What tech stack does Alphabet use?
Alphabet builds on one of the most sophisticated in-house technology stacks in the world. Officially supported languages at Google include C++, Java, Kotlin, Python, Go, and TypeScript, running atop proprietary infrastructure the company invented — Borg, Spanner, BigQuery, and the protocols (Protobuf, gRPC) much of the industry later adopted. This profile is detected from public sources (the engineering blog, open-source projects, StackShare, and job posts), so treat it as directional rather than a confirmed internal inventory.
- Backend
- C++, Java, Go, Python
- Frontend
- TypeScript, Angular
- Cloud
- Google Cloud Platform (own)
- Data
- Bigtable, Spanner, BigQuery
- Orchestration
- Borg / Kubernetes
- Mobile
- Android (Kotlin/Java)
Detected technologies
Languages, infrastructure, and data systems Google uses or originated, grouped by layer.
- C++· Backend
- Java· Backend
- Go· Backend
- Python· Backend
- TypeScript· Frontend
- Angular· Frontend
- Kotlin· Mobile
- Android· Mobile
- Borg· Infrastructure
- Kubernetes· Infrastructure
- Protocol Buffers (Protobuf)· Infrastructure
- gRPC / Stubby· Infrastructure
- Bigtable· Data
- Spanner· Data
- BigQuery· Data
- TensorFlow / JAX· AI
- TPUs (custom silicon)· AI
Sources:Inside Google's engineering tech stack (Pragmatic Engineer)From Google Borg to Kubernetes (Happtiq)
What does Alphabet use on the backend and infrastructure?
Google's backend is built primarily in C++, Java, Go, and Python. C++ powers performance-critical systems (search serving, Chrome, networking); Go was created at Google specifically for scalable systems software and underpins much of its cloud and Kubernetes tooling; Java and Python cover services, data work, and tooling.
The infrastructure layer is overwhelmingly proprietary and self-built. Google runs workloads on Borg (the cluster manager that inspired Kubernetes, which Google open-sourced in 2014), communicates between services with Protocol Buffers and gRPC/Stubby, and stores data in systems it invented — Bigtable, Spanner, and Colossus — all running on its own global data-center fleet rather than third-party cloud.
What does Alphabet use on the frontend, data, and AI tooling?
On the frontend, Google relies heavily on TypeScript and Angular (which it created and maintains), along with web frameworks and design systems built in-house for products like Search, Gmail, and Workspace. Android development uses Kotlin and Java.
For data and analytics, the stack centers on BigQuery (its serverless data warehouse), Bigtable, and Spanner. On AI, DeepMind and Google build on TensorFlow and JAX, serve models via Vertex AI, and run training and inference on Google's custom TPU accelerators rather than relying solely on third-party GPUs — a key signal of how deeply Alphabet builds its own stack, and the focus of much of its $180-190 billion 2026 capex.
What Alphabet's stack means if you sell to them
Alphabet is the textbook 'build-over-buy' account. It invented much of the modern infrastructure stack (Kubernetes, Protobuf, gRPC, Spanner) and runs on its own cloud, its own databases, and its own AI chips — so wholesale-replacement pitches for core infrastructure rarely land.
The pitches that work are specialization and augmentation: tools that integrate with its Go/Python/C++ and Kubernetes-native environment, that complement (not replace) BigQuery or Vertex AI, or that address niches the in-house teams don't prioritize.
Because the stack here is detected from public engineering sources and open-source projects, confirm specifics with the relevant product team during discovery rather than assuming a uniform company-wide standard.
As of June 2026.Sources:Inside Google's engineering tech stack (Pragmatic Engineer)What code is Google written in? (DesignGurus)
Alphabet — frequently asked questions
