What tech stack does Meta use?

Meta's primary backend language is Hack — a statically typed superset of PHP that Meta's engineers built to modernize the PHP codebase at scale while maintaining compatibility with a massive existing code base. C++ is used for performance-critical infrastructure systems where latency tolerance is in the microsecond range. Python dominates the machine learning and data pipeline layer, and Java is used for certain backend services, particularly those with Android or older infrastructure heritage.

Meta's infrastructure is almost entirely proprietary and self-hosted. Key purpose-built systems include TAO (the distributed social graph database powering News Feed and friend graph at billions of edges), ZippyDB (a strongly consistent distributed key-value store), Tectonic (a distributed file system designed for exabyte-scale storage across data center racks), and Twine (the cluster management system that orchestrates millions of machines globally). Additional proprietary layers include Service Router (internal RPC and service mesh), Taiji (traffic load balancing), and Delos (a distributed control plane for meta-operations). Meta designs its own servers and racks through contributions to the Open Compute Project, and operates its own globally distributed data center network — it does not rely on AWS, GCP, or Azure for core workloads.

For AI training and inference, Meta runs NVIDIA H100 and Blackwell GPU clusters alongside its own MTIA (Meta Training and Inference Accelerator) custom silicon deployed at scale for ranking and recommendation workloads — primarily serving ads. The company is also a significant buyer of AMD MI300X GPUs. A new Meta Compute division was established in 2026 to manage the expanding AI data center footprint, including the Louisiana gigawatt-scale campus.

React — invented at Facebook and open-sourced in 2013 — is Meta's UI framework for all web surfaces and remains the most widely used JavaScript UI framework in the world. React Native, also created at Meta and open-sourced in 2015, powers mobile apps on iOS and Android through a shared JavaScript codebase layered over native iOS (Objective-C/Swift) and Android (Kotlin) rendering. GraphQL, invented at Meta and open-sourced in 2015, is the API query language used across Meta's entire app layer and has become an industry standard for client-defined data fetching.

For AI and ML, Meta's canonical framework is PyTorch, open-sourced in 2016 in collaboration with Caffe2 and now the most widely used deep learning framework in academic and commercial AI research globally. GPU kernel optimization for custom workloads runs through Triton, an open-source GPU programming language originally from OpenAI but adopted and contributed to by Meta engineering. Meta's large language model family is Llama 4 (Scout, Maverick, and Behemoth, launched April 5, 2025), used internally to power Meta AI assistant features across all apps and as the model layer underpinning Advantage+ creative and optimization systems.

For GTM and advertising technology, Meta's most commercially significant internal tool is Advantage+ — its AI-powered ad automation platform built entirely on Meta's own ML infrastructure, including Llama-family models for creative generation and its proprietary ranking models for audience optimization. Advantage+ represents both a product (sold to advertisers) and a platform capability (differentiating Meta's inventory from competitors by delivering measurably better advertiser ROI).

Meta's build-over-buy posture is among the most deeply ingrained in enterprise technology — the company invented React, PyTorch, GraphQL, Hack, and TAO rather than adopt external alternatives, and its engineering culture celebrates internal building as a competitive differentiator. Software displacement at the core infrastructure or ML framework layer is nearly impossible: no vendor is displacing PyTorch or Hack at Meta. Successful vendor entries are typically in areas where speed-to-market, compliance burden, or highly specialized domain expertise makes external purchase faster than internal development.

Categories that have proven viable for external vendors at Meta include: security and compliance tooling (where regulatory requirements and audit mandates create external dependency), HR, workforce, and recruiting platforms (where enterprise SaaS scale-economy arguments hold), developer productivity and observability tools (where Meta has occasionally adopted best-in-class external solutions rather than reinventing), and specialized data tooling in emerging domains. Integration pitches that reference Meta's open-source stack — PyTorch-native integrations, React-compatible SDKs, GraphQL API compatibility — gain technical credibility during engineering evaluation.

The 2026 AI CapEx buildout ($125–145 billion) creates large procurement opportunities primarily for hardware vendors (NVIDIA, AMD, networking OEMs, power infrastructure, data center construction) and energy infrastructure firms — categories where Meta buys rather than builds. For enterprise SaaS vendors, the most effective motion is to identify a functional domain where Meta has not invested engineering capacity, map the specific team and budget owner, and enter with a proof of concept that minimizes Meta's security and compliance review burden.