Just tried to use Kimi's API to process a few hundred PDFs for a RAG pipeline. It's a straightforward batch job: send doc, get structured JSON, move on. Their documentation casually mentions "dynamic rate limiting" for the Moonshot API, which is corporate speak for "we'll throttle you and you'll figure out the limits by having your job crash."
My script, which uses a simple token bucket pattern, got hammered with 429s after about 50 documents. The retry logic in their SDK is basically non-existent. You're left to implement exponential backoff and hope. Here's the naive loop that failed:
```python
for doc in doc_collection:
response = client.chat.completions.create(
model="moonshot-v1-32k",
messages=[{"role": "user", "content": f"Extract entities from: {doc['text']}"}]
)
# 429 lands here, script dies
```
The real kicker? There's no published hard numbers for tiered limits (requests/minute, tokens/minute). You have to contact support and beg for a quota increase, which defeats the purpose of an API for automation. This feels like a classic vendor move: attract devs with capable models, then make scaling a painful negotiation to push you toward some "enterprise" plan.
Has anyone actually gotten a clear, written SLA on the rate limits for the standard API tier? Or found a reliable way to structure batch jobs that doesn't involve stitching together a circuit breaker and a queue system from scratch? I'm considering switching to a self-hosted model just to avoid this opaque throttling. The processing is great when it works, but the reliability for batch operations is a joke.
Just my 2 cents
Dynamic rate limiting is the API world's version of "the beatings will continue until morale improves." You hit the core issue: they want you to automate until you're successful, then they make automation impossible.
The token bucket pattern is a decent start, but it's guessing against a black box. Their system is likely judging you on total concurrent requests or aggregate tokens per sliding window, not a simple steady drip. Your loop fires and forgets, giving you no chance to adapt.
Skip the SDK's retry logic if it's brittle. Implement a client-side circuit breaker with jittered backoff, and more importantly, track your actual throughput and latency. The limits often adjust based on your recent behavior and overall cluster load. You might find that sending 20 docs, then pausing for 45 seconds, gets you further than a constant trickle. It's frustrating engineering for a moving target.
And yes, the quota negotiation is the old enterprise playbook dressed as a cloud API. They're filtering for customers willing to get on the phone and pay more.
keep it simple
Totally feel that "moving target" frustration. You're spot on about tracking throughput and latency - that's been key for us when integrating any external API into our sales automation pipelines. We started logging every single response time and token count to a time-series database, which let us visualize the actual, shifting rate limit window.
A caveat from our experience, though: sometimes the pause-and-chunk strategy you mentioned can backfire if the provider's system interprets the sudden burst of requests after a quiet period as a new, aggressive client. We had to smooth it out into more of a staggered, continuous flow, almost like a heartbeat, to avoid tripping some other hidden concurrency rule.
Have you found any specific tools or libraries that help with this kind of adaptive client-side rate limiting, or is it all custom instrumentation at this point?
Pipeline is king.
The visualization approach with a time-series DB is genius, it turns a mystery into a data problem. We did something similar for S3 cost attribution and it's the same principle.
Your point about the "heartbeat" flow is crucial. A burst after silence can look like a DDoS probe. The trick is to treat the rate limiter as a PID controller - you're constantly adjusting based on the error (429s) and the rate of change (response time deltas). Most generic libraries like `tenacity` or `backoff` don't get this nuanced.
I've had decent luck with `celery` for this, not for its queueing but for its `rate_limit` decorator applied at the task level. You feed it metrics from your monitoring and dynamically adjust the limit string. It's still mostly custom glue, though. The real pain is when they limit by tokens *and* RPM - then you're juggling two knobs in the dark.
Has anyone tried to reverse-engineer the actual sliding window by injecting progressively larger bursts and logging the exact timestamps of 429s?