What AI Models to Use? Choosing the Right AI Model for Your Needs

Whether you’re building chatbots, automating workflows, or developing AI-driven apps, the right AI model can make a huge difference in performance, security, and cost.

This post is a practical guide. It keeps the language simple, but it doesn’t dodge the real trade-offs.

If you want one “north star”: pick the model that makes your system reliable and affordable, not the model that wins a single benchmark.

If you’re new to evaluation, start here: OpenAI evals getting started.

A quick rule of thumb (what to optimize for)

Most teams should choose models by answering these questions in order:

1. What is the job? (chat, extraction, classification, code, vision, speech)
2. What matters most? (latency, cost, reasoning quality, safety, multilingual, tool use)
3. Where will it run? (cloud API, your VPC, on-device)
4. What data can it see? (PII, IP, regulated data)
5. How will you measure success? (eval set + online metrics)

Comparing model families (a practical table)

This table is intentionally “high level”. Providers change names and pricing often, but the decision logic stays stable.

If you want a directory of many models: MetaSchool’s AI Models Directory.

Which one should you use? (simple decision flow)

Here’s the simple breakdown:

• If you’re shipping a product next week: start with a hosted frontier model (fastest path).
• If you’re dealing with sensitive data / strict rules: use a managed enterprise setup or open models in your own infra.
• If cost/latency is the bottleneck: use smaller models, routing, caching, and distillation.

Considerations When Picking an AI Model

Data Privacy

When choosing an AI model, data privacy should be a key factor. If you’re working with sensitive or proprietary data, you might prefer an open-source, self-hosted model like LLaMA or Mistral to ensure full control over data.

Cloud-based models like OpenAI’s GPT-4 or AWS Bedrock handle data differently, often with retention policies or logging mechanisms, so be sure to review their documentation and terms before implementation.

Foundation models, fine-tuning, and where “distilled models” fit

Foundation models (what they are)

A foundation model is trained on huge datasets to learn general language/vision patterns. It’s not trained for your specific company task yet—it’s a general engine.

Most LLMs start with self-supervised learning:

• predict the next token
• minimize cross-entropy loss
• optimize with gradient descent variants (often Adam/AdamW)

Distilled models (what they are and why they matter)

Knowledge distillation is “teacher → student” training:

• a large model (teacher) generates targets (answers, probabilities, reasoning traces)
• a smaller model (student) learns to imitate those targets

Why teams like distilled models:

• lower latency
• lower cost
• easier to run in private infrastructure

A canonical example is DistilBERT (a distilled version of BERT): https://huggingface.co/docs/transformers/model_doc/distilbert

Fine-tuning (how models become “yours”)

There are two common paths:

• Full fine-tuning: update all weights (expensive, heavy ops).
• Parameter-efficient fine-tuning (PEFT): update a small set of parameters (e.g., LoRA) and keep the base model mostly frozen. Docs: https://huggingface.co/docs/peft/index

In practice, many teams combine:

• a general foundation model
• a small, task-tuned model for routine jobs
• a routing layer to decide which model runs each request

How Generative AI / LLMs relate to “regular” machine learning

LLMs are still ML. They just operate at a huge scale and use a particular architecture.

Think of it like this:

• ML is the field (supervised, unsupervised, RL, probabilistic models, etc.)
• Deep learning is a subset (neural networks trained with backprop)
• LLMs are a subset (large transformer-based models trained mostly with self-supervised learning)

LLMs show up in ML systems the same way other models do:

• data → training → evaluation → deployment → monitoring → iteration

The core ideas and algorithms behind modern LLMs (high level)

If you want a mental model without a math wall:

• Transformer: the architecture (attention + feed-forward layers).
• Attention: “which tokens matter most right now?”
• Tokenization (BPE-like): how text becomes token IDs.
• Training objective: next-token prediction (most of the time).
• Decoding algorithms: greedy, beam search, sampling with temperature/top-p.
• Alignment: extra training steps to better match human preferences (often SFT + preference optimization like RLHF/DPO-style approaches).

Other AI Models to Explore

This is not a complete list, as the AI landscape is constantly evolving. Here are some additional AI models worth considering:

Learning resources (courses + videos)

I’m linking instead of embedding to avoid YouTube embed errors.

Keep exploring (official docs + hands-on)

• OpenAI models docs: https://platform.openai.com/docs/models
• Anthropic Claude docs: https://docs.anthropic.com/
• Google Gemini API docs: https://ai.google.dev/
• Meta Llama: https://ai.meta.com/llama/
• Mistral docs: https://docs.mistral.ai/
• Hugging Face Transformers: https://huggingface.co/docs/transformers/index
• Hugging Face PEFT (LoRA, etc.): https://huggingface.co/docs/peft/index

Choosing the right model is an iterative process. Start simple, measure, then add sophistication: routing, caching, distillation, and fine-tuning only when you can prove the value.