Choosing an LLM provider: a decision framework

New frontier models ship every few weeks now. Chasing the newest one is a losing game unless your architecture makes switching free.

Put the provider behind one interface

In every NextUpgrad app, the LLM sits behind one port, and swapping Groq for Anthropic or OpenAI is a config change, not a rewrite. When a new model ships, changing one line and rerunning the evals is the entire migration.

What actually differs between providers

• Latency: Groq's inference speed matters when a user is waiting on a streamed answer.
• Context window: matters for document-heavy tasks like OCR chat, less for short classification.
• Cost per token: matters at scale, less for a low-traffic student project.
• Tool-use reliability: matters enormously for agentic workflows, barely at all for single-turn Q&A.

The evaluation that actually predicts your bill

A public benchmark tells you who won an exam. Your own evals, on your own documents and your own budget, tell you who you'd actually hand production traffic to.