45 companies in the Modeling Warehouse run on Multi-Cloud, and how directly they consume their foundational models. Avg Foundation Proximity Score 69/100.
Creative · Multi-Cloud · Native (own / self-hosted weights)
Adobe runs a **multi-cloud** architecture primarily across AWS, Azure, and selected GCP sovereign regions, with Adobe Experience Platform and Experience Cloud services deployed on both AWS and Azure, and Firefly’s GPU-heavy training and inference infrastructure built on AWS. Core data and AI workloads rely on managed storage, compute, and networking primitives from these clouds, with Adobe layering proprietary platforms such as Experience Platform, Sensei, and Firefly on top.[1][2][3][5][6][7][12][14][15]
AI/LLM · Multi-Cloud · Native (own / self-hosted weights)
AI21 Labs develops and serves its own Jamba family of LLMs and related services via its AI21 Studio API and private deployments, while also distributing Jamba models through third‑party clouds such as Azure and upcoming NVIDIA APIs.[5][6][7] Public materials describe deployment options across public cloud and private/on‑prem environments but do not reveal a full production stack beyond this high‑level multi‑cloud posture.[3][5][6]
AI/LLM · Multi-Cloud · Native (own / self-hosted weights)
Anthropic runs **Claude** and related services on a safety-first **multi-cloud** compute fabric spanning AWS Trainium2, Google TPUv7 and NVIDIA GPUs, fronted by Kubernetes‑based microservices (API gateways, orchestration, rate limiting, caching, and safety filters), with state held in PostgreSQL, vector stores, Redis, and cloud object storage.[1][10][13] Production offerings like Claude API and Managed Agents expose a fully managed orchestration and agent runtime, while emerging self‑hosted sandboxes move tool execution into customer infrastructure but keep Claude inference, routing, and session state on Anthropic’s cloud.[2][3][11]
AI-infra · Multi-Cloud · Native (own / self-hosted weights)
Model-inference platform serving open models (Llama, etc.) on autoscaling GPU infra.
AI/LLM · Multi-Cloud · Native (own / self-hosted weights)
Enterprise-focused LLM lab training its own Command/Embed/Rerank models, served across clouds (Google Cloud, Oracle, AWS) and deployable in-VPC for data-sensitive customers. North platform targets RAG.
AI/LLM · Multi-Cloud · Native (own / self-hosted weights)
RAG-native enterprise platform training its own grounded language models.
DevTools · Multi-Cloud · Native (own / self-hosted weights)
Cursor runs a multi-cloud, event-driven architecture with most CPU/backend services on AWS, major GPU inference clusters on Azure plus newer GPU clouds, and specialized AI workloads on GCP, fronted by stateless gateways, queues, and GPU worker fleets for LLM inference and agents.[6][10] Workspace data and embeddings flow through turbopuffer, Pinecone, and Kafka-compatible streams, with Temporal-based orchestration for agent workflows and large multi-GPU clusters (including Fireworks AI-hosted proprietary fine-tuned models) serving code-focused LLMs.[4][6][7][8][10]
Data Platform · Multi-Cloud · Native (own / self-hosted weights)
Databricks runs a two-layer architecture where a Databricks-managed control plane hosts the UI, APIs, metadata, and orchestration services, while customer workloads execute in a compute/data plane inside the customer’s AWS, Azure, or GCP account (or Databricks’ serverless account) against cloud object storage using Spark and Delta Lake.[3][4][9][11] Around this core, Databricks positions an open-core lakehouse stack (Delta Lake, Spark, MLflow) with proprietary governance, serverless, and AI platform services, integrated into medallion-style production patterns.[1][2][6][12]
Search/Data · Multi-Cloud · Native (own / self-hosted weights)
Ships ELSER, its own retrieval model, and plugs partner LLMs for RAG.
AI/Voice · Multi-Cloud · Native (own / self-hosted weights)
ElevenLabs exposes proprietary speech, audio, and agent models via HTTPS REST, SSE streaming, and WebSocket APIs behind api.elevenlabs.io, backed by credit-based authentication and multi-tenant SaaS infrastructure designed for high concurrency and enterprise scalability.[1][7][9] Core value is provided by in-house deep learning voice and audio models rather than a foundational LLM as the primary engine.[2][5]
AI Infra · Multi-Cloud · Native (own / self-hosted weights)
Fast inference for open models on its own serving stack.
Biotech · Multi-Cloud · Native (own / self-hosted weights)
Generative-chemistry drug discovery on its own target-discovery + molecule-generation models.
AI Infra · Multi-Cloud · Native (own / self-hosted weights)
Builds PyTorch Lightning + a studio to train/serve open models (Llama, etc.) on cloud GPUs.
Automotive · Multi-Cloud · Native (own / self-hosted weights)
In-car MBUX assistant on Google Cloud Gemini; large in-house ADAS/vehicle ML.
AI/LLM · Multi-Cloud · Native (own / self-hosted weights)
Mistral runs a multi-cloud architecture where its hosted La Plateforme and Studio offerings sit atop partner clouds (Google Cloud, AWS, Azure, SAP, IBM, Snowflake, NVIDIA, Outscale), while open-weight models are also deployable on‑prem via standard inference containers.[10][11] The production stack is therefore split between first‑party EU‑centric hosting for regulated workloads and distribution of models through major cloud marketplaces and managed services.
Healthcare AI · Multi-Cloud · Native (own / self-hosted weights)
Medical-evidence answer engine on its own models trained over peer-reviewed literature.
Cybersecurity · Multi-Cloud · Native (own / self-hosted weights)
Palo Alto Networks runs a multi-cloud SaaS architecture with AWS as the primary runtime for major platforms like Prisma Cloud, using services such as RDS, Redshift, Neptune, OpenSearch, Redis, and S3 alongside Kubernetes-based microservices for data ingestion, analytics, and Infinity Graph. GCP (including Vertex AI) and Azure are used selectively for AI/ML workloads and cloud-native firewall offerings, with proprietary security engines and data stores layered on top of these managed services.
AI/Search · Multi-Cloud · Native (own / self-hosted weights)
Perplexity operates a multi-cloud architecture: it historically built and hosted its Sonar models on AWS (including Bedrock for Anthropic/Claude) while later signing a large Azure agreement to deploy models via Microsoft Foundry, indicating active use of both providers.[1][3][8][9][15] Its core production stack centers on proprietary Sonar LLMs (built on and fine‑tuned from open-source bases like Mistral and later Llama 3.x) combined with partner frontier models (GPT‑5/4-class, Claude, Gemini, Grok) selected per query.[1][2][5][6][10][11][13][14]
AI/LLM · Multi-Cloud · Native (own / self-hosted weights)
Reka trains and serves its own multimodal encoder–decoder frontier models (Core, Flash, Edge) on custom Kubernetes-based GPU clusters spanning multiple vendors, using PyTorch on large H100/A100 fleets and a separate A10/A100 inference stack.[2][5] Public deployment is via Reka’s own web app and API endpoints (chat.reka.ai, platform.reka.ai, showcase.reka.ai), with an OpenAI-compatible API server for Edge provided through vLLM and Hugging Face artifacts.[2][7][12]
Enterprise · Multi-Cloud · Native (own / self-hosted weights)
ServiceNow runs a proprietary SaaS platform across hyperscaler regions on AWS, Azure, and GCP, plus sovereign-cloud deployments, with a custom database layer (RaptorDB on PostgreSQL) replacing MariaDB/MySQL for most instances.[7][8] Its architecture is tightly integrated with NVIDIA NeMo microservices and GPU infrastructure for training and inference of its Apriel/Nemotron family of open models, while exposing workflow, observability, and discovery services over these multi-cloud foundations.[2][4][5][8]
Data Platform · Multi-Cloud · Native (own / self-hosted weights)
Snowflake runs a proprietary three-layer, multi-cluster shared-data architecture on AWS, Azure, and GCP, separating compressed columnar storage on cloud object stores from MPP compute warehouses and a distributed cloud services control plane.[1][16][19] Internally it is a service-oriented system with independently scalable storage, compute, and metadata/transaction services built for OLAP workloads.[16][19]
Automotive (EU) · Multi-Cloud · Native (own / self-hosted weights)
Automaker (Jeep/Peugeot/Fiat) embedding Mistral in-vehicle plus its own ADAS ML.
Healthcare AI · Multi-Cloud · Native (own / self-hosted weights)
Ambient clinical assistant blending its own medical models with partner LLMs.
AI-coding · Multi-Cloud · Native (own / self-hosted weights)
Privacy-first code assistant with its own models plus optional partner LLMs, deployable air-gapped.
Analytics · Multi-Cloud · Native (own / self-hosted weights)
Search/AI analytics; Spotter agent blends its own engine with OpenAI over the in-memory data store.
Gaming/3D · Multi-Cloud · Native (own / self-hosted weights)
Real-time 3D engine; Muse AI blends partner LLMs with its own generative tooling for creators.
Observability · Multi-Cloud · Direct API
Grafana Cloud runs a SaaS observability platform built on its open source engines (Mimir, Loki, Tempo, Prometheus) deployed across major public clouds with object storage backends like S3/GCS/Azure Blob for long‑term data. The core Grafana server is a Go backend with a React/TypeScript frontend that acts as a visualization and control plane over these distributed storage and query services.
Observability · Multi-Cloud · Cloud-hosted (Bedrock/Vertex/Azure)
Runs primarily on AWS with significant GCP presence; ingests trillions of points/day through Kafka into Cassandra and custom time-series stores. Bits AI / Watchdog use in-house anomaly ML plus LLMs.
CRM · Multi-Cloud · Cloud-hosted (Bedrock/Vertex/Azure)
Salesforce runs a proprietary, metadata-driven, multitenant application stack (Superpods, instances, orgs) on its Hyperforce infrastructure spanning its own data centers and leading public clouds, with a single logical multitenant database per instance and stateless app servers layered above it.[2][4][7][10] Around this core, it operates a hybrid data and streaming platform (Ajna Kafka, Hadoop/HDFS, monitoring stacks) and integrates external warehouses like Snowflake for Data Cloud and analytics workloads.[8][17]
Collaboration · Multi-Cloud · Cloud-hosted (Bedrock/Vertex/Azure)
Slack’s core production stack runs primarily on AWS with a cellular architecture across availability zones, Vitess‑sharded MySQL storage, stateful real‑time messaging services, and global edge regions, while its AI serving stack has evolved into a multi‑cloud setup spanning AWS Bedrock and Google Cloud Vertex AI.[3][5][10][11][4] Slack AI is integrated as an application layer atop this core messaging infrastructure, with strict data‑segregation and privacy controls rather than being a foundational replacement of Slack’s existing backend.[9][4]
Data · Multi-Cloud · Middleware / wrapper
Open-source data integration; connectors + AI assist via partner LLMs.
Database · Multi-Cloud · Middleware / wrapper
Open-source columnar OLAP DB; AI features plug partner LLMs over its analytics engine.
Data Platform · Multi-Cloud · Middleware / wrapper
Managed Apache Kafka (Confluent Cloud) deployed across AWS/Azure/GCP with the Kora cloud-native engine; adds stream processing via Flink. Built around open Kafka with proprietary cloud tooling.
Data · Multi-Cloud · Middleware / wrapper
Analytics-engineering standard (dbt); Copilot AI is model-agnostic over the semantic layer.
Data · Multi-Cloud · Middleware / wrapper
Managed data movement; AI features via partner LLMs over pipeline metadata.
Database · Multi-Cloud · Middleware / wrapper
Atlas runs the MongoDB document database as a managed service across AWS, Azure and GCP. Atlas Vector Search makes it a RAG datastore; AI features integrate partner LLMs rather than a captive model.
AI Infrastructure · Multi-Cloud · Middleware / wrapper
Managed vector database for RAG, deployed across AWS/GCP/Azure with a serverless architecture that separates storage (object store) from query compute. The index, not a model, is the product.
AI Infra · Multi-Cloud · Middleware / wrapper
Open-source vector database for RAG; model-agnostic.
Consumer AI · Multi-Cloud · Middleware / wrapper
AI hardware (r1); routes to multiple partner LLMs behind a large-action-model orchestration layer.
AI Infra · Multi-Cloud · Middleware / wrapper
Open-source vector database; the index, not a model, is the product.
MLOps · Multi-Cloud · Middleware / wrapper
ML experiment-tracking + LLMOps (Weave); model-agnostic, runs over customers chosen models.
AI Search · Multi-Cloud · Middleware / wrapper
AI search/agent routing across multiple frontier + open models over its own web index.
DevSecOps · Multi-Cloud · Off the LLM map (in-house non-LLM ML)
Secure-by-default container images + supply-chain security; off the foundational-LLM map at the core.
Dev-infra · Multi-Cloud · Off the LLM map (in-house non-LLM ML)
Durable-execution platform; model-agnostic orchestration, off the foundational-LLM map at the core.
Cybersecurity · Multi-Cloud · Off the LLM map (in-house non-LLM ML)
Wiz operates as a SaaS platform with an agentless, multi-cloud security architecture that ingests cloud, workload, identity, and data metadata into a unified Security Graph. Its production environment is described as immutable infrastructure managed through infrastructure-as-code, with isolated production databases and cloud-native encryption, but the exact underlying database vendors are not publicly disclosed.