API rate limiting best practices
AI Search Visibility Analysis
Analyze how brands appear across multiple AI search platforms for a specific query
Total Mentions
Total number of times a brand appears
across all AI platforms for this query
Platform Presence
Number of AI platforms where the brand
was mentioned for this query
Linkbacks
Number of times brand website was
linked in AI responses
Sentiment
Overall emotional tone when brand is
mentioned (Positive/Neutral/Negative)
Brand Performance Across AI Platforms
| BRAND | TOTAL MENTIONS | PLATFORM PRESENCE | LINKBACKS | SENTIMENT | SCORE |
|---|---|---|---|---|---|
1Redis | 3 | 0 | 95 | ||
2Kong | 1 | 1 | 63 | ||
3Zuplo | 1 | 1 | 63 | ||
4GitHub | 0 | 2 | 61 | ||
5Okta | 0 | 1 | 55 | ||
6Cloudflare | 0 | 1 | 55 | ||
7Moesif | 0 | 1 | 55 |
Strategic Insights & Recommendations
Dominant Brand
Redis is consistently mentioned across platforms as the preferred solution for distributed rate limiting in high-concurrency environments.
Platform Gap
ChatGPT focuses on structured best practices, Google AIO emphasizes gateway solutions, while Perplexity provides more technical implementation details.
Link Opportunity
There's an opportunity to create comprehensive guides linking rate limiting tools like Kong, Zuplo, and Redis with specific implementation examples.
Key Takeaways for This Query
Implement granular rate limits at multiple levels (user, IP, endpoint, global) for precise control and system protection.
Use appropriate algorithms like token bucket for burst traffic or sliding window for smoother rate limiting based on your API needs.
Provide clear communication through HTTP headers (X-RateLimit-*) and informative error messages to improve developer experience.
Monitor usage patterns continuously and adjust limits dynamically to balance performance, cost-efficiency, and fair resource allocation.
AI Search Engine Responses
Compare how different AI search engines respond to this query
ChatGPT
BRAND (1)
SUMMARY
ChatGPT provides a comprehensive 10-point guide covering clear rate limit policies, granular implementation, algorithm selection (fixed window, sliding window, leaky bucket, token bucket), proper HTTP response codes, monitoring, distributed systems, error messaging, user tier differentiation, throttling mechanisms, and regular policy reviews. The response emphasizes transparency through HTTP headers and documentation.
REFERENCES (5)
Perplexity
BRAND (5)
SUMMARY
Perplexity presents expert-backed recommendations emphasizing alignment with actual usage patterns, multi-tiered limits, dynamic distributed rate limiting using Redis, clear client communication, intelligent retry mechanisms with exponential backoff, request prioritization, caching strategies, appropriate timeouts, critical endpoint protection, robust client identification, and continuous monitoring for optimization.
REFERENCES (8)
Google AIO
BRAND (4)
SUMMARY
Google AIO offers a structured approach focusing on traffic pattern analysis, algorithm selection, key-level limits, API gateway usage, and graceful error handling. It covers four main algorithms, emphasizes centralized management through gateways like Kong and Zuplo, and highlights the importance of monitoring metrics and dynamic adjustments based on real-time conditions.
REFERENCES (30)
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