Meta AI: Foundation Models

Founded 2013|Menlo Park, CA|10001+ people|Open Source

What is Meta AI?

Meta AI develops the Llama series of open-weight large language models, which have become the foundation for a large portion of the open-source AI ecosystem. The AI division, formerly known as Facebook AI Research (FAIR), was founded in 2013 by Mark Zuckerberg and Yann LeCun.

Llama models are freely available under a community license for commercial use, can be fine-tuned and self-hosted, and are offered through dozens of inference providers including Together AI, Groq, DeepInfra, and AWS Bedrock. The Llama 4 family includes Scout (109B MoE, 10M context) and Maverick (400B MoE), with competitive performance at a fraction of proprietary model costs.

Meta Platforms employs approximately 78,865 people globally and generated $200.97 billion in revenue in 2025. The company has committed over $60B to AI infrastructure investment and released PyTorch, one of the most widely used machine learning frameworks in the world.

Key features

Core capabilities this platform advertises.

Llama open-source model family
Free for commercial use
Self-hostable on any infrastructure
Active open-source community
Multiple model sizes (8B to 405B)

Strengths and tradeoffs

What this tool does well, and the limitations to keep in mind.

Pros

Completely free open-weight models for commercial use
Strong performance competitive with proprietary models at lower cost
Flexible deployment: self-host, edge, cloud API, or air-gapped
Advanced MoE architecture with up to 10M token context windows
Massive ecosystem support across inference providers and frameworks

Cons

Hallucination issues with confidently incorrect outputs
Expensive GPU hardware required for self-hosting larger models
Math and complex reasoning still lag behind GPT-4 and Claude
Open weights but not fully open source (training data not published)

Plans & pricing

What's included in each plan, and how the tiers compare.

Open Weights (Self-Host)

Free

Free model weights download
Commercial use license
Full fine-tuning capability
Hardware costs: $2K-$100K+ for GPUs

Llama 4 Scout (API)

$0.15/1M input, $0.50/1M output

109B MoE architecture
10M token context window
Via third-party providers

Llama 4 Maverick (API)

$0.22/1M input, $0.85/1M output

400B MoE architecture
Competitive with GPT-4o
Natively multimodal

View official pricing page

Common use cases

Developers and researchers who want full control over their AI models and infrastructure

Self-hosted inference for data privacy
Fine-tuning for domain-specific applications
On-device AI for mobile and edge
Research and academic use
Cost-optimized inference at scale

Using Meta AI with Respan

Respan provides comprehensive observability for applications built on Llama models. Whether self-hosted or accessed via API, Respan tracks inference quality, latency, and cost across Llama deployments, helping teams optimize their open-source AI stack.

Monitor Llama model inference quality and latency
Track per-request costs across API providers
Evaluate fine-tuned Llama model performance over time
Compare Llama outputs against proprietary model baselines

Run Meta AI in production with Respan

10KFree traces/mo

1,000+Models

5 minSetup

Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 1,000+ models through one gateway.

Try Respan free

Best Meta AI alternatives & competitors

Top companies in Foundation Models you can use instead of Meta AI.

OpenAI

GPT-4o and GPT-4 Turbo frontier models