LLM Gateway 101: Everything You Need to Know in 2025

The AI landscape has exploded. With hundreds of LLM providers and thousands of models available, managing AI infrastructure has become a complex challenge for developers and enterprises alike. Enter the LLM gateway – the critical infrastructure layer that's transforming how we build, scale, and manage AI applications in 2025.

Whether you're a startup experimenting with AI or an enterprise running mission-critical LLM workloads, understanding LLM gateways is no longer optional – it's essential. This comprehensive guide will walk you through everything you need to know about LLM gateways, from basic concepts to advanced features, helping you make informed decisions for your AI infrastructure.

What is an LLM Gateway?

An LLM gateway (also called an LLM router or AI gateway) is an infrastructure layer that sits between your applications and multiple large language model providers. Think of it as a smart traffic controller for your AI requests – abstracting away provider-specific APIs, managing traffic intelligently, and providing a unified interface to access hundreds of models.

At its core, an LLM gateway solves several critical problems:

  • Vendor Lock-in: Without a gateway, switching between providers like OpenAI, Anthropic, or Google requires code changes and potential downtime

  • Reliability Issues: When a provider experiences an outage, your application goes down with it

  • Cost Overruns: Different models have vastly different pricing, and without intelligent routing, you might be overspending significantly

  • Security Gaps: Managing authentication, authorization, and compliance across multiple providers becomes a nightmare

  • Observability Challenges: Tracking usage, costs, and performance across providers requires custom tooling

Requesty addresses all these challenges by providing a unified gateway to 160+ models, including the latest Claude 4, DeepSeek R1, and GPT-4o, with intelligent routing that can reduce costs by up to 80%.

Core Features Every LLM Gateway Should Have

Unified API Interface

The foundation of any LLM gateway is its ability to provide a single, consistent API across all providers. This means you can switch from GPT-4 to Claude 4 or any other model without changing a single line of code. Requesty's LLM routing makes this seamless with OpenAI-compatible endpoints that work with your existing code.

Intelligent Routing and Load Balancing

Modern gateways don't just pass requests through – they make intelligent decisions about where to route each request based on:

  • Cost optimization

  • Latency requirements

  • Model performance for specific tasks

  • Provider availability

  • Regional considerations

Smart routing capabilities can automatically select the best model for each task, ensuring optimal performance while minimizing costs.

Automatic Failover and Redundancy

Provider outages are inevitable. When OpenAI goes down (and it will), your application shouldn't. LLM gateways provide automatic failover to alternative providers, ensuring your AI features remain operational. Requesty's routing optimizations include sophisticated failover policies that can chain multiple models as fallbacks.

Caching for Cost and Performance

Caching is one of the most underutilized features in AI applications. Advanced gateways offer:

  • Semantic Caching: Cache responses based on meaning, not just exact matches

  • In-Memory Caching: Ultra-fast response times for frequently requested data

  • Distributed Caching: Share cache across multiple instances

With proper caching, you can reduce API calls (and costs) by 50-80% for many use cases.

Security and Compliance

As AI adoption grows, so do security concerns. Essential security features include:

  • Prompt injection protection

  • Content filtering and guardrails

  • SOC2, HIPAA, and GDPR compliance

  • Audit trails for every interaction

  • Role-based access control

Requesty's security features include comprehensive guardrails and compliance tools that protect your applications and users.

Advanced Capabilities for 2025

Prompt Management and Optimization

Managing prompts across multiple models and versions is challenging. Modern gateways provide:

  • Centralized prompt libraries

  • Version control for prompts

  • A/B testing capabilities

  • Automatic prompt optimization

Requesty's prompt library lets you manage system prompts and configurations in one place, with optimization features that improve performance across different models.

Team and Budget Management

Enterprise teams need granular control over AI spending. Look for features like:

  • User-specific budgets and limits

  • Department-level cost allocation

  • Virtual API keys for different teams

  • Real-time spend tracking

Enterprise features like SSO integration and role-based budgets ensure your organization maintains control over AI usage and costs.

Observability and Analytics

You can't optimize what you can't measure. Essential observability features include:

  • Real-time dashboards

  • Cost tracking by user, team, and project

  • Latency and error metrics

  • Integration with monitoring tools

  • Custom metadata for detailed analytics

Regional Routing and Data Residency

For global applications and compliance requirements, gateways should support:

  • Routing to the nearest provider region

  • Data residency controls

  • Compliance with local regulations

  • Optimized global performance

Comparing Leading LLM Gateway Solutions

The LLM gateway landscape in 2025 offers various options, each with unique strengths:

Open-Source Solutions:

  • LiteLLM: Highly customizable, great for engineering teams building custom infrastructure

  • Helicone: Fast performance with Rust, strong observability features

Managed Solutions:

  • OpenRouter: User-friendly with passthrough billing, good for prototyping

  • Portkey: Strong enterprise features and compliance tools

  • Requesty: Comprehensive routing to 160+ models, advanced optimization features, trusted by 15k+ developers

When evaluating gateways, consider:

  • Number of supported models and providers

  • Setup complexity and time to value

  • Performance and reliability features

  • Security and compliance capabilities

  • Cost structure and pricing transparency

Real-World Use Cases and Applications

Customer Support Automation

Imagine a customer support chatbot that needs to handle everything from simple FAQs to complex technical issues. With an LLM gateway, you can:

  • Route simple queries to cost-effective models

  • Escalate complex issues to more capable (but expensive) models

  • Implement fallbacks for high availability

  • Cache common responses for instant replies

Development and Coding Assistants

Requesty's integrations with tools like VS Code and Roo Code enable developers to switch between models instantly while coding, choosing the best model for each task.

RAG (Retrieval-Augmented Generation) Pipelines

For applications using RAG:

  • Cache embedding generations

  • Route retrieval and generation to different models

  • Implement stepwise evaluation

  • Optimize costs while maintaining quality

Multi-Tenant SaaS Platforms

SaaS providers offering AI features need:

  • Per-customer usage tracking

  • Flexible model access controls

  • Cost allocation and billing

  • Compliance with various regulations

Evaluation, Monitoring, and Governance Best Practices

Continuous Evaluation

Successful AI applications require ongoing evaluation:

  • Offline Evaluation: Test with curated datasets before deployment

  • Online Evaluation: Monitor real-world performance continuously

  • A/B Testing: Compare models and prompts in production

  • User Feedback: Integrate human feedback loops

Governance and Compliance

As AI regulations evolve (EU AI Act, industry-specific requirements), gateways become the enforcement point for:

  • Authentication and authorization

  • Data residency requirements

  • Audit trail maintenance

  • Policy enforcement

Operational Excellence

Best practices for running LLM gateways include:

  • Declarative, version-controlled configurations

  • Automated deployment pipelines

  • Comprehensive monitoring and alerting

  • Regular security audits

  • Disaster recovery planning

Getting Started: Practical Implementation Tips

Start Simple, Scale Smart

Begin with basic routing and failover, then add advanced features as needed:

1. Week 1: Set up basic routing with Requesty's quickstart guide 2. Week 2: Implement caching for your most common requests 3. Week 3: Add failover policies for critical endpoints 4. Week 4: Enable cost tracking and set up budgets

Choose the Right Deployment Model

Consider your requirements:

  • Managed Service: Fastest time to value, minimal operational overhead

  • Self-Hosted: Maximum control, data stays in your infrastructure

  • Hybrid: Critical data self-hosted, non-sensitive through managed service

Integration Best Practices

  • Use OpenAI-compatible SDKs for easy migration

  • Implement proper error handling for failovers

  • Add request metadata for better analytics

  • Test failover scenarios regularly

  • Monitor costs and performance from day one

Future Trends and Considerations

The Expanding Model Ecosystem

With new models launching weekly, gateways become even more critical for:

  • Rapid model evaluation and adoption

  • Cost optimization across providers

  • Managing model deprecations

  • Leveraging specialized models

Environmental Impact

As AI's carbon footprint grows, intelligent routing and caching help:

  • Reduce unnecessary API calls

  • Route to energy-efficient providers

  • Optimize resource usage

  • Track and report environmental impact

Integration with Existing Infrastructure

Modern API gateways are evolving to support LLM traffic natively, with features like semantic caching and prompt management becoming standard.

Conclusion: Why LLM Gateways Are Essential in 2025

LLM gateways have evolved from a nice-to-have to mission-critical infrastructure. They provide the reliability, security, cost control, and agility needed to run AI applications at scale. As the AI ecosystem becomes more complex and regulated, having a robust gateway strategy is foundational for success.

Whether you're building your first AI application or scaling to millions of users, the right LLM gateway can make the difference between success and failure. With Requesty, you get access to 160+ models through a unified API, with intelligent routing that can reduce costs by up to 80% while improving reliability and performance.

Ready to optimize your AI infrastructure? Sign up for Requesty and join 15k+ developers who are building smarter, more efficient AI applications. With features like smart routing, enterprise-grade security, and comprehensive integrations, you'll have everything you need to succeed in the AI era.

The future of AI is multi-model, and with the right gateway, you're ready for whatever comes next.