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.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
