As the AI landscape rapidly evolves, understanding the terminology around LLM gateways has become essential for developers, architects, and business leaders. Whether you're building your first AI-powered application or optimizing an enterprise deployment, this comprehensive glossary will help you navigate the complex world of LLM infrastructure.
At Requesty, we've helped over 15,000 developers simplify their AI implementations while reducing costs by up to 80%. This glossary draws from our experience routing billions of requests across 160+ models to bring you the most relevant terms and concepts for 2025.
Core Concepts & Definitions
LLM Gateway
An LLM Gateway is an intermediary platform that manages, routes, and monitors API traffic between your applications and large language models. Think of it as the intelligent traffic controller for your AI infrastructure.
Key functions include:
API call routing across multiple models
Traffic throttling and rate limiting
Content filtering and security
Session tracking and analytics
Cost optimization and caching
Requesty's LLM routing solution exemplifies a modern gateway, providing unified access to 160+ models through a single API endpoint.
Foundation Models vs Fine-Tuned Models
Foundation Models (also called base models) are large, pre-trained AI models trained on massive datasets. These versatile models like GPT-4, Claude, and Llama-3 can handle a broad range of tasks out of the box.
Fine-Tuned Models take foundation models and further train them on domain-specific data. For example, a legal analysis model might be fine-tuned on case law, while a coding assistant might specialize in specific programming languages.
With Requesty's smart routing, you can automatically select the best model for each task, whether it's a general foundation model or a specialized fine-tuned variant.
Small Language Models (SLMs)
Not every task requires a massive model. Small Language Models are compact, efficient alternatives designed for specific tasks with lower resource requirements. They're perfect for edge computing, mobile applications, or high-volume simple tasks where cost and speed matter more than broad capabilities.
Traffic Control & Infrastructure
Model Routing
Model Routing dynamically directs requests to the appropriate model based on factors like:
Task requirements
Cost constraints
Performance needs
Availability and load
Requesty's routing optimizations include automatic failover, load balancing, and intelligent model selection to ensure your requests always reach the best available model.
Traffic Throttling
Traffic Throttling controls the rate of API calls to:
Manage costs effectively
Prioritize critical requests
Enforce usage policies
Prevent system overload
This is especially important when dealing with expensive models or during traffic spikes.
Content Filtering
Content Filtering screens both requests and responses to ensure compliance with ethical, legal, and organizational standards. Requesty's security features include built-in guardrails that automatically filter harmful content while maintaining performance.
Request & Response Processing
Understanding Tokens
Tokens are the fundamental units of text that LLMs process. Typically, one token equals about 4 characters or ¾ of a word. Understanding tokens is crucial because:
API pricing is usually per token
Context windows are measured in tokens
Response length limits are token-based
Tokenization is the process of breaking text into these tokens for processing.
Context Windows
The Context Window defines how much information an LLM can process in a single request. Larger windows enable more context but cost more and may increase latency. Modern models range from 4K tokens (older models) to 200K+ tokens (Claude 3, GPT-4 Turbo).
When using Requesty's API, you can leverage models with varying context windows and automatically route to the most cost-effective option for your needs.
Inference Parameters
Inference is the process of generating outputs from a model. Key parameters include:
Temperature: Controls randomness (0 = deterministic, 1+ = creative)
Top-K/Top-P: Limits word selection for diversity control
Max Tokens: Sets response length limits
Seed: Ensures reproducible outputs
Requesty's structured outputs feature helps you get consistent responses across different models by standardizing these parameters.
Retrieval-Augmented Generation (RAG)
What is RAG?
Retrieval-Augmented Generation enhances LLMs with external knowledge sources, improving accuracy and grounding responses in verifiable data. Instead of relying solely on training data, RAG systems:
Search relevant documents
Extract pertinent information
Augment prompts with retrieved context
Generate responses with source attribution
This approach significantly reduces hallucinations and improves factual accuracy.
Key RAG Components
Embeddings: Numerical representations of text for semantic search
Vector Databases: Store and retrieve embeddings efficiently
Semantic Search: Matches queries by meaning, not just keywords
Re-ranking: Prioritizes retrieved documents by relevance
Response Attribution: Provides source references for transparency
Security, Safety & Compliance
Guardrails
Guardrails are policies and technical measures preventing harmful, biased, or inappropriate outputs. Modern guardrails include:
Prompt injection detection
PII redaction
Content moderation
Output validation
Requesty's guardrails automatically protect your applications without requiring complex configuration.
Hallucination Prevention
Hallucinations occur when LLMs generate plausible-sounding but incorrect information. Mitigation strategies include:
Using RAG for fact-grounding
Implementing verification layers
Setting appropriate temperature values
Using specialized models for factual tasks
Data Security
Modern LLM gateways must ensure:
Encryption in transit and at rest
Access Control with granular permissions
Data Segregation for multi-tenant environments
Compliance with regulations like GDPR and HIPAA
Requesty's enterprise features include SSO integration, role-based access control, and comprehensive audit logging.
Development & Operations
LLM DevOps
LLM DevOps integrates development and operations for efficient model deployment. Key practices include:
Continuous monitoring of model performance
Automated failover and scaling
Cost optimization through caching
A/B testing different models
Requesty's platform provides built-in DevOps tools including real-time analytics, automatic caching, and intelligent failover policies.
Analytics & Monitoring
Essential metrics for LLM operations:
Token usage and cost tracking
Latency and response times
Error rates and failure patterns
Model performance comparisons
These insights help optimize your AI infrastructure for both performance and cost.
2025 Trends & Developments
Agentic Systems
AI Agents that autonomously plan, reason, and execute tasks are becoming mainstream. These systems often use LLM gateways for:
Orchestrating multiple model calls
Managing conversation memory
Coordinating tool usage
Ensuring consistent behavior
Requesty's integrations with tools like Cline and Roo Code demonstrate how modern agents leverage gateway infrastructure.
Energy Efficiency
With growing environmental concerns, the industry is shifting toward:
Smaller, more efficient models
Optimized inference techniques
Smart routing to minimize compute
Caching to reduce redundant processing
Requesty's auto-caching feature can reduce token usage by up to 80% for repetitive queries.
Open vs Proprietary Models
The ecosystem now includes:
Open Models: Llama, Mistral, Qwen (self-hostable, customizable)
Proprietary APIs: OpenAI, Anthropic, Google (managed, cutting-edge)
Hybrid Deployments: Combining both for optimal results
Requesty supports all major models, letting you seamlessly switch between open and proprietary options.
Practical Implementation Tips
Getting Started
1. Define Your Use Case: Understand whether you need general intelligence or specialized capabilities 2. Set Up Routing: Configure primary and fallback models for reliability 3. Implement Caching: Reduce costs for repetitive queries 4. Add Guardrails: Protect against harmful outputs and ensure compliance 5. Monitor Performance: Track costs, latency, and quality metrics
Cost Optimization Strategies
Use smaller models for simple tasks
Implement aggressive caching for common queries
Set up smart routing to automatically select cost-effective models
Monitor token usage and set spending limits
Leverage batch processing where possible
Requesty's smart routing automatically implements these strategies, reducing costs while maintaining quality.
Conclusion
Understanding LLM gateway terminology is crucial for building robust, scalable AI applications in 2025. From basic concepts like tokens and inference to advanced techniques like RAG and agentic systems, this glossary provides the foundation for navigating the modern AI landscape.
As you implement these concepts, remember that a good LLM gateway should simplify complexity, not add to it. Requesty handles the intricate details of model routing, caching, security, and optimization, letting you focus on building great applications.
Ready to put this knowledge into practice? Start with Requesty's quickstart guide and join over 15,000 developers who are already saving up to 80% on their LLM costs while improving reliability and performance.
For questions or to discuss your specific use case, join our Discord community or reach out to our team. The future of AI is unified, secure, and efficient – and it starts with understanding the fundamentals.