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Meta

Llama (Meta)

The most widely adopted open-weight model family, and the usual starting point for self-hosting.

Chat & General LLMs Active #LLM#OpenWeight#Self-Hosted

In short

Removes the third-party data path entirely: weights can be run on infrastructure the organization controls. For regulated environments that is frequently the deciding factor, irrespective of benchmark position.

What it is best at

  1. Running a capable model entirely inside a controlled environment
  2. Fine-tuning on proprietary data without sending it to a vendor
  3. Avoiding per-token costs at high, predictable volume

Built for: Dev Teams · Enterprise Operations · Compliance/Audit Professionals

Technical foundation

Base model
Meta's own open-weight model family, released in several sizes.
Context and file handling
Depends entirely on the serving stack chosen, not on the model distribution.
Latency
A function of the hardware it runs on. Smaller variants run acceptably on modest hardware; the largest need serious GPU capacity.
Output quality and limits
Competitive with proprietary models on many tasks, generally behind the frontier ones on the hardest reasoning. The trade is capability for control.

Pricing and access tiers

TierModelKey inclusionsLimits
Self-hostedInfrastructure cost onlyFull weights, fine-tuning rights subject to licenceBounded by your own hardware
Third-party hostedPay per tokenManaged inference from cloud and inference providersProvider rate tiers

Pricing, version numbers, context-window sizes, and compliance certifications change frequently. Where stated they are accurate as of the as_of date and should be confirmed with the vendor before any procurement or compliance decision. Where they could not be stated confidently they are omitted rather than guessed.

Security, privacy and governance

Training data opt-out
Not applicable when self-hosted: the data never leaves your environment. That is the entire argument for this option.

The governance question this raises

The licence is a community licence, not a standard open-source licence. It carries conditions, including at very large user scale. Have counsel read it before building a commercial product on it rather than assuming MIT-style terms.

No compliance certifications are listed here. Certification status is vendor-specific and time-specific, so it is stated only where verified rather than assumed. Check the vendor’s trust centre and confirm it covers the specific tier you are buying.

Integrations and ecosystem

  • Hugging Face, Ollama, llama.cpp
  • Every major cloud model marketplace
  • Standard orchestration frameworks

API and SDKs: No first-party API. Served through whatever stack you choose or a third-party host.

The verdict

Strengths

  • Complete data control when self-hosted
  • No per-token cost at volume
  • Large ecosystem and abundant tooling

Drawbacks

  • Community licence is not standard open source and has real conditions
  • Behind the frontier on hardest reasoning
  • Self-hosting demands infrastructure and ML capability

Consider instead: Mistral / Le Chat, Hugging Face, DeepSeek

Frequently asked questions

What is Llama (Meta) used for?

Removes the third-party data path entirely: weights can be run on infrastructure the organization controls. For regulated environments that is frequently the deciding factor, irrespective of benchmark position.

What model does Llama (Meta) run on?

Meta's own open-weight model family, released in several sizes.

Does Llama (Meta) train on your data?

Not applicable when self-hosted: the data never leaves your environment. That is the entire argument for this option.

What are the alternatives to Llama (Meta)?

The closest comparable tools are Mistral / Le Chat, Hugging Face, DeepSeek. Which fits depends on where the work already lives and what the organization's data terms require.

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