I do not train foundation models. I use models someone else already trained, then I spend my time on the product around them.
That is the default for almost every AI feature I have shipped.
Why I start here
Benefits of pre-trained models:
- You can demo a feature this week, not after a research project
- Quality on language, code, and images is already high
- You pay for inference, not for a GPU cluster and a data team
- You can swap providers if price, latency, or policy changes
Where they disappoint
Limitations and considerations I check before I commit:
- The model does not know your private data
- It will sound sure when it is wrong
- Context length is finite. More tokens cost more and often answer worse
- Cut-off dates / knowledge mean anything after training is invisible unless you retrieve it
- Pricing, rate limits, and data-retention rules differ by vendor
- A model that is great at chat can be bad at strict JSON
Popular models I actually compare
I keep a short list and re-test when a feature is expensive or flaky:
- OpenAI models — strong default for chat, tools, vision, and image generation. The API surface is what a lot of tutorials assume.
- Anthropic's Claude — I reach for it when I want careful long-form writing or a large context that still follows instructions.
- Google's Gemini — worth a bake-off when the product already lives in Google Cloud, or when multimodal is the whole feature.
- Mistral and Cohere — useful when I want a smaller, cheaper, or more specialized model, including embeddings from Cohere.
- Hugging Face models — where I look when I might self-host instead of calling a closed API.
- Replicate — a way to run open models without standing up GPUs myself.
- Azure AI and AWS SageMaker — what I use when the company already mandates that cloud, or when procurement will not approve a direct OpenAI key.
There is no permanent winner. I pick on a golden set for this feature: quality, latency, and dollars per successful task.
Capabilities I write down before I choose
For each candidate I note:
- Capabilities — chat, tools, vision, JSON mode, embeddings
- Context length — input plus output, not the marketing number alone
- Knowledge cutoff — and whether the product needs live facts anyway
If two models tie on quality, I take the one with clearer pricing and a fallback I can switch to in an afternoon.
Next
The API most tutorials start with, and the token math behind it: