Topic
Rate limits & scaling comparison
12 posts on rate limits & scaling comparison — part of competitor comparisons on the n4n AI blog.
What happens when you hit a 429 on the OpenAI API
OpenAI API 429 error explained: triggers, rate limit mechanics, and production patterns for resilient LLM inference with code examples.
Understanding tokens-per-minute limits across LLM providers
Tokens-per-minute limits LLM providers restrict token throughput per 60-second window. This explainer covers how TPM works, why it matters, and mitigation.
Scaling past OpenAI's tier-based rate limits
Practical guide to scaling past OpenAI tier rate limits: map limits, reduce tokens, smart retries, multi-provider failover, batching, and per-token metering.
Scaling an LLM app from 10 to 10,000 requests per minute
Practical engineering path to scale an LLM app from 10 to 10,000 RPM: rate limits, concurrency control, queues, caching, fallback, and load testing.
Rate limits for Claude 3.5 Sonnet vs GPT-4o compared
A practical engineer's comparison of Claude 3.5 Sonnet vs GPT-4o rate limits across capabilities, cost, latency, and ecosystem, with a verdict.
OpenAI vs Anthropic: comparing default API rate limits
A practical head-to-head of OpenAI vs Anthropic rate limits: how defaults, tiers, and throttling differ, with code to inspect limits and a clear verdict.
n4n.ai vs OpenRouter: concurrency and throughput compared
A technical head-to-head on n4n.ai vs OpenRouter concurrency, covering throughput, rate limits, fallback behavior, and cost for production LLM systems.
How to handle rate limit errors in production LLM apps
Practical steps to handle rate limit errors production LLM apps with retries, fallbacks, and queueing. Code examples for resilient LLM integrations.
How multi-key pooling raises LLM API throughput
A rate limit pooling multi-key gateway aggregates multiple LLM API keys into one quota pool to boost throughput and avoid per-key rate limits in production.
How LLM API gateways raise effective rate limits
LLM API gateway rate limit pooling merges quotas across providers into one shared limit, boosting throughput via fallback and concurrency. Explainer.
Concurrent request limits across inference providers
Compare concurrent request limits inference providers: OpenAI, Anthropic, Google Gemini, Azure OpenAI, and Groq. Head-to-head table and which to choose.
Bring-your-own-key vs gateway pooled limits explained
Understand bring-your-own-key vs pooled rate limits: how BYOK and gateway pooling differ in quota, failover, and cost for LLM apps in production.
More topics in competitor comparisons
- Best API for coding assistants & AI IDEs15
- Gateway pricing & token markup comparison15
- Accessing Llama 4 across inference providers14
- Best API for AI agents & tool use14
- Best gateway for startups & indie developers14
- Framework integrations across gateways14
- Inference speed benchmarks14
- n4n vs calling providers directly14
- n4n vs OpenRouter14
- Accessing Claude Opus 4.8 via gateway vs Anthropic direct13
- Accessing DeepSeek models via gateway13
- Accessing Gemini 3 via gateway vs Google direct13