llm-what-to-use-when

name	llm-what-to-use-when
description	Practical decision guide for choosing between Vercel AI SDK, LiteLLM, LightLLM, and direct provider SDKs based on runtime, deployment model, and constraints. Use when asked which LLM tooling to use for JS/TS apps, Python apps, static SPAs, or self-hosted inference.

LLM Inference: What To Use When

A practical guide for choosing the right library or approach depending on your runtime, use case, and constraints.

When to use this skill

Use this skill when you need to choose an LLM integration approach based on:

• Runtime and language (JS/TS vs Python)
• Hosting model (browser-only static site, backend app, or self-hosted inference)
• Portability needs (single provider vs multi-provider abstraction)
• Operational constraints (key handling, CORS, and gateway architecture)

---

Quick Decision Chart

Your situation	Use this
Python app, any provider	LiteLLM
JS/TS app with a backend (Node, Next.js, etc.)	Vercel AI SDK
Static SPA (GitHub Pages, no server) + npm build	Vercel AI SDK (direct browser mode)
Static SPA + no build step, vanilla JS	Provider SDK direct (e.g. openai via CDN)
Hosting/serving your own open-source model	LightLLM (inference server)
Both Python app and SPA, unified gateway	LiteLLM proxy + call it from both

---

Option 1: Vercel AI SDK (ai)

Best for: npm-bundled SPAs, React/Vue/Svelte apps, Next.js, static sites on GitHub Pages.

Licence: MIT ✅
npm: npm install ai @ai-sdk/openai @ai-sdk/anthropic @ai-sdk/google

Strengths

• Multi-provider with a unified API — swap providers by changing one import
• First-class tool use and structured output (via Zod schemas with generateObject)
• Agentic loops via maxSteps in generateText
• Works in the browser with no backend required (direct provider calls)
• MCP (Model Context Protocol) support in AI SDK 6+
• Type-safe throughout

Browser / Static Site Usage

For GitHub Pages or any static host, call providers directly from the browser. The user supplies their own API key (stored in memory only, never hardcoded).

import { generateText } from 'ai'
import { createOpenAI } from '@ai-sdk/openai'
import { z } from 'zod'

const openai = createOpenAI({ apiKey: userSuppliedKey })

const { text, toolResults } = await generateText({
  model: openai('gpt-4o-mini'),
  tools: {
    rollDice: {
      description: 'Roll a die with N sides',
      parameters: z.object({ sides: z.number() }),
      execute: async ({ sides }) => Math.ceil(Math.random() * sides)
    }
  },
  maxSteps: 5,
  prompt: 'Roll a d20 and tell me if I hit AC 15'
})

Swapping to Anthropic or Gemini only changes the import and model string — all tool/structured output code stays identical.

Provider CORS Notes

Provider	Direct browser calls	Notes
OpenAI	✅ Works	Most permissive
Google Gemini	✅ Works	Good CORS support
Anthropic	⚠️ Requires flag	Pass dangerouslyAllowBrowser: true to createAnthropic()
Ollama (local)	✅ Works	localhost only; not useful for deployed static sites

Structured Output Example

import { generateObject } from 'ai'
import { z } from 'zod'

const { object } = await generateObject({
  model: openai('gpt-4o-mini'),
  schema: z.object({
    enemyName: z.string(),
    hp: z.number(),
    abilities: z.array(z.string())
  }),
  prompt: 'Generate a fantasy RPG enemy for a level 5 party'
})
// object.enemyName, object.hp, object.abilities are all typed

Key Caveat for Static Sites

API keys in client-side code are visible to anyone who opens DevTools. The safest UX pattern is:

• Prompt the user to enter their own API key on first use
• Store it in sessionStorage or in-memory only (never localStorage for sensitive keys)
• Never commit a key to your repo

---

Option 2: LiteLLM (Python)

Best for: Python scripts, FastAPI/Flask backends, data pipelines, CLI tools.

Licence: MIT ✅
Install: pip install litellm

Strengths

• Unified completion() call across 100+ providers (OpenAI, Anthropic, Gemini, Mistral, Bedrock, Ollama, etc.)
• Drop-in OpenAI SDK replacement
• Tool use and structured output supported
• Can run as a proxy server (OpenAI-compatible REST API) — useful for serving SPAs too

Basic Usage

from litellm import completion

response = completion(
    model="gpt-4o-mini",          # or "claude-3-haiku", "gemini/gemini-1.5-flash", etc.
    messages=[{"role": "user", "content": "Hello!"}],
    api_key=os.environ["OPENAI_API_KEY"]
)
print(response.choices[0].message.content)

LiteLLM as a Proxy (Unified Gateway)

Run once, call from anywhere — Python apps, SPAs, curl:

litellm --model gpt-4o-mini --port 8000

Your SPA can then call http://localhost:8000/v1/chat/completions with OpenAI-format JSON. In production, deploy to a cheap VPS/container.

---

Option 3: LightLLM (Self-Hosted Inference)

Best for: Hosting your own open-source models (LLaMA, Mistral, Qwen, etc.) on GPU hardware.

Licence: Apache 2.0
Not for: Calling external APIs like OpenAI or Anthropic

LightLLM is an inference server, not a client SDK. You'd use it if you're running models yourself and want a fast, production-ready serving layer. Your app would then call it via HTTP, and you'd use LiteLLM or the Vercel AI SDK to talk to it.

---

Option 4: Direct Provider SDKs

Best for: Simple use cases, no dependency on a wrapper library, vanilla JS with a CDN import.

Provider	JS package	Python package
OpenAI	openai	openai
Anthropic	@anthropic-ai/sdk	anthropic
Google Gemini	@google/generative-ai	google-generativeai

Each has tool use and structured output but in their own format. You lose portability — switching providers means rewriting call sites.

---

Scenarios: Your Specific Stack

Idle game SPA on timeless.github.io/idlegames

Recommended: Vercel AI SDK in direct browser mode.

npm install ai @ai-sdk/openai zod

• User pastes their OpenAI (or Gemini) key into a settings panel
• Call generateText or generateObject directly in JS
• Tools let the LLM interact with game state (check resources, trigger events, etc.)
• Structured output means you get typed JSON back, not text to parse

Python utility / automation script

Recommended: LiteLLM.

pip install litellm

• Same completion() call regardless of provider
• Easy to swap models for cost/speed tuning
• Works well in async contexts with acompletion()

You want the same provider/model logic in both

Option A: Use LiteLLM as a proxy server, call it from both Python (directly) and the SPA (via HTTP). One config, all providers.

Option B: Accept that the Python and JS codebases use different libraries (LiteLLM and AI SDK respectively) but follow the same conceptual pattern — both support tools and structured output with near-identical mental models.

---

Summary

Library	Runtime	Multi-provider	Tools	Structured output	MIT licence	Browser-native
Vercel AI SDK	JS/TS	✅	✅	✅ (Zod)	✅	✅
LiteLLM	Python	✅	✅	✅	✅	❌
LightLLM	Python (server)	❌ (self-host only)	—	—	Apache 2.0	❌
openai (direct)	JS + Python	❌	✅	✅	MIT	⚠️
anthropic (direct)	JS + Python	❌	✅	✅	MIT	⚠️

Bottom line for your projects:

• Static SPAs on GitHub Pages → Vercel AI SDK, direct browser mode, user-supplied key
• Python apps → LiteLLM
• Want one gateway for both → LiteLLM proxy + call it from everywhere