Gemma vs LLaMA

Gemma

Gemma is a good starting point when you want an open model you can run locally or fine-tune, with good tooling support and permissive licensing in the latest generation. Pick the size to match your hardware, and treat the small variants as edge models rather than replacements for large hosted ones.

Pros

• +Wide range of sizes, from edge models to a 31B model
• +Apache 2.0 license for Gemma 4 allows commercial use and modification
• +Works with Ollama, LM Studio, Hugging Face, PyTorch, JAX and Android tooling
• +Specialised variants for medical, safety and function-calling use cases

Cons

• −Weights only: you provide the hardware, serving stack and safety layer
• −Older generations such as Gemma 2 use different, more restrictive terms
• −Google's page warns the models can produce inaccurate or offensive content

LLaMA

The default choice when you need weights you can actually hold, fine-tune and run offline, with an ecosystem of quantisations and inference stacks already built around it. The licence is not OSI-open, and Meta has been reorganising the Llama web presence, so links rot faster than the models do.

Pros

• +Weights are downloadable, not just API-accessible
• +Broad tooling support: llama.cpp, vLLM, Hugging Face and most inference runtimes
• +Multiple sizes so you can trade quality against a single GPU
• +Commercial use permitted for almost everyone under the MAU threshold

Cons

• −Community licence is not open source; attribution and the MAU cap both bind
• −Download links are pre-signed and expire after 24 hours or five uses
• −Meta keeps moving the site: llama.meta.com now forwards twice before landing
• −Running it is your cost and your operational problem

Does Gemma or LLaMA have a free tier?

Both: Gemma is free to use, and LLaMA is free to use.

Which has a public API, Gemma or LLaMA?

LLaMA ships a public API, as of our last verification. A public API for Gemma is not confirmed by us — check its developer docs before you plan an integration.