Fix Together AI rate limit error
Quick answer
A RateLimitError from Together AI occurs when your app exceeds the allowed request rate. Add exponential backoff retry logic around your API calls to automatically handle these errors and avoid immediate failures.
ERROR TYPE
api_error ⚡ QUICK FIX
Add exponential backoff retry logic around your API call to handle RateLimitError automatically.
Why this happens
Together AI enforces rate limits to prevent abuse and ensure fair usage. When your application sends requests too quickly, the API responds with a RateLimitError. This error typically looks like:
openai.error.RateLimitError: You have exceeded your current quota, please check your plan and billing details.Example of code triggering this error without retries:
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["TOGETHER_API_KEY"])
response = client.chat.completions.create(
model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content) output
openai.error.RateLimitError: You have exceeded your current quota, please check your plan and billing details.
The fix
Wrap your Together AI API calls with exponential backoff retry logic to handle RateLimitError gracefully. This approach retries the request after increasing delays, reducing the chance of repeated failures.
Example fixed code using time.sleep and retries:
from openai import OpenAI
import os
import time
client = OpenAI(api_key=os.environ["TOGETHER_API_KEY"], base_url="https://api.together.xyz/v1")
max_retries = 5
retry_delay = 1 # initial delay in seconds
for attempt in range(max_retries):
try:
response = client.chat.completions.create(
model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)
break # success, exit loop
except Exception as e:
from openai import RateLimitError
if not isinstance(e, RateLimitError):
raise
if attempt == max_retries - 1:
raise # re-raise after last attempt
time.sleep(retry_delay)
retry_delay *= 2 # exponential backoff output
Hello! How can I assist you today?
Preventing it in production
- Implement robust retry logic with exponential backoff and jitter to avoid synchronized retries.
- Monitor your API usage and rate limit headers to proactively adjust request rates.
- Use client-side rate limiting or queueing to smooth request bursts.
- Consider fallback models or cached responses when rate limits are hit.
Key Takeaways
- Use exponential backoff retry logic to handle Together AI RateLimitError automatically.
- Monitor API usage and implement client-side rate limiting to prevent hitting rate limits.
- Always get your API key from environment variables and never hardcode it in code.