# Meta Llama ## Kurzbeschreibung **Llama **is Meta's family of generative foundation models for text and, in part, image/text understanding. Meta positions Llama as a flexibly deployable model series that can be fine-tuned, distilled, and deployed “anywhere”; this includes self-hosting, private cloud, and hosting through partners. Llama 4 brings native multimodality, while Llama 3.x continues to address important text, coding, translation, and agent use cases. ## Claim LLM “Industry Leading, Open-Source AI” ## Geeignet für - API Integration - Automation / Workflows - Data Extraction / Document Analysis - Customer Service & Chatbots - Programming / Software Development - Research - Writing & Editing - Texts / Content - Translations - Knowledge Management / Internal Search ## Kernfunktionen - API - Chat - Coding - Coding Assistant - Edge - Fine-tuning - Llama Stack - Multimodal - RAG - Self-Hosting - Language model - Tool Calling - Vision ## Preismodell - **free:** **Llama model weights / Download** Llama models can be downloaded, fine-tuned, distilled, and self-hosted under the Meta license; infrastructure costs for self-hosting are incurred separately. **Meta Llama API Preview / Waitlist **The Llama API is officially positioned via waitlist/login; I could not reliably substantiate a permanently freely usable public API free version with guaranteed limits. - **other:** **Managed Llama API** API access to current Llama models, API key, playground, SDKs, OpenAI-like integration, tool calling, and models such as Llama 4 Maverick/Scout according to the official Llama API page. **Self-hosting / own cloud / edge** Operation of the model weights on your own infrastructure, with cloud providers, or locally; suitable for data protection, cost control, and individual optimization. **Cloud provider / third-party hosting** Llama models are available through various cloud and inference providers; data protection, pricing, and server locations then depend on the respective provider. **Fine-tuning / distillation / Llama Stack** Customization and integration into your own AI architectures, depending on the model license, infrastructure, and technical setup. ## DSGVO und Datenschutz **Gesamteinschätzung:** Conditional **Overall assessment of hosting & data: ** Meta Llama is particularly strong because the models are available not only via an API, but also as downloadable model weights. This means that on-premises, private cloud, EU cloud, edge, and hybrid deployments are generally possible, provided the respective Llama license, infrastructure costs, and security requirements are met. Positive aspects include model portability, a self-hosting path, Llama Stack, fine-tuning/distillation options, and reduced vendor lock-in. A critical point is that although Llama is marketed by Meta as “open source,” it is licensed under Meta’s own license; depending on the definition of open source, this is not entirely equivalent to traditional open source. **Conclusion: ** Llama is very well suited for organizations that want maximum control over hosting, model operations, and data flows; for an immediately usable, contractually fully documented managed API with EU data residency, additional review of the specific API or cloud hosting variant is necessary. [Privacy Policy](https://www.facebook.com/privacy/policy/) **GDPR assessment:** Meta Llama must be assessed in two parts from a GDPR perspective: The Llama models as downloadable/open-weight models can generally be operated in a very privacy-friendly way when self-hosted, because server location, logging, access control, and data flows can be controlled directly. The Meta Llama API, on the other hand, can only be assessed with limited clarity from a GDPR perspective, because not all details regarding the DPA/data processing agreement, EU data residency, and the specific API server location are transparently documented publicly. **Positive** is that Meta explicitly states for the Llama API that API inputs and outputs are not used for training or improving the models, that data is not used for advertising/ad targeting, that role-based access controls are used, that API data is stored separately from other Meta product data, and that data is encrypted both in transit and at rest. **Negative **is that, according to the official site, the Llama API still operates via waitlist/login and that no publicly fully verifiable EU DPA/server location details are freely available. **Server location:** Freely selectable for self-hosting; for the Meta Llama API, it cannot be publicly verified as EU-only. Further links: Llama API, Llama models, Llama license/FAQ. [Privacy Policy](https://www.facebook.com/privacy/policy/) ## Hosting und Daten - **On-Prem / lokales Hosting:** unknown - **Private Cloud / Rechenzentrum:** unknown - **EU SaaS / Managed:** teilweise / indirekt - **Hybrid:** abgedeckt - **AVV / DPA:** unknown - **Kein Training auf Kundendaten:** abgedeckt - **Open-Source / Transparenz-Pfad:** abgedeckt ## Herkunft **Land:** USA **Taxonomie:** USA Meta Platforms, Inc., 1 Meta Way, Menlo Park, California 94025, USA. ## Vorteile - – Very flexible deployment paths: local, data center, private cloud, public cloud, managed provider. - – Broad model portfolio ranging from small/edge-capable models to large enterprise models. - – Well suited for coding, summarization, translation, tool use, RAG, and chatbots. - – Strong ecosystem fit across providers, GitHub, Hugging Face, and partner hosting. ## Nachteile - – No mature “all-in-one” business SaaS like with classic workplace tools; additional integration effort is usually required. - – The license is not unrestricted: among other things, there is a special rule for providers with >700 million monthly active users. - – “Open source” is legally disputed; the OSI does not regard Llama as open source under its definition. - – For Meta’s own Llama API, no clear, Llama-specific pricing transparency is publicly documented. ## Quellen - Offizielle Website: https://www.llama.com/ ## Letzter Datenstand 2026-04-24 ## Originalseite https://kifox.ai/en/ki-tools/meta-llama-en/