Azure AI Search

Alibaba's flagship open-source foundation model family covering Qwen (text), Qwen-VL (vision-language), Qwen-Audio, and Qwen-Coder — accessible via the DashScope API with chat, completion, embeddings, and function-calling endpoints

Managed OpenSearch and Elasticsearch 7.10 compatible search, log analytics, and vector search service with Multi-AZ, cold / UltraWarm storage tiers, OpenSearch Ingestion (Data Prepper), and a serverless option for variable workloads

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

AWS-built foundation model family covering text (Micro, Lite, Pro, Premier), image generation (Canvas), and video generation (Reel) — accessed through the Bedrock runtime with tight pricing and low-latency streaming, launched at re:Invent 2024

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

Managed retrieval-augmented-generation service — index your content (R2 buckets, websites, Workers KV) and query it with natural language from a Workers binding, REST API, or MCP server. Originally launched as AutoRAG and renamed AI Search in 2026.

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

AI inference runtime that deploys models to Gcore's edge POPs and routes requests to the nearest GPU-backed endpoint, with support for open-source LLMs and custom model containers

Unified platform to build, deploy, and scale ML models with AutoML, custom training on TPUs and GPUs, model registry, pipelines, feature store, and generative AI studio

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

Managed inference service hosting Cohere Command and Embed plus Meta Llama large language models — pay-per-token chat / completion / embedding APIs, plus fine-tuning on customer datasets via dedicated AI clusters

End-to-end platform for building, deploying, and governing production AI workloads on OCI — unifies Gen AI models, agent orchestration, retrieval-augmented generation, and policy-based governance controls in a single managed service so enterprises don't have to assemble them from primitives.

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

Fully managed AI and ML service offering hosted LLMs, vector search, and ML functions inside Snowflake SQL

Tencent's in-house family of large language models (Hunyuan-Pro, Standard, Lite, plus multimodal Hunyuan-Vision) accessible via the Hunyuan API, with enterprise-grade context windows up to 256K, function calling, embeddings, and tuning