What tech stack does Tesla use?

Tesla's vehicle software is predominantly C and C++ — the majority of in-car software from infotainment to the autonomy stack is written in C or C++. Python handles rapid AI/ML prototyping: Musk has described the pattern of building neural networks in Python first (using PyTorch as the primary deep-learning framework for FSD), then converting to C++/C for production deployment in the vehicle. Go and Java power backend services outside the vehicle.

The most significant infrastructure change in 2025 was the shutdown of the Dojo supercomputer program. Tesla disbanded the Dojo team in August 2025 — with Dojo's lead, Peter Bannon, departing — and pivoted to a hybrid strategy: a 67k H100-equivalent Cortex NVIDIA GPU cluster at Gigafactory Texas for near-term FSD training needs, and a $16.5 billion deal with Samsung for next-generation AI6 chips for inference deployment at scale. Dojo's vision of a fully proprietary exascale AI training chip was deemed too expensive and complex relative to the Samsung partnership. Tesla separately committed $500 million toward a supercomputer facility at Gigafactory New York (Buffalo).

Tesla's web properties (tesla.com, the owner app, Tesla Shop) use React and TypeScript, with Node.js powering API layers. The Tesla mobile app (iOS and Android) enables owners to control vehicles, schedule charging, and book service. For data infrastructure, Tesla relies on Apache Kafka for real-time streaming of vehicle telemetry from its global fleet, Apache Spark and Hadoop for batch processing and AI training data pipelines, and PostgreSQL/MySQL for relational storage.

On GTM tooling, Tesla made a notable decision to replace Salesforce CRM with a proprietary system called the Tesla Operating System — a reflection of the company's deep vertical integration bias. For DevOps, Tesla uses Docker for containerization, Git via Atlassian Stash/Bitbucket for version control, and JIRA for project management. Primary public cloud is AWS, verified through job postings and infrastructure signals.

Tesla's build-vs-buy posture is aggressively internal. The decision to replace Salesforce, build its own FSD AI chips (HW4/AI4 in vehicles), develop the Cortex GPU cluster, and now sign a $16.5B Samsung deal for AI6 chips rather than rely on merchant silicon demonstrates that Tesla will fund enormous capital commitments to maintain control of its stack. Most horizontal SaaS categories face a 'we'll build it ourselves' veto at the engineering level.

Vendors with the strongest opportunity are in categories where the cost and complexity of building is prohibitive or where deep specialization creates a clear ROI: industrial simulation software for Cybercab and Optimus manufacturing lines, battery chemistry and formation tooling, grid interconnection hardware, automotive embedded cybersecurity (UN R155 compliance required for European markets), synthetic data generation for FSD training at scale, and ML observability platforms that can ingest from Kafka streams. Robotics-adjacent vendors — end-effectors, actuators, force sensors — have a clear opening as the Optimus Gen 3 ramp accelerates toward a target of 1 million units per year by late 2026. Budget owners are engineering-led; route through VP Ashok Elluswamy's team for software/AI and VP Lars Moravy's team for manufacturing/materials.