I'm building a customer support assistant, which LLM API should I use

For a customer support assistant, I’d generally recommend using an API with:

• Strong instruction following for consistent policy adherence
• Low latency for fast replies
• Good tool/function calling for CRM, ticketing, order lookup, etc.
• Low cost at scale
• Strong safety controls for customer-facing use

Best default choice

If you’re starting from scratch, a strong general-purpose option is:

• OpenAI API — good quality, tool use, structured outputs, and broad ecosystem support.

Good alternatives depending on your needs

• Anthropic API — often chosen for natural dialogue quality and safety-oriented assistant behavior.
• Google Gemini API — worth considering if you already use Google Cloud or need multimodal capabilities.
• AWS Bedrock — useful if you want to compare multiple models behind one AWS-native interface.
• Azure OpenAI — best if you’re already in Microsoft/Azure enterprise infrastructure.

How to choose

Pick based on:

1. Answer quality
   Test with your actual support tickets and edge cases.

2. Tool calling / workflow integration
   Can it reliably fetch order status, reset passwords, create tickets, etc.?

3. Latency and cost
   Support bots need to be fast and affordable.

4. Safety / compliance
   Look for data retention controls, SOC 2 / ISO, PII handling, regional hosting if needed.

5. RAG support
   If the bot needs to answer from docs/KB articles, make sure the model works well with retrieval.

Practical recommendation

A common production setup is:

• LLM: OpenAI or Anthropic
• Knowledge base: RAG over help docs + policies
• Tools: CRM, order system, ticketing system
• Fallback: human handoff when confidence is low

If you want a short answer

• Start with OpenAI API if you want the easiest strong baseline.
• Evaluate Anthropic if your top priority is conversational quality and safety.
• Use Azure OpenAI or Bedrock if enterprise procurement/infrastructure matters most.

If you want, I can also give you a side-by-side comparison for customer support use cases or recommend a specific model + architecture based on your budget, volume, and compliance requirements.