Meta
Open-weight and hosted models spanning Llama plus Meta’s paid Muse Spark API.
Meta advances open multimodal research and releases the Llama open-weight model family used widely in self-hosted AI stacks. Muse Spark 1.3 (with Muse Code) is Meta’s paid Meta Model API path for agentic coding and multimodal agents. Combined with PyTorch, Llama still lets organizations train, fine-tune, and serve models without depending on a proprietary API.
Why Meta matters
Meta changed the economics of AI by releasing capable open-weight models, and Muse Spark extends Meta into paid API competition with OpenAI and Anthropic. Because Llama models are released as open weights, organizations can fine-tune, self-host, and deploy them in environments where proprietary APIs are unsuitable—on-prem, air-gapped, or cost-sensitive at scale.
Last reviewed: 4 September 2026
Best fit for
When architects typically choose Meta.
- Self-hosting
- On-premises AI
- Fine-tuning
- Cost-sensitive inference
- Open-source stacks
- Paid Meta Model API agents (Muse Spark / Muse Code)
Strengths
Qualitative snapshot for architects—not a public ranking.
- Open weights★★★★★
- Self-hosting★★★★★
- Fine-tuning★★★★★
- Ecosystem (PyTorch)★★★★★
- Hosted API★★★☆☆
Quick facts
- Founded
- 2004
- Headquarters
- Menlo Park, CA, USA
- Ownership
- Public
- Open source
- Yes
- Enterprise
- Yes
- Flagship model
- Llama 4
On DataAIHub
- 3Models
- 5Products
- 5Tools
- 3GitHub
- 2Research
- 4Guides
- 13Benchmarks
Company profile
- Founded
- 2004
- Headquarters
- Menlo Park, CA, USA
- Founders
- Mark Zuckerberg, Eduardo Saverin, Andrew McCollum, Dustin Moskovitz, Chris Hughes
- CEO
- Mark Zuckerberg
- Funding
- Public (NASDAQ: META)
- Ownership
- Public
- Country
- United States
- Primary focus
- Open-weight foundation models, Multimodal research, PyTorch, Meta Model API / Muse Spark
- Target users
- Researchers, Developers, Enterprises with self-host needs, API builders evaluating Meta Model API
- Revenue model
- Advertising (parent), Open models as ecosystem strategy, Meta Model API usage
- Deployment
- Self-hosted Llama / Muse Glimmer weights via Hub/Ollama/vLLM; Muse Spark via Meta Model API
- Licensing
- Llama Community License; Muse Glimmer Apache-2.0; Muse Spark proprietary API
- Open source
- Yes
- Cloud provider
- No
- Website
- https://ai.meta.com
- Confidence
- High
- Source coverage
- 16
Ecosystem
Competes with
- OpenAI
Open Llama stacks are the main self-hosted alternative to GPT APIs.
- Google DeepMind
Competes on multimodal open vs closed model strategies.
- Mistral AI
Fellow open-weight provider competing for European and enterprise self-host.
Works with
- Ollama
Easiest local runtime for Llama models.
- vLLM
High-throughput serving engine for Llama in production.
- LlamaIndex
Popular RAG framework used with Llama backends.
- Muse Spark
Closed Meta Model API path for agentic coding alongside open Llama.
- Muse Glimmer
Apache-2.0 ~30B on-device Muse agent weights alongside open Llama.
Recommended for
- Llama models
Pick the right Llama generation for your deployment.
- Fine-tuning
Because Llama models are released as open weights, organizations can fine-tune, self-host, and deploy them where proprietary APIs are unsuitable.
Often paired with
- Hugging Face
Default Hub for distributing and discovering Llama weights.
How Meta evolved
Key moments in chronological order.
- Model
Agentic and coding upgrade in Muse Code and the Meta Model API. Previously available reasoning modes ship now; max reasoning is withheld pending additional safety testing. Open weights for Muse Spark remain unreleased.
- Open source
Apache-2.0 30B on-device agent model from Meta Superintelligence Labs; distinct from closed Muse Spark and from Llama 4.
- Product
Coding-focused Muse Spark 1.2 and Muse Code terminal agent beta; Meta Model API access expands globally.
- Model
Muse Spark 1.1 + Meta Model API
Paid multimodal agentic model via Meta’s first public Model API preview.
- Model
Next open multimodal generation for self-hosted stacks.
- Model
Llama 3
Major quality jump for open-weight chat and coding models.
- Open source
Llama 2 open release
Openly available weights for commercial and research use.
- Model
LLaMA to researchers
Research weights that sparked the open-model wave.
- Open source
PyTorch released
Becomes the dominant research framework and later the industry standard for training modern neural nets.
Products
Foundation models
Related tools
Related research
- Meta AI research
Evaluation
- CRAG — Comprehensive RAG Benchmark
Original Paper
GitHub
Related benchmarks
Related rankings
Related guides
- Llama Models
How Llama generations differ for chat, coding, and multimodality.
- Fine-Tuning
Adapt open Llama weights to your domain.
- Large Language Models
Foundations behind open-weight LLM deployment.
- RAG
Self-hosted retrieval stacks built on Llama.
Explore more companies
- Mistral AIFranceMistral AI builds Mistral Large 3, Small/Medium commercial tiers, and open-weight models, plus La Plateforme APIs and Le Chat. Mixtral remains an Apache-2.0 self-host option; it is not listed on Mistral serverless API pricing as of 2026-08-16. Regional Endpoints (EU or US) are generally available.
- Hugging FaceUnited StatesHugging Face provides the largest open platform for discovering, sharing, evaluating, and deploying machine learning models, datasets, and demos. It has become the default collaboration hub for the open-source AI ecosystem—backed by Transformers, Diffusers, PEFT, and managed inference. On 2026-09-03 NVIDIA agreed to acquire Hugging Face for $12.93B; until close (expected H1 2027, pending regulators) Hugging Face remains independent, and NVIDIA says the Hub will stay open to all model builders, clouds, and accelerators.
- AlibabaChinaGlobal technology group whose Qwen team releases competitive multilingual and multimodal foundation models for cloud and open-weight use.
- AnthropicUnited StatesAnthropic builds the Claude family of foundation models for enterprises that prioritize instruction following, long-context reasoning, coding quality, and predictable, safety-conscious behavior. Constitutional AI and careful system design are core to how Claude is trained and positioned.
- DatabricksUnited StatesDatabricks builds the Lakehouse platform and Mosaic AI for training, serving, and governing models on enterprise data—plus open projects like MLflow, Delta Lake, and Unity Catalog. Teams use it when RAG, agents, and fine-tuning need to run next to governed tables. Kimi K3 is available through Unity AI Gateway (2026-08-06) alongside proprietary frontier APIs.
- MicrosoftUnited StatesMicrosoft delivers Azure AI, Microsoft 365 Copilot, GitHub Copilot, Phi small models, and Semantic Kernel—plus deep distribution of OpenAI models through Azure OpenAI Service and Microsoft Foundry (GPT-6 Astra as of 2026-09-03). It is the default enterprise path for copilots, governed LLM APIs, and AI that sits inside Office, GitHub, and Azure workloads.