Vertex AI

Enterprise ML and AI platform covering PAI-Studio visual workflow builder, PAI-DSW Jupyter notebooks, PAI-EAS elastic inference serving, PAI-Blade inference optimisation, and integration with Alibaba's Qwen foundation models

Next-generation SageMaker (rebranded SageMaker AI) unifying data, analytics, and AI in one workspace — Studio notebooks, HyperPod for foundation-model training at scale, Lakehouse with QuickSight + S3 Tables integration, AutoPilot AutoML, managed training jobs, hosted inference endpoints, and Feature Store, with re:Invent 2024 introducing the unified SageMaker AI workspace and 2025 Summit additions extending it with lakehouse auto-onboarding

Build generative AI applications with foundation models from Anthropic (Claude Opus 4.7 from April 2026), Cohere, Meta, Mistral, Stability AI, TwelveLabs (video understanding), and Amazon's own Nova family — accessed via a single API with fine-tuning, knowledge bases, agents, and a model marketplace for discovery and easy onboarding

Production runtime for AI agents — managed memory, identity, gateway, observability, and tool integration so teams can ship agentic workflows on top of any framework (Strands Agents, LangGraph, CrewAI, vendor-direct) without rebuilding the operational substrate

Native vector storage in S3 — up to 2 billion vectors per index, sub-100 ms query latency, S3-native durability, and pricing claimed up to 90 percent lower than dedicated vector databases for retrieval-augmented generation and embedding-heavy workloads

Enterprise access to OpenAI models including GPT-4, GPT-3.5, and DALL-E with Azure security, private networking, regional deployments, and pay-as-you-go or provisioned throughput

End-to-end platform for building and deploying ML models with automated ML, designer (drag-and-drop), managed compute clusters, MLflow tracking, and responsible AI dashboards

Video streaming platform with global delivery and per-minute pricing

Globally-distributed vector database for RAG, similarity search, and recommendations with native Workers AI integration, up to 5M vectors per index, metadata filtering, and cosine / Euclidean / dot-product similarity

Serverless GPU-backed AI inference at the edge running a catalogue of open-source text, image, speech, and embedding models (Llama, Mistral, Stable Diffusion, Whisper, BGE) with pay-per-neurone pricing and direct binding from Workers code

End-to-end AI platform (formerly MLflow + Mosaic ML) for training, fine-tuning, deploying, and monitoring foundation models and custom ML models on the Lakehouse

Serverless vector database built into the Lakehouse for similarity search, RAG applications, and recommendation systems with automatic embedding sync from Delta tables

Hosted MLflow with managed tracking server, model registry, and deployment integration — open-source ML lifecycle tooling tightly coupled to Databricks Unity Catalog and Mosaic AI

End-to-end AI development platform with AutoML, data labelling, distributed training on Ascend and GPU clusters, and one-click deployment to cloud or edge

Enterprise AI studio for training, validating, tuning, and deploying foundation models and traditional ML models, with IBM's Granite model family, Hugging Face integration, prompt lab, synthetic data generation, and governance via watsonx.governance

Kakao's Korean-first foundation-model family (Kanana Flash / Essence / Nano) for chat, code, and embeddings — multilingual but tuned for Korean conversational performance

Naver's HyperCLOVA X foundation-model platform for Korean-language LLM workloads — chat completion, embeddings, function calling, RAG over Korean text with strong native-language performance

Managed MLOps platform (formerly Open Data Hub) for training, serving, and monitoring ML models on OpenShift with JupyterHub, KServe, Kubeflow, and PyTorch operators

Fully managed service offering Cohere and Llama large language models

Fully managed machine learning platform with JupyterLab notebooks, conda-environment library, job orchestration, model deployments as HTTPS endpoints, feature store, and model catalog — integrated with Autonomous Database and Object Storage for end-to-end ML workflows

AI layer integrated across Salesforce products: predictive lead scoring, opportunity scoring, generative AI for emails and summaries

Salesforce platform for building, deploying, and governing autonomous AI agents grounded in CRM data, with Atlas Reasoning Engine, Agent Studio, and Data Cloud retrieval

Managed inference for open-source LLMs (Llama, Mistral, DeepSeek) hosted in EU datacentres

Developer framework for building data applications in Python, Java, and Scala that run inside Snowflake