Automotive
Proximity
Native (own / self-hosted weights)
Models
Google · Geminiproprietary / self-built models
Cloud · datastore
Multi-Cloudown
Compliance
ISO 27001GDPR
Architecture
In-car MBUX assistant on Google Cloud Gemini; large in-house ADAS/vehicle ML.
Automotive
Proximity
Native (own / self-hosted weights)
Models
proprietary / self-built models
Cloud · datastore
HybridAWS S3 (object storage / data lake)[10]LakeFS (data lake versioning)[10]Parquet (columnar file format for video / telemetry)[10]TFRecord (training data format)[10]Snowflake (cloud data warehouse)[10]BigQuery (cloud data warehouse)[10]Redis (real-time state / caching)[1]Kafka / Pulsar (streaming backbone)[1][10]
Compliance
TLS/SSL-encrypted APIs for vehicle-cloud communication[10]Tokenized vehicle identifiers for data ingestion[10]Phased OTA rollout and canary deployments for safety and reliability[1][4]Simulation-driven QA and regression testing before production updates[4]
Architecture
Tesla runs a vertically integrated, edge-first architecture where custom FSD hardware in vehicles and robots performs real-time inference, while a hybrid cloud (including AWS and in-house GPU clusters) ingests telemetry into Kafka/Flink streams, S3/LakeFS lakes, and Snowflake/BigQuery warehouses for continuous model training and OTA updates.[1][7][8][10] Production for cars, energy products, and robots is tightly coupled to this AI and data infrastructure, effectively making Gigafactories and AI clusters part of a single closed-loop manufacturing and autonomy system.[2][6][7]