Llama

Llama is Meta's open-weight AI model family for developers and teams that want to build generative AI applications, assistants, developer tools, and multimodal workflows with more control over model selection, deployment, and integration than a turnkey chatbot or hosted SaaS application usually provides.

The listed pricing is Free for model access, while production teams should separately evaluate any deployment, hosting, storage, monitoring, safety evaluation, and engineering costs required to operate the models. Llama is presented in the current tool data as Meta's family of open AI models. That positioning matters: unlike a single SaaS chatbot, Llama is primarily a foundation model ecosystem for teams that want to build their own products, workflows, and infrastructure around generative AI. The website URL is https://www.llama.com/, the listed category is AI Models, and the available pricing information is Free.

Current official Llama resources identify Llama 4 Scout and Llama 4 Maverick as recent Llama 4 models released on April 5, 2025. Meta describes Llama 4 Scout as a natively multimodal mixture-of-experts model with 17 billion active parameters, 16 experts, single-H100 efficiency with Int4 quantization, and a 10 million token context window. Meta describes Llama 4 Maverick as a natively multimodal model for image and text understanding with 17 billion active parameters and 128 experts. Official Llama documentation also lists Llama Guard 4 as an updated protection model with support for Llama 4. Earlier Llama 3.1 details remain relevant for some buyers: Meta announced a 405B model, 128K context length, and support across eight languages in 2024.

The main value of Llama is flexibility. Developers can use Llama models as building blocks for custom chatbots, internal copilots, retrieval-augmented generation systems, coding assistants, research prototypes, and domain-specific AI applications. Because it is an open model family from Meta, teams can evaluate model behavior more directly, adapt deployment choices to their own infrastructure, and avoid designing every workflow around a single hosted vendor interface. This is especially relevant for organizations with privacy, latency, cost-control, or customization requirements that make black-box API-only systems less attractive.

Compared to application-layer AI tools, Llama is strongest when the user's priority is control over implementation rather than a finished end-user interface. Hosted providers such as OpenAI, Anthropic, and Google Gemini may be easier choices for teams that want managed APIs, polished hosted tooling, and commercial support paths. Mistral and Qwen are closer alternatives for teams evaluating open or open-weight model ecosystems. Llama is most compelling for technical teams that can manage model selection, hosting, orchestration, evaluation, and application integration themselves. Its biggest tradeoff is that the free model availability does not eliminate engineering work: users still need to handle deployment, inference costs, safety evaluation, monitoring, and product integration.