Foundation compute, storage, and networking resources that everything runs on. Includes container orchestration, cloud resources, and infrastructure as code.

Tools for data ingestion, processing, and storage. Includes data pipelines, streaming platforms, data lakes, and data warehouses.

Solutions for managing vector embeddings and structured ML features. Enables efficient storage, retrieval, and serving of both for powering AI applications.

Frameworks and tools for developing, training, and fine-tuning models. Includes ML frameworks, research environments, and foundation model APIs.

Tools for operationalizing models through their lifecycle, including experiment tracking, CI/CD pipelines, model registry, versioning, and approval workflows.

Infrastructure for deploying and serving models in production with optimized performance. Supports real-time, batch, and streaming inference patterns.

Tools for building user-facing AI applications and APIs. Includes frontend frameworks, API development tools, and end-user interfaces.

Orchestrates LLM workflows, agents, prompts, and tools for retrieval-augmented generation (RAG) and multi-agent applications.

Tools for monitoring and debugging AI systems in production. Includes metrics collection, logging, tracing, drift detection, and continuous evaluation.

Solutions for securing AI systems and ensuring compliance with policies. Includes authentication, authorization, explainability, fairness, and auditability.