MavenUp Technology Stack — Languages, Frameworks & Tools
The technologies we use to build custom software, AI systems, and web applications — with a short explanation of why each one is in the stack.
We choose tools based on what fits the project requirements, not what's trending. A new framework isn't worth the risk if a mature one solves the problem reliably. That said, we stay current — most of our production applications run on technologies released or substantially updated in the last three years.
Languages & Frameworks
Used for application logic, user interfaces, and server-side services.
TypeScript / JavaScript
Primary language for web and Node.js backends. TypeScript adds static typing that reduces runtime errors in large codebases.
Python
Used for AI/ML pipelines, data processing, scripting, and backend APIs where the scientific library ecosystem (NumPy, Pandas, PyTorch) matters.
Java
Enterprise backend services, Android apps, and systems requiring strong typing, mature tooling, and long-term maintainability.
PHP
WordPress, Drupal, and Laravel projects where PHP is the right tool for the existing ecosystem.
React / Next.js
Default for new web applications. Next.js adds server-side rendering and static generation, which matters for SEO and initial load performance.
Vue.js
Selected when clients prefer a lighter learning curve or have existing Vue codebases. Good fit for mid-size dashboards and internal tools.
React Native
Cross-platform mobile apps that share 70%+ of code between iOS and Android. Chosen over Flutter when the web team needs to contribute to mobile.
Node.js
API servers, real-time applications, and backend services where JavaScript on the server reduces context switching for full-stack teams.
Django / FastAPI
Python web frameworks used for API backends in AI-heavy projects and data-driven applications.
Laravel
PHP framework for rapid application development and CMS-backed web apps when full PHP control is needed.
Platforms & Cloud
Infrastructure, hosting, deployment, and DevOps tooling.
AWS
Primary cloud for production workloads. Lambda, EC2, RDS, S3, and CloudFront cover most architecture needs. Used when clients require enterprise-grade SLAs.
Google Cloud
Preferred for AI and ML workloads. Vertex AI, BigQuery, and Google's AI APIs integrate directly with Python-based data pipelines.
Azure
Required for clients in Microsoft-heavy enterprise environments. Strong integration with Active Directory, SharePoint, and .NET ecosystems.
Vercel
Front-end deployment for Next.js applications. Zero-config CI/CD and edge network give fast global performance with minimal DevOps overhead.
Docker
Consistent environments from development through production. Every application we build is containerized to eliminate environment-specific bugs.
Kubernetes
Container orchestration for applications that need auto-scaling, rolling deployments, or multi-service architecture management.
Supabase
Postgres-backed backend-as-a-service. Used for applications needing auth, real-time subscriptions, and storage without building backend infrastructure from scratch.
Netlify
Static site deployments and JAMstack projects where build-time rendering is preferred over server-side.
Databases & Data Tools
Data storage, retrieval, and query tooling used in production systems.
PostgreSQL
Default relational database for most applications. ACID compliance, JSON support, and mature ecosystem make it the safest choice for production data.
MySQL / MariaDB
Used for WordPress-based projects and legacy system integrations where MySQL is the existing standard.
MongoDB
Document storage for applications with flexible schemas — product catalogs, content management, and event logs.
Redis
Caching, session storage, and real-time pub/sub messaging. Reduces database load and enables sub-millisecond response times for hot data.
Elasticsearch
Full-text search and log analytics for applications that need fast search across large datasets.
Prisma
Type-safe ORM for Node.js. Reduces boilerplate database code and provides compile-time query validation.
How We Choose the Stack for Your Project
Every project starts with requirements, not preferences. If you have an existing codebase, we work within it. If you're starting from scratch, we recommend a stack based on your team's future maintainability needs, your deployment environment, your expected traffic scale, and whether you'll need mobile alongside web.
The one thing we don't do is use novel technology to make a project sound more impressive. Postgres and React have solved the same problems reliably for a decade. We use them when they fit. We use newer tools when they materially reduce complexity or cost.