Waymo

What tech stack does Waymo use?

Waymo's core engineering runs on C++ and Python with TensorFlow for machine learning, and it leans heavily on Google Cloud for data and compute — unsurprising for an Alphabet subsidiary. Note: the stack below is detected from public signals (the Waymo Open Dataset and tooling, the engineering blog, research papers and job posts), so it is directional rather than an exhaustive internal inventory.

Primary languages
C++, Python
ML framework
TensorFlow
Cloud
Google Cloud
Critical path
On-vehicle perception/planning
Data
Google Cloud Storage, BigQuery
Parent platform
Alphabet / Google infra

What technologies does Waymo use?

C++/Python core, TensorFlow ML, and Google Cloud infrastructure — detected from public sources.

  • C++· Backend / on-vehicle
  • Python· Backend / ML
  • TensorFlow· Machine learning
  • Google Cloud· Infrastructure
  • Google Cloud Storage· Data
  • BigQuery· Data
  • GPUs / TPUs· Compute
  • Custom on-vehicle compute· Hardware/Infra
  • iOS / Android (rider app)· Mobile

Sources:GitHub — Waymo Open Dataset (C++/Python/TF)PyPI — waymo-open-dataset (TensorFlow)

What does Waymo use on the backend and infrastructure?

Waymo's backend and on-vehicle systems are built primarily in C++ (for performance-critical perception, prediction and planning) and Python (for tooling, data pipelines and ML workflows) — both are visible in the publicly released Waymo Open Dataset library, which ships custom TensorFlow ops written in C++ and Python.

For infrastructure, Waymo leans on Google Cloud as an Alphabet subsidiary: research and tooling reference Google Cloud Storage for data and Google's compute (GPUs and TPUs) for training. The autonomy software also runs on custom on-vehicle compute — the sixth-generation Waymo Driver integrates cameras, lidar and radar with onboard processing for real-time decisions.

What does Waymo use on the frontend, data, or GTM tooling?

On the rider side, Waymo ships native iOS and Android apps (the Waymo One app) for booking, plus an in-vehicle screen experience. The consumer product surface is mobile-first, consistent with a ride-hailing service.

On data, Waymo's ML pipelines use TensorFlow with data stored and processed in Google Cloud (Cloud Storage, and BigQuery for analytics-scale work is a reasonable read given the Alphabet stack). Public signals don't reliably expose Waymo's internal GTM/CRM tooling, so we don't list specific sales or marketing vendors — only technologies with a real public signal are included here.

What Waymo's stack means if you sell to them

Waymo is a deeply technical, build-heavy Google-native shop: a strong C++/Python/TensorFlow core on Google Cloud, with custom hardware. For vendors, that means a high build-vs-buy bar on anything close to the autonomy stack or core infrastructure — pitching to replace internal ML or compute is a hard sell.

The realistic openings are adjacent to the core: rider-facing product analytics, payments, support/CX tooling, marketing and growth, fleet/operations software, and security/compliance — areas that scale with rides and cities rather than with the self-driving research. Because it's an Alphabet subsidiary, expect strong Google Cloud alignment and Google-grade security review, so integrations and pitches that complement (not displace) Google Cloud will land better.

As of June 2026.Sources:GitHub — Waymo Open DatasetPyPI — waymo-open-dataset (TensorFlow)

Waymo — frequently asked questions

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