Architecture comparison

Anthropic vs OpenAI

Anthropic runs closer to the foundational model (Native (own / self-hosted weights)) than OpenAI (Native (own / self-hosted weights)). No shared foundational model — different bets.

Anthropic

AI/LLM

Proximity

Native (own / self-hosted weights)

Models

Anthropic · Claude

Cloud · datastore

Multi-CloudPostgreSQL[13]vector databases (e.g., Qdrant)[4][13]Redis/Valkey (cache)[5][9][13]object storage (cloud blob storage, e.g., S3/GCS)[1][13]

Compliance

SOC 2 (inferred, typical for enterprise AI SaaS; not explicitly confirmed)[2][3]ISO 27001 (inferred, typical for scale enterprise workloads; not explicitly confirmed)[2][3]privacy-preserving sandbox perimeter for agents (policy/architecture control rather than formal cert)[2][11]network, application, and API security enforcement via distributed infrastructure (Akamai)[12]

Architecture

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]

OpenAI

AI/LLM

Proximity

Native (own / self-hosted weights)

Models

OpenAI · GPT

Cloud · datastore

HybridAzure Cosmos DBPostgreSQLBlob/Object Storage (e.g., Azure Blob)Kafka (for event/log streaming)

Compliance

SOC 2 (inferred from enterprise-grade cloud use on Azure)[15][16]ISO 27001 (inferred from Azure AI and hyperscaler environments)[15][16]Regional data residency and enterprise controls via Azure/OpenAI Deployment Company (inferred)[15][19][20]

Architecture

OpenAI runs large GPU superclusters and Kubernetes-based orchestration across Azure and its own data centers in a hybrid setup, with Azure as the primary cloud and custom HPC clusters (e.g., Stargate) for training and serving frontier models.[15][16][19] Core application and API workloads use relational stores like PostgreSQL for accounts/settings and globally scalable databases such as Azure Cosmos DB plus Kafka streams for high-volume conversation, analytics, and event data.[1][8][12]

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