Architecture comparison

Cruise vs Wayve

Cruise runs closer to the foundational model (Native (own / self-hosted weights)) than Wayve (Native (own / self-hosted weights)). Shared foundation: proprietary / self-built models (highlighted).

Cruise

Autonomous

Proximity

Native (own / self-hosted weights)

Models

proprietary / self-built models

Cloud · datastore

GCPown

Compliance

SOC 2

Architecture

GM self-driving running its own perception/planning ML; restructured toward personal-vehicle autonomy.

Wayve

Autonomous

Proximity

Native (own / self-hosted weights)

Models

proprietary / self-built models

Cloud · datastore

AzureAzure Storage (Blob/Object)[7]Azure Databricks (Delta Lake/Parquet over object storage)[7]Azure Machine Learning experiment/model metadata stores[9][12]Internal time-series/telemetry stores for fleet data (unspecified, likely on Azure)[9][12]

Compliance

Azure built-in compliance (ISO 27001, SOC 1/2/3, GDPR support) inferred from exclusive Azure usage[7][12]Wayve safety framework for AV (Safety 2.0 paradigm, not a formal security cert but core to production governance)[8]

Architecture

Wayve runs a proprietary end-to-end embodied driving stack on Microsoft Azure, using large vision-world models (AI Driver, GAIA-3) trained on petabyte-scale fleet and simulated data via Azure Kubernetes Service, Azure Machine Learning, Databricks, and GPU clusters, then deploys a compressed model on in-vehicle compute for production driving.[7][9][12] The architecture replaces modular perception/mapping/planning with a single neural policy and related foundation models (driving, simulation, scenario classification, language explanation), all controlled via internal MLOps pipelines on Azure.[1][4][9][12]

← Full orbit map · Score your own stack →