JSON twin: https://www.healthaidb.com/software/llama.json
Meta
Llama is Meta's family of large multimodal language models (Llama 4) for building AI applications that support text and image understanding, long-context reasoning, and multilingual generation.
Llama (Llama 4) is a natively multimodal large language model series from Meta offering variants optimized for long-context understanding (Scout), efficient multimodal inference (Maverick), and research-scale teacher models (Behemoth). Models are available for download and via Llama API (waitlist); documentation, model cards, and developer resources are provided to support integration and deployment across research and commercial applications.
yes
2004
US-based limited preview for Llama API; deployment/hosting region depends on self-host or chosen cloud partner (BYO cloud region via downloads/partner clouds)
P0942
2025-09-07
Meta model license (open-source / permissive terms for downloaded models; contact vendor for API/commercial agreements)
Model weights available for free download; Llama API access via vendor waitlist; enterprise/managed pricing and support: contact vendor (Meta) for commercial terms.
free
2023
Python 3.8+, GPU-enabled Linux for large-model inference; cloud partner managed infra available
product
1.0
{
"company_name": "Meta",
"company_url": "https://ai.meta.com/",
"company_offices": [
"United States",
"Ireland",
"United Kingdom",
"Canada",
"India",
"Singapore",
"Australia",
"Germany",
"France",
"Brazil"
],
"company_founding": "2004",
"product_url": "https://www.llama.com/",
"categories": [
"research",
"developer platform",
"generative AI",
"clinical (model applied to healthcare use cases)",
"diagnostic assistance",
"patient-facing (via chatbots)"
],
"market_segment": [
"enterprise",
"research",
"consumer",
"smb"
],
"links": [
"https://www.llama.com/",
"https://ai.meta.com/blog/llama-3/",
"https://elion.health/products/llama",
"https://huggingface.co/meta-llama",
"https://ai.meta.com/blog/bimedix-built-with-llama",
"https://www.ibm.com/products/watsonx-ai/llama",
"https://www.llama.com/resources/case-studies/major-health-system/",
"https://about.meta.com/",
"https://github.com/facebookresearch/llama"
],
"summary": "Llama is Meta's family of large multimodal language models (Llama 4) for building AI applications that support text and image understanding, long-context reasoning, and multilingual generation.",
"description": "Llama (Llama 4) is a natively multimodal large language model series from Meta offering variants optimized for long-context understanding (Scout), efficient multimodal inference (Maverick), and research-scale teacher models (Behemoth). Models are available for download and via Llama API (waitlist); documentation, model cards, and developer resources are provided to support integration and deployment across research and commercial applications.",
"target_users": [
"developers",
"data scientists",
"AI researchers",
"software engineers",
"healthcare vendors / integrators",
"clinical informaticists"
],
"specialties": [
"clinical documentation augmentation",
"medical question answering",
"radiology image-text tasks (research/proof-of-concept)",
"clinical summarization",
"clinical decision support (prototype/integration)",
"biomedical research assistance",
"coding and billing assistance (NLP)",
"patient triage/chatbots (integration)"
],
"regions_available": [
"Global",
"United States",
"European Union",
"United Kingdom",
"Canada",
"Australia",
"India",
"Japan",
"South Korea",
"Latin America"
],
"languages_supported": [
"English",
"Spanish",
"Chinese (Simplified)",
"French",
"German",
"Portuguese",
"Arabic",
"Russian",
"Japanese",
"Korean",
"Italian",
"Dutch"
],
"pricing_model": "free",
"pricing_details": "Model weights available for free download; Llama API access via vendor waitlist; enterprise/managed pricing and support: contact vendor (Meta) for commercial terms.",
"license": "Meta model license (open-source / permissive terms for downloaded models; contact vendor for API/commercial agreements)",
"deployment_model": [
"SaaS (Llama API)",
"self-host / on-prem (downloadable model weights)",
"cloud-hosted via partners (AWS Bedrock, Google Vertex AI, etc.)"
],
"os_platforms": [
"Linux (server/GPU)",
"macOS",
"Windows",
"Web (API/Playground)"
],
"features": [
"Access to Llama family models (Llama 3, 4 variants)",
"Llama API with API keys and interactive playground",
"Lightweight SDKs (Python, TypeScript)",
"Model downloads for local/self-hosted inference",
"Support for multimodal (vision+text) models",
"One-click API key creation and managed endpoints (preview)",
"Guides and cookbooks for deployment and integration",
"Safety/protections tooling (Llama Protections, Guard)",
"Compatibility endpoints for OpenAI-style integrations",
"Streaming inference support (via partner platforms)"
],
"optional_modules": [
"Llama Protections / Guard (safety tooling)",
"Developer Use Guide & safety cookbooks",
"Managed hosting via cloud partners (Bedrock/Vertex/etc.)",
"Multimodal model variants"
],
"integrations": [
"AWS Bedrock (partner hosted Llama models)",
"Google Vertex AI (partner hosted Llama models)",
"Amazon/AWS ecosystem integrations",
"Hugging Face (model distribution)",
"Together AI (model hosting)",
"Lightning AI / Lightning deploy examples",
"Modal and other inference platforms"
],
"data_standards": [],
"api_available": "yes",
"system_requirements": "Python 3.8+, GPU-enabled Linux for large-model inference; cloud partner managed infra available",
"compliance": [],
"certifications": [],
"security_features": [
"Encryption in transit",
"Encryption at rest",
"Strict access control / API keys",
"Separation in storage (per Meta docs)",
"Developer guidance for vulnerability management"
],
"privacy_features": [
"Meta commitment: API inputs/outputs not used to train models",
"Separation in storage",
"Developer Responsible Use Guide and safeguards recommendations"
],
"data_residency": "US-based limited preview for Llama API; deployment/hosting region depends on self-host or chosen cloud partner (BYO cloud region via downloads/partner clouds)",
"customers": [
"ANZ Bank"
],
"user_reviews": [
"Llama models are great for running locally and for RAG workflows, but performance can be inconsistent on coding tasks.",
"I used Llama to build an app that matches resumes to jobs; it worked well for parsing and semantic search when tuned.",
"Llama 4 Scout and Maverick improved multimodal and long-context capabilities, but some users reported disappointment versus expectations.",
"Switched internal workloads to fine-tuned Llama variants to reduce API costs and increase control over data/privacy."
],
"ratings": [
"G2 (Meta Llama 3): 4.1/5 (aggregate listing of Llama/Meta Llama on G2)"
],
"support_channels": [
"documentation",
"community",
"developer guides",
"resources (cookbooks/videos/case studies)"
],
"training_options": [
"documentation",
"cookbooks",
"videos",
"case studies",
"community support"
],
"release_year": "2023",
"integration_partners": [
"AWS",
"NVIDIA",
"Databricks",
"Groq",
"Dell",
"Microsoft Azure",
"Google Cloud",
"Oracle Cloud",
"Groq",
"Hugging Face",
"Intel",
"Anthropic (ecosystem integrations)",
"Open source community projects (llama.cpp, LlamaIndex/Llama-Index)",
"Startups in the Llama Startup Program"
],
"id": "P0942",
"slug": "llama",
"type": "product",
"version": "1.0",
"last_updated": "2025-09-07",
"links_json": {
"self": "https://www.healthaidb.com/software/llama.json"
}
}