concurrency multiplies costs; the same workflow run in parallel can now exhaust credits far faster than before.

If your agents stop abruptly when a hidden trigger trips a cap, see this trigger-focused explainer.

Solution Overview

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

Step-by-Step Solution

1) Baseline actual spend per request (add usage headers to logs)

• For Claude API, capture rate‑limit/usage headers to see real capacity and burn:

# Add -i to capture headers and log them
curl -sS -i https://api.anthropic.com/v1/messages \
 -H "x-api-key: $ANTHROPIC_API_KEY" \
 -H "anthropic-version: 2023-06-01" \
 -H "content-type: application/json" \
 -d '{
 "model": "claude-3-5-sonnet-20241022",
 "max_tokens": 512,
 "messages": [{"role":"user","content":"Return a 1-sentence summary."}]
 }' | tee /tmp/anthropic.log

# Extract key headers (remaining tokens/requests per window)
grep -i "anthropic-ratelimit" /tmp/anthropic.log

These headers (e.g., anthropic-ratelimit-requests-remaining, -tokens-remaining) help quantify real‑time limits and prove any regression. Reference: Anthropic rate limits.

• For Gemini API, log responses and enable generation config caps:

# Example: Gemini via REST with explicit caps
curl -sS https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key="$GEMINI_API_KEY" \
 -H "content-type: application/json" \
 -d '{
 "contents": [{"role": "user", "parts":[{"text":"Return a 1-sentence summary."}]}],
 "generationConfig": {"maxOutputTokens": 256, "temperature": 0.2, "topK": 40, "topP": 0.9}
 }' | tee /tmp/gemini.log

Read: Gemini API rate limits and AI credits and pricing.

2) Cap tokens and shrink context immediately

• Large system prompts and long conversation history silently drain credits. Set hard caps and trim context. Anthropic example:

curl -sS https://api.anthropic.com/v1/messages \
 -H "x-api-key: $ANTHROPIC_API_KEY" \
 -H "anthropic-version: 2023-06-01" \
 -H "content-type: application/json" \
 -d '{
 "model": "claude-3-5-sonnet-20241022",
 "max_tokens": 512,
 "system": "You are concise. Keep outputs <= 150 words.",
 "messages": [
 {"role":"user","content":"Summarize this:"}
 ],
 "metadata": {"project":"antigravity-prod"}
 }'

Gemini example:

curl -sS https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key="$GEMINI_API_KEY" \
 -H "content-type: application/json" \
 -d '{
 "generationConfig": {"maxOutputTokens": 256, "temperature": 0.2},
 "contents": [{"role":"user","parts":[{"text":"Summarize this:"}]}]
 }'

Tip: Cap output first (max tokens), then reduce prompt size and history length.

3) Reduce parallel agents (the biggest multiplier)

• If you previously ran “multiple agents for 5+ hours,” parallelism is likely multiplying new credit accounting. Node.js concurrency guard with p-limit:

import pLimit from 'p-limit';
const limit = pLimit(3); // start with 2–3
const tasks = items.map(item => limit(() => runAgent(item)));
await Promise.all(tasks);

Bash semaphore (GNU parallel alternative):

N=3
for job in "${JOBS[@]}"; do
 ((i=i%N)); ((i++==0)) && wait
 run_agent "$job" &
done
wait

4) Disable default streaming/tool-calls unless required

• Streaming and frequent tool/function calls add token and runtime overhead. Anthropic: remove or restrict tools; request non‑streaming responses.

{
 "model": "claude-3-haiku-20240307",
 "max_tokens": 256,
 "messages": [{"role":"user","content":"Short answer only."}],
 "tools": [] // ensure no tool-call inflation
}

Gemini: prefer non‑streaming endpoints for batch jobs and avoid function calling unless essential.

5) Move background tasks to cheaper/burst‑friendly models

• Keep high‑IQ steps on premium models; offload routine summarization/classification to smaller ones.
• Claude: opus/sonnet → haiku where possible.
• Gemini: 1.5 Pro → 1.5 Flash for bulk tasks.

6) Stop infinite retries and loops

• Retries can silently triple spend under higher error rates. Example guard (TypeScript):

async function withRetry<T>(fn: () => Promise<T>, max=1) { // max=1 disables multi-retry
 try { return await fn(); } catch (e) { throw e; }
}

7) Add spend guards and alerts right now

• Hard cap tokens per request, per session, and per minute.
• Log input/output token counts and ratelimit headers. Quick shell counter for Anthropic headers:

awk 'BEGIN{FS=": "}/anthropic-ratelimit-requests-remaining|anthropic-ratelimit-tokens-remaining/{print}' /tmp/anthropic.log

• Set alerts when remaining drops under a threshold so you can throttle proactively.

If you’re hitting abrupt terminations after the update, this guide on critical quota errors in Antigravity explains typical kill conditions and safe guards.

Alternative Fixes & Workarounds

• Split workloads across projects/accounts (if terms allow) to bypass per‑project caps during investigation.
• Schedule heavy jobs to run sequentially during lower traffic hours.
• Cache deterministic results (idempotent prompts, embeddings), and short‑circuit repeats.
• Pre‑chunk long inputs client‑side to avoid huge single prompts.
• Request a temporary quota bump while you adapt your configuration; include before/after metrics.

Troubleshooting Tips

• Check for hidden token drains:
• Very long system prompts, RAG context dumps, or excessive tool/function JSON.
• Image/audio inputs or large files: these are far costlier than plain text.
• Streaming turned on by default in SDKs.
• Confirm model migrations: a silent switch (e.g., to a larger context or pricier model) can multiply costs.
• Validate concurrency actually obeys the limit; thread pools often exceed caps under burst.
• Compare your measured per‑request tokens to pricing sheets:
• Anthropic: Claude pricing
• Google: Gemini pricing
• If Antigravity shows agent terminations after quota trips, see this fix: resolving agent terminated errors.

Best Practices

• Enforce request‑level budgets: max input tokens, max output tokens, and max tool‑call depth.
• Keep prompts short; move bulky instructions to lightweight lookups.
• Use cheaper models for background/batch; reserve premium models for the small set of reasoning steps that need them.
• Add concurrency guards at the orchestration layer; default to sequential for long‑running chains.
• Log and review monthly: sampled inputs, outputs, token counts, headers, and cost estimates.

Final Thought

The update most likely changed how credits are counted, not just how they’re displayed. By capping tokens, reducing parallel agents, and turning off default streaming/tool-calls, most teams recover hours of headroom immediately. If usage still looks off after these changes, your logs and headers provide the proof you need for support to correct plan mapping or fix mis‑metering.