API Managers for Managing LLM APIs
Access our ebook on how to manage LLM APIs using the functionalities of the leading API Managers on the market
Which LLM APIs exist and how are they managed in API Managers?
Nowadays, language models (LLMs) are mainly accessed via REST or streaming APIs (SSE), in chat or completion formats. Providers such as OpenAI, Anthropic, Azure OpenAI, Amazon Bedrock, Mistral, and Hugging Face offer similar interfaces, but each has particularities in token limits, costs, moderation, and observability.
This is where API Managers (such as Kong Gateway or WSO2) come in, acting as a layer for governance, security, and standardisation. A good API Manager allows you to:
Abstract and unify different providers into a single endpoint.
Apply rate limiting based on tokens and costs.
Incorporate content moderation policies with semantic filters.
Manage prompt templates and enrich context without modifying end clients.
Add advanced observability to monitor latency, usage, and costs.
What does this ebook include?
- Proxy and standardisation: how to unify OpenAI, Anthropic, Azure, Mistral, and more under a single API.
- Advanced rate limiting: real-time control of consumption by tokens and costs.
- Content moderation: from basic filters to semantic analysis and Azure security services.
- Prompt engineering: templates and decorators managed within the API Manager.
- Observability and metrics: how to monitor latency, requests, costs, and usage in Grafana.
- Semantic caching: reducing costs and latency for repetitive requests.
- Technical comparison between Kong Gateway and WSO2.
Download our manual for free
Fill in your details and we will redirect you to the ebook on managing LLM APIs in API Managers
Frequently asked questions about managing LLM APIs in API Managers
Because LLMs have context limits and variable costs per token. An API Manager allows the application of rate limiting, observability, and moderation, preventing overcharges and security risks.
Plugins such as AI Rate Limiting Advanced in Kong allow quotas to be configured based on tokens or monetary costs, returning headers to the client with the remaining usage.
It allows the definition of centralized templates and the dynamic enrichment of context without modifying clients. This makes it easier to maintain consistency and evolve prompts without deploying changes to each application.
Through semantic caching, where a request with a meaning similar to a previously answered one reuses the stored response (e.g., in Redis), reducing costs and latency.
Basic moderation filters using regex or keywords, whereas semantic moderation uses embedding similarity to detect prohibited intents (violence, PII, etc.), making it more robust against adversarial prompt engineering.
OVER 10 YEARS OF EXPERIENCE
The top experts at CloudAPPi
Our ebook on managing LLM APIs has been created by our top experts in API Managers and AI technologies. This way, we provide you with exclusive, practical content with the guarantee of being completely reliable.
