Architecture comparison

Anthropic vs Liquid AI

Anthropic runs closer to the foundational model (Native (own / self-hosted weights)) than Liquid AI (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]

Liquid AI

AI/LLM

Proximity

Native (own / self-hosted weights)

Models

proprietary / self-built models

Cloud · datastore

Hybrid

Compliance

—

Architecture

Liquid AI builds and serves its own Liquid Foundation Models (LFM and LFM2) with a custom hybrid liquid/convolution/attention architecture, optimized for both data-center and fully on-device deployment across CPUs, GPUs, and NPUs.[1][3][4][9][11] Public materials emphasize hardware-in-the-loop training and edge/PC deployment, but do not expose a full production cloud stack, suggesting a mix of self-hosted/model-serving infrastructure plus OEM/partner integrations rather than a single public hyperscaler.[9][11]

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