Agent Stack Landscape

Graph-based orchestration runtime for long-running, stateful agents.

Framework for building LLM-powered applications and workflows.

AI-native code editor with codebase context, multi-file agents, and intelligent model routing for teams.

Data framework for connecting LLMs to private and structured data.

OpenAI develops the GPT family of foundation models, ChatGPT, developer APIs, and enterprise AI platforms. It helped popularize large language models through ChatGPT and now powers AI assistants, coding tools, enterprise copilots, retrieval systems, and agent-based applications across industries.

Anthropic 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.

Google combines frontier AI research (DeepMind), cloud infrastructure (Vertex AI), custom AI hardware (TPUs), and productivity products (Workspace, Android, Search), making it one of the most vertically integrated AI platforms. Gemini is the model layer; Google Cloud and consumer surfaces are the distribution layer.

Meta advances open multimodal research and releases the Llama open-weight model family used widely in self-hosted AI stacks. Muse Spark 1.1 adds Meta’s first paid Meta Model API 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.

Microsoft delivers Azure AI, Microsoft 365 Copilot, GitHub Copilot, Phi small models, and Semantic Kernel—plus deep distribution of OpenAI models through Azure OpenAI Service. It is the default enterprise path for copilots, governed LLM APIs, and AI that sits inside Office, GitHub, and Azure workloads.

Databricks 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, pipelines, and Spark workloads rather than as a standalone chat API.

Snowflake runs the Data Cloud for analytics and increasingly AI—Cortex functions, Arctic models, and LLM apps that stay close to governed tables. Enterprises use it when the priority is secure data access and SQL-native AI features more than building a separate model platform.