Rate Limits

OfoxAI’s rate limits ensure platform stability. Understanding the limits helps you optimize your API usage.

Default Limits

OfoxAI uses pay-as-you-go pricing with a unified rate policy for all users:

Limit	Quota
RPM (requests/minute)	100 (team-aggregated)
TPM (tokens/minute)	Unlimited

Team-level aggregation: RPM is calculated cumulatively across the entire organization (team). Multiple API Keys under the same team share a single quota, preventing multi-key stacking from breaking through upstream provider rate limits at the source. If you need a higher RPM quota, contact [email protected] to request an adjustment.

Rate Limit Headers

Every API response includes rate limit information:


x-ratelimit-limit-requests: 100
x-ratelimit-remaining-requests: 95
x-ratelimit-reset-requests: 12s

Header	Description
`x-ratelimit-limit-requests`	RPM limit value
`x-ratelimit-remaining-requests`	Remaining request count
`x-ratelimit-reset-requests`	Time until request limit resets

Handling 429 Errors

When rate-limited, the API returns 429 Too Many Requests:


from openai import RateLimitError
import time
 
try:
    response = client.chat.completions.create(...)
except RateLimitError as e:
    retry_after = float(e.response.headers.get("retry-after", 1))
    print(f"Rate limited, waiting {retry_after}s...")
    time.sleep(retry_after)

Optimization Strategies

1. Use Prompt Caching

For repeated system prompts, enabling caching reduces token consumption:


response = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[
        # Long system prompts are automatically cached
        {"role": "system", "content": "You are a professional... (long text omitted)"},
        {"role": "user", "content": "User question"}
    ]
)

See Prompt Caching for details.

2. Batch Processing

Consolidate multiple short requests into a single request:


# ❌ Not recommended: separate request for each question
for question in questions:
    client.chat.completions.create(messages=[{"role": "user", "content": question}])
 
# ✅ Recommended: combine into one request
combined = "\n".join(f"{i+1}. {q}" for i, q in enumerate(questions))
client.chat.completions.create(
    messages=[{"role": "user", "content": f"Please answer the following questions:\n{combined}"}]
)

3. Choose the Right Model

For recommended models, see the Model Marketplace .

4. Control max_tokens

Set a reasonable max_tokens limit to avoid unnecessary token consumption:


response = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "Summarize in one sentence"}],
    max_tokens=100  # Limit output length
)

5. Use Model Fallback

Automatically switch to alternative models when the primary model hits its limit:


response = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[...],
    extra_body={
        "provider": {
            "fallback": ["anthropic/claude-sonnet-4.6", "google/gemini-3.1-flash-lite-preview"]
        }
    }
)

See Fallback for details.