Fireworks AI rate limits
Quick answer
Fireworks AI enforces rate limits on API requests to prevent abuse and ensure service stability. When you exceed these limits, the API returns a
RateLimitError. To handle this, implement exponential backoff retry logic around your API calls to gracefully recover from rate limiting. ERROR TYPE
api_error ⚡ QUICK FIX
Add exponential backoff retry logic around your API call to handle
RateLimitError automatically.Why this happens
Fireworks AI applies rate limits to control the number of API requests per minute or second from a client. If your code sends requests too quickly or in bursts, the API responds with a RateLimitError, typically with HTTP status 429. This error indicates you must slow down your request rate.
Example of triggering code without retry logic:
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["FIREWORKS_API_KEY"])
response = client.chat.completions.create(
model="accounts/fireworks/models/llama-v3p3-70b-instruct",
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 API calls with exponential backoff retry logic to handle RateLimitError. This approach waits and retries the request after increasing delays, preventing immediate repeated failures.
Example with retry using time.sleep and catching RateLimitError:
from openai import OpenAI
import os
import time
from openai import RateLimitError
client = OpenAI(api_key=os.environ["FIREWORKS_API_KEY"])
max_retries = 5
retry_delay = 1 # seconds
for attempt in range(max_retries):
try:
response = client.chat.completions.create(
model="accounts/fireworks/models/llama-v3p3-70b-instruct",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)
break
except RateLimitError:
print(f"Rate limit hit, retrying in {retry_delay} seconds...")
time.sleep(retry_delay)
retry_delay *= 2 # exponential backoff
else:
print("Failed after multiple retries due to rate limits.") output
Rate limit hit, retrying in 1 seconds... Rate limit hit, retrying in 2 seconds... 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 respect Fireworks AI documented rate limits.
- Use client-side rate limiting or request pacing to smooth out bursts.
- Consider caching frequent responses to reduce API calls.
- Handle other transient errors similarly to maintain resilience.
Key Takeaways
- Fireworks AI rate limits trigger
RateLimitErrorwhen exceeded. - Use exponential backoff retry logic to handle rate limits gracefully.
- Monitor and pace your API requests to prevent hitting limits in production.