LLM API
Definition
An LLM API (Large Language Model Application Programming Interface) is a hosted service that exposes LLM capabilities through standard HTTP endpoints, typically RESTful with JSON request/response formats. Developers send a prompt (and optional parameters like temperature, max_tokens, and model selection) to the API and receive a generated response. Major providers include OpenAI (GPT-4o, GPT-4o-mini), Anthropic (Claude 3.5 Sonnet, Claude 3 Haiku), Google (Gemini 1.5 Pro, Gemini Flash), and Mistral. LLM APIs handle model serving infrastructure, scaling, and maintenance—enabling teams to build AI applications without ML ops expertise. Pricing is usage-based: per input token and per output token.
Why It Matters
LLM APIs are the primary way application developers access AI capabilities. Building and hosting your own LLM requires substantial ML infrastructure expertise and capital; APIs provide instant access to frontier model quality with per-query pricing and zero infrastructure investment. For 99helpers customers, LLM APIs are the backbone of AI chatbot features: the platform calls an LLM API for each user message, passing the retrieved knowledge base context alongside the user's question. Understanding API pricing, rate limits, and model selection helps teams optimize costs and maintain quality at scale.
How It Works
A standard LLM API call (OpenAI-compatible format): POST /v1/chat/completions with JSON body: {model: 'gpt-4o', messages: [{role: 'system', content: 'You are a support assistant.'}, {role: 'user', content: 'How do I reset my password?'}], temperature: 0.3, max_tokens: 500}. The response includes: choices[0].message.content (the generated text), usage.prompt_tokens, usage.completion_tokens (for billing), finish_reason ('stop', 'length', or 'content_filter'). Streaming responses (stream: true) return tokens as server-sent events, enabling progressive display in the UI. Most providers offer an OpenAI-compatible API, enabling drop-in provider switching.
LLM API — Request/Response Flow
Client
POST /v1/chat/completions
model: gpt-4o
messages: [...]
temperature: 0.7
max_tokens: 500
Authorization: Bearer sk-…
API
Auth
Rate limit
Route
Response
200 OK
choices[0].message
usage.prompt_tokens: 312
usage.completion_tokens: 148
finish_reason: "stop"
Rate limits
Cost model (example)
Streaming mode (stream: true) returns server-sent events — one chunk per token — enabling the UI to display output incrementally.
Real-World Example
A 99helpers platform integration uses the Anthropic API: const response = await anthropic.messages.create({model: 'claude-3-5-sonnet-20241022', max_tokens: 1024, system: systemPrompt, messages: [{role: 'user', content: userMessage}]}). The response is streamed to the user interface for immediate feedback while the full response completes. API usage tracking shows 2.1M input tokens and 450K output tokens per day across all customers. At Claude 3.5 Sonnet pricing ($0.003/1K input, $0.015/1K output), this costs $6.30 + $6.75 = $13.05/day. Switching 40% of queries to Claude 3 Haiku ($0.00025/$0.00125 per 1K tokens) for simple factual queries saves $8/day ($2,920/year).
Common Mistakes
- ✕Hard-coding a single LLM provider without an abstraction layer—switching providers later requires touching every API call in the codebase.
- ✕Ignoring rate limits until hitting them in production—LLM APIs have requests-per-minute and tokens-per-minute limits that require retry logic and queue management.
- ✕Not implementing streaming for user-facing features—users experience much better UX when tokens appear progressively rather than waiting for the full response.
Related Terms
Large Language Model (LLM)
A large language model is a neural network trained on vast amounts of text that learns to predict and generate human-like text, enabling tasks like answering questions, writing, translation, and code generation.
LLM Inference
LLM inference is the process of running a trained model to generate a response for a given input, encompassing the forward pass computation, token generation, and the infrastructure required to serve predictions at scale.
Token
A token is the basic unit of text an LLM processes—roughly 4 characters or 3/4 of an English word. LLM APIs charge per token, context windows are measured in tokens, and generation speed is measured in tokens per second.
LLM Router
An LLM router dynamically selects which language model to use for each query based on complexity, cost requirements, or domain, routing simple queries to cheaper models and complex queries to more capable ones.
Model Provider
A model provider is a company that trains and serves large language models through APIs—including OpenAI, Anthropic, Google, Mistral, and Meta—offering different models with varying capability, cost, and privacy characteristics.
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