When building AI-powered applications, one of the most critical decisions you'll face is whether to use an LLM gateway or make direct API calls to your chosen model providers. This choice can significantly impact your application's performance, reliability, and operational complexity.
As teams scale their AI implementations, the trade-offs between latency, uptime, and management overhead become increasingly important. Let's dive into real-world benchmarks and explore when each approach makes sense for your use case.
Understanding the Fundamentals
Before we analyze the performance differences, let's clarify what we're comparing:
Direct API Calls connect your application directly to the LLM provider's endpoint (like OpenAI or Anthropic). This approach offers the shortest path between your application and the model, potentially minimizing latency.
LLM Gateways act as an intelligent intermediary layer, managing all traffic between your applications and various LLM providers. Modern gateways like Requesty provide centralized control, security, monitoring, and optimization features while routing requests to 160+ models through a single API.
Latency Benchmarks: The Real Numbers
Industry benchmarks recommend keeping API response times under 100ms for optimal user experience. But how do gateways and direct calls actually perform?
Gateway Performance
Recent benchmarks show impressive results for AI-optimized gateways:
Adastra's LLM Gateway achieved ~80ms average latency, beating the 100ms industry target
AI-first gateways like TrustGate processed ~19,758 requests/second with just 2.5ms average latency on high-performance hardware
Traditional API gateways (Kong, Tyk, KrakenD) showed slightly higher latency at 2.8–5.3ms
The key insight? Purpose-built LLM gateways add minimal overhead—typically just 2-20ms—while providing substantial benefits. Requesty's smart routing automatically selects the fastest available model for each request, often resulting in better overall performance than direct calls to a single provider.
Direct API Call Performance
Direct calls offer the theoretical minimum latency since there's no intermediary processing. However, this advantage comes with limitations:
No automatic failover when providers experience downtime
No intelligent routing to faster models
No built-in caching to eliminate redundant requests
Manual implementation required for each optimization
Azure OpenAI's documentation reveals that latency varies significantly based on model type, prompt size, and system load. Without a gateway's optimization features, you're at the mercy of your chosen provider's current performance.
Uptime and Reliability: Beyond Single Points of Failure
Uptime is where gateways truly shine. Modern LLM gateways with proper monitoring and load balancing can achieve 99.9%+ uptime through several mechanisms:
Gateway Reliability Features
Automatic Failover: When one provider experiences issues, traffic instantly routes to backup models
Load Balancing: Distributes requests across multiple endpoints to prevent overload
Regional Deployments: Edge deployments can reduce latency by 40-70% while improving availability
Health Monitoring: Proactive detection and rerouting around degraded endpoints
Requesty's routing optimizations include automatic failover policies that ensure your AI applications stay online even when individual providers face outages. With support for 160+ models, there's always an alternative path for your requests.
Direct Call Limitations
When making direct API calls, your uptime is entirely dependent on your chosen provider's reliability:
No automatic failover during outages
Manual implementation required for redundancy
Limited visibility into provider health
No protection against rate limits or quota exhaustion
Performance Optimization Strategies
Whether you choose a gateway or direct calls, several optimization techniques can improve performance:
Caching
Gateways excel at intelligent caching. Requesty's auto-caching feature can reduce latency to near-zero for repeated requests while cutting costs by up to 80%. Implementing similar caching for direct calls requires custom development and infrastructure.
Streaming Responses
Both approaches can leverage streaming to reduce perceived latency. However, gateways can provide unified streaming interfaces across different providers, simplifying implementation.
Payload Optimization
Reducing prompt and response sizes improves performance regardless of your approach. Gateways can automatically optimize payloads and implement compression transparently.
Security and Governance Considerations
While not directly related to latency and uptime, security features can impact performance:
Gateway Security Benefits
Centralized authentication and authorization
Consistent content filtering and moderation
Guardrails for prompt injection protection
Audit logging and compliance features
These features add minimal latency (typically <5ms) while providing essential protection for production applications.
Direct Call Security Challenges
Implementing equivalent security for direct calls requires:
Custom authentication for each provider
Manual content filtering implementation
Individual logging and monitoring setup
Separate compliance measures for each endpoint
Real-World Use Cases and Recommendations
When to Use an LLM Gateway
LLM gateways like Requesty are ideal for:
Enterprise applications requiring governance and compliance
Multi-model deployments needing unified interfaces
Production systems where uptime is critical
Cost-conscious teams wanting to leverage caching and optimization
Applications using multiple AI features (chat, embeddings, image generation)
When Direct API Calls Make Sense
Consider direct calls for:
Proof-of-concept projects with minimal complexity
Single-model applications with extremely strict latency requirements
Isolated use cases where centralized management isn't needed
Development environments where simplicity trumps features
Making the Right Choice for Your Application
The benchmark data clearly shows that modern LLM gateways add minimal latency overhead while providing substantial benefits in reliability, security, and operational efficiency. For most production use cases, the 2-20ms gateway overhead is a small price to pay for:
99.9%+ uptime through intelligent failover
Up to 80% cost savings through caching and optimization
Centralized security and compliance
Unified access to 160+ models
Automatic performance optimization
Requesty's platform exemplifies these benefits, trusted by 15,000+ developers and teams at companies like Shopify and Microsoft. With features like smart routing, enterprise-grade security, and extensive integrations, you get the performance of direct calls with the reliability and features of an enterprise platform.
Conclusion
While direct API calls offer theoretical minimum latency, the real-world benefits of LLM gateways far outweigh the minimal performance overhead. Modern AI-first gateways deliver sub-100ms latency while providing critical features for production applications: automatic failover, intelligent routing, cost optimization, and centralized security.
For teams serious about building reliable, scalable AI applications, an LLM gateway isn't just a nice-to-have—it's essential infrastructure. The benchmarks prove that you don't have to sacrifice performance for reliability and features.
Ready to experience the benefits of intelligent LLM routing? Start with Requesty's quickstart guide and see how easy it is to improve your AI application's performance and reliability while reducing costs by up to 80%.