What tech stack does Grammarly use?

Grammarly's backend is polyglot by design, reflecting the different latency, concurrency, and throughput requirements across its product surface. Java and JVM-based services handle core business logic and API layers. Go is used for high-throughput microservices requiring concurrency. Python powers ML training pipelines and NLP inference. Erlang handles concurrent, fault-tolerant messaging components. Most unusually, Grammarly runs Common Lisp in production for parts of its NLP rule engine — a choice documented on the engineering blog, valued for macro-based metaprogramming and rapid iteration on linguistic rules without recompilation cycles.

Infrastructure is primarily AWS, deployed across multiple regions to minimize inference latency for a global user base. The architecture uses AWS ALBs, CloudFront for edge distribution, and a private network for all user-data processing. Kubernetes orchestrates containerized services; NGINX sits at the edge layer. The company holds SOC 2 Type II and ISO 27001 certifications attesting to the security rigor of this infrastructure. Recent engineering blog posts from January and March 2025 document upgrades to the ML infrastructure for research experimentation and expansion of on-device AI model deployment — reducing latency and network dependency for mobile users.

The frontend is JavaScript/TypeScript with React, but the Grammarly team developed a proprietary editor framework called Embrace to handle the unique demands of an AI-assisted text editor — real-time annotation overlays, suggestion rendering, and cursor management at typing speed. The monorepo is managed with Bazel for deterministic, scalable builds across a large polyglot codebase. Browser extensions leverage standard Web Extension APIs built on this same Embrace framework for consistency across Chrome, Firefox, Safari, and Edge.

On the data side, PostgreSQL serves as the primary relational store; Redis handles caching and session data. A February 2025 engineering post documented work on reducing text input lag for web performance, indicating ongoing investment in client-side rendering optimization. Google Analytics is confirmed for web analytics; G Suite (Google Workspace) is used for internal productivity. Specific CRM and sales-engagement vendors are not publicly disclosed — Salesforce and common outbound tools are plausible given the enterprise sales motion but remain unconfirmed from public signals.

Grammarly's heavy AWS reliance makes AWS Marketplace listings and AWS-native integrations a natural GTM angle for infrastructure vendors. The multi-region, Kubernetes-based architecture signals appetite for container-native observability, security scanning, and cloud cost management tooling. Any vendor in the APM, security, or DevOps space has a well-matched conversation if they can demonstrate AWS-native depth.

The polyglot backend (Java, Go, Python, Erlang, Common Lisp) means Grammarly's engineering team is sophisticated and unlikely to be impressed by generic developer tools — pitches must speak to specific stack compatibility and real-world performance at NLP-inference scale. Grammarly's build-vs-buy posture leans toward building proprietary solutions for core AI capabilities (evidenced by Embrace and the Lisp NLP engine) while buying commodity infrastructure and GTM tools. Vendors should position around integration depth, AI-native features, and proven scale — not generic productivity claims. With the Coda and Rows integrations underway, vendors in the collaborative data and workflow automation space may also find expanded budget lines as the Superhuman product suite matures.