Tool Categories for Tech Stack Building
Tool categories in the Tekyous catalogue — from frontend frameworks and databases to AI builders and LLMs — with descriptions and subcategories.
Every tool in the Tekyous catalogue belongs to one category. Categories serve three purposes on the platform: they let you filter the catalogue by area of the stack, they group tools into columns when you use the organised layout in the stack builder, and they structure the tool picker drawer when you are adding tools to the canvas.
Most categories contain subcategories — finer groupings that distinguish, for example, Node.js backend frameworks from Python ones, or OLTP databases from document stores. Subcategories appear in the organised canvas layout when you enable the detailed view, and each has its own catalogue page.
There are currently 19 categories.
Frontend Frameworks
JavaScript and TypeScript frameworks for building user interfaces. Frontend frameworks handle the component model, state management, and rendering of the client-side layer — from single-page applications to server-rendered pages and static sites.
Subcategories
- JS Frontend Frameworks — Component-based UI frameworks built on JavaScript and TypeScript, used to build the interactive layer of web applications. React, Vue, and Svelte are examples of this type.
- JS Metaframeworks — Full-stack frameworks layered on top of a UI framework, adding server-side rendering, static generation, file-based routing, and deployment integration. Next.js, Nuxt, SvelteKit, and Astro are in this group.
- CSS Frameworks — CSS libraries and design systems for styling web UIs, ranging from utility-first approaches like Tailwind CSS to component-based libraries that provide pre-styled elements.
- HTML-first Frameworks — Libraries that add interactivity with minimal JavaScript, keeping HTML as the primary interface layer rather than replacing it with a component tree. HTMX is the main example in this space.
- Mobile Frontend Frameworks — Cross-platform frameworks for building native mobile UIs from a shared codebase, targeting both iOS and Android. React Native and Flutter are the two dominant options.
- Browser Extension Frameworks — Tooling for building browser extensions following the Manifest V3 API, which structures extensions into content scripts, background service workers, and extension pages.
Backend Frameworks
Server-side frameworks and runtime environments for building APIs and web applications. Backend frameworks provide the structural layer of the server — routing, middleware, request handling, and conventions for organising application logic.
Subcategories
- Python Backend Frameworks — Server-side frameworks for Python, covering both lightweight API-focused tools like FastAPI and Flask, and full-featured web frameworks like Django.
- Node.js Frameworks — Server-side frameworks running on the Node.js runtime, ranging from minimal HTTP layers like Express to structured application frameworks like NestJS and Fastify.
- Go Frameworks — Lightweight HTTP frameworks for building performant Go web services. Gin is the most widely used option in the catalogue.
- Ruby Frameworks — Server-side frameworks for Ruby. Ruby on Rails follows a convention-over-configuration approach and is the primary option in this space.
- PHP Frameworks — Backend frameworks for PHP, with Laravel as the dominant option offering a full-stack ecosystem including ORM, queue handling, and templating.
- Java Frameworks — Enterprise-grade backend frameworks for Java, with Spring Boot as the standard for production Java web services.
- Rust Frameworks — Web frameworks that leverage Rust's performance and memory safety guarantees. Axum is a commonly used async web framework in this space.
- JavaScript Runtimes — Server-side runtimes for executing JavaScript and TypeScript. Node.js is the established standard and the runtime underlying most frameworks in this catalogue.
Programming Languages
General-purpose and domain-specific languages used to write application logic. The language choice determines the runtime, ecosystem, and available frameworks — and often constrains which backend libraries and tooling are relevant to a project.
Databases
Systems for storing, querying, and managing data. The database choice is one of the most consequential decisions in a stack — it affects query patterns, scalability approach, consistency model, and how the rest of the backend integrates with stored data.
Subcategories
- OLTP Databases — Relational databases designed for transactional workloads — fast reads and writes with strong consistency. PostgreSQL, MySQL, and SQLite are the primary options in this group.
- NoSQL / Document — Document stores and schemaless databases suited to semi-structured or variable-shape data. MongoDB is the most widely used standalone option; Firebase includes a document database as part of its platform.
- Backend as a Service — Managed platforms that combine a database, authentication, storage, and real-time subscriptions into a single hosted service. Supabase and Firebase are the main examples.
- Vector Databases — Databases optimised for storing and querying high-dimensional vector embeddings — the storage layer behind semantic search, retrieval-augmented generation, and recommendation systems.
- OLAP Databases — Columnar databases optimised for analytical queries over large datasets. ClickHouse, DuckDB, and BigQuery are examples of this type.
- Key-Value Stores — In-memory and persistent key-value stores used for caching, session management, and pub/sub messaging. Redis is the dominant tool in this category.
- In-Process SQL — Embedded SQL engines that run inside the application process rather than as a separate server — well-suited to local analytics, testing, and edge deployments. DuckDB and SQLite are the two main options.
Hosting & Cloud
Platforms for deploying and running applications in the cloud. Covers everything from high-level PaaS platforms that abstract away infrastructure to full cloud providers and self-managed virtual servers that give complete control over the environment.
Subcategories
- App Hosting — PaaS and serverless platforms for deploying web applications without managing servers directly. Vercel, Render, and Railway are common options in this space.
- Cloud Providers — Full-service cloud infrastructure platforms offering compute, storage, networking, and managed services at scale. AWS, Google Cloud, and Azure are the main providers in the catalogue.
- VPS & Servers — Self-managed virtual private servers for teams that want full control over their infrastructure without the abstraction of a PaaS layer.
AI Builders
AI-powered platforms that generate full-stack applications from natural language prompts. These tools handle scaffolding, code generation, and in some cases deployment — significantly compressing the time from idea to working prototype, particularly for non-technical users and early-stage exploration. Bolt.new, Lovable, v0, and Replit are examples in this category.
No-Code / Low-Code
Visual development platforms that enable building apps, websites, and workflows without writing code. These tools range from drag-and-drop website builders to full application development platforms capable of handling logic, data storage, and third-party integrations.
Subcategories
- Website Builders — Drag-and-drop platforms for building websites and landing pages visually, without a separate frontend codebase. Webflow, Framer, and Squarespace are examples in this space.
- No-Code Backend — Visual platforms for managing data, defining APIs, and building backend logic without code. Airtable and Xano are the main options in the catalogue.
- E-commerce Platforms — Managed platforms for building and running online stores, handling product catalogues, checkout, and order management. Shopify and WooCommerce are the primary options in this group.
- Mobile App Builders — Visual tools for creating native mobile apps without writing code from scratch, targeting iOS and Android from a shared interface. Adalo and FlutterFlow are examples in the catalogue.
Development Tools
The personal developer workflow toolkit — editors, coding assistants, and version control. These tools shape how developers write, review, and manage code day-to-day, and are largely independent of the project's tech stack.
Subcategories
- IDEs & Editors — Code editors and integrated development environments where developers write and navigate code. Options range from lightweight editors like VS Code and Zed to more feature-heavy IDEs like PyCharm.
- AI Coding Agents — AI-powered tools that assist with writing, editing, and reviewing code — from inline autocomplete to autonomous agents that can plan and execute multi-step coding tasks. Claude Code and Codex are examples in this space.
- Version Control — Code hosting and collaboration platforms built on version control systems, enabling teams to manage history, review changes, and coordinate development. GitHub and GitLab are the two dominant options.
Authentication
Services and libraries for handling user identity, login, and access control. Authentication is a cross-cutting concern in almost every application with users — covering session management, token issuance, social logins, and multi-factor authentication, either as a managed service or a self-hosted solution. Auth0, Clerk, and BetterAuth are examples in this category.
Automation & Integration
Platforms and tools for automating workflows and connecting services. Used to trigger actions between systems, sync data, send notifications, and run scheduled tasks — without building custom integrations from scratch. Both no-code visual workflow builders and code-based automation tools fall into this category. Zapier, Make, and n8n are examples in this space.
APIs & Infrastructure
Third-party APIs, infrastructure tooling, and platform services that extend the stack. Covers payment processing, infrastructure-as-code, CDN and edge services, and API layer tooling that sits outside the core application code.
Subcategories
- Payment Processors — APIs and platforms for accepting payments, managing subscriptions, and handling billing logic. Stripe is the most widely used option in this category.
- Infrastructure — Infrastructure-as-code tools and cloud networking services for managing and provisioning cloud resources programmatically. Cloudflare is a key example, covering CDN, DNS, edge networking, and serverless compute.
- GraphQL Engines — Servers and platforms that generate or manage a GraphQL API layer over your data sources. Hasura and PostGraphile are the primary options in the catalogue.
DevOps & CI/CD
Tooling for containerisation, deployment, and automated pipelines. Covers the operational layer of a project — how code gets packaged, tested, and shipped to production, and how containers and services are managed at runtime.
Subcategories
- Containerization — Tools for packaging applications into containers and orchestrating them at scale. Docker handles the container runtime and image layer; Kubernetes manages container orchestration across clusters.
- CI/CD Pipelines — Platforms for automating build, test, and deployment workflows — typically triggered on code push or pull request events, reducing manual steps in the path to production.
Data Engineering & ETL
Tools for building data pipelines — ingesting, transforming, and moving data between systems. This category covers the infrastructure layer of data-oriented architectures: orchestration, ingestion, transformation, warehousing, and streaming.
Subcategories
- Orchestration — Workflow schedulers and pipeline orchestration frameworks that define, schedule, and monitor data jobs. Apache Airflow, Prefect, and Dagster are the main options in this space.
- Extract & Load — Tools for ingesting and replicating data from source systems into a data warehouse or analytical store. Fivetran and Airbyte are the primary options respectively.
- Transformation — SQL-based transformation frameworks that define and test data models as version-controlled code, applied after data has been loaded into a warehouse. dbt is the widely adopted standard in this space.
- Cloud Data Platforms — Managed data warehouse and lakehouse platforms for storing and querying large analytical datasets. Databricks and BigQuery are the main options in the catalogue.
- Streaming — Event streaming and message queue infrastructure for real-time data ingestion and processing. Apache Kafka is the established standard; Redpanda is a Kafka-compatible alternative with a lighter operational footprint.
Data & ML Libraries
Python libraries for data manipulation, machine learning, and model development. These are the computational building blocks of data science and ML workflows — used in scripts, notebooks, pipelines, and training jobs rather than as standalone applications.
Subcategories
- Data Processing — Libraries for data manipulation, transformation, and numerical computation. Pandas, Polars, and NumPy cover the core operations needed to prepare data for analysis or model training.
- ML Frameworks — Deep learning and machine learning libraries for defining, training, and evaluating models. PyTorch, TensorFlow, and scikit-learn represent the main options across deep learning and classical ML.
- ML Operations — Tools for experiment tracking, model versioning, and monitoring model performance in production. MLflow and Weights & Biases are the primary options in this group.
Data Apps
Frameworks for building interactive data-driven applications and dashboards in code. These tools sit between a pure data library and a full frontend framework — designed for analysts and data scientists who need to build UIs and expose data outputs without deep frontend expertise. Streamlit, Gradio, and Dash are examples in this category.
BI & Analytics
Platforms and tools for analysing data and building reports and dashboards. BI tools are primarily aimed at business users and analysts who need to explore data and create visualisations without writing application code — though the line between BI platforms and code-based data apps is increasingly blurred.
Subcategories
- BI Tools — Business intelligence and dashboarding platforms for building charts, reports, and self-serve analytics. Metabase, Tableau, and Power BI are examples in this category.
- Notebooks — Interactive computing environments that combine code, outputs, and narrative in a single document, primarily used for data exploration and analysis. Jupyter Notebook is the most widely used format in this space.
- Spreadsheets — Spreadsheet tools used for data analysis, reporting, and lightweight automation. Microsoft Excel and Google Sheets are the two primary options in the catalogue.
CMS
Content management systems for creating, editing, and publishing digital content. CMS platforms manage the authoring workflow and content storage — and either render the frontend themselves (classic CMS) or expose content via API for a decoupled frontend (headless CMS).
Subcategories
- Classic CMS — Traditional content management platforms that bundle the editing interface and frontend rendering together. WordPress is by far the most widely used option in this category.
- Headless CMS — API-first content platforms that decouple content from presentation, exposing structured content via API for any frontend to consume. Contentful, Sanity, and Strapi are examples in this space.
Observability & Monitoring
Tools for understanding what is happening in production systems. Covers application performance monitoring, error tracking, infrastructure metrics, logging, and user behaviour analytics — the visibility layer for both technical and product teams.
Subcategories
- Infrastructure & APM — Application performance monitoring and infrastructure metrics tools for tracking system health, error rates, and latency in production. Datadog, Grafana, and Sentry are examples in this group.
- Web & Product Analytics — Tools for tracking user behaviour, product usage, and conversion — helping teams understand how their product is actually being used. PostHog is a privacy-friendly open-source option; Plausible and Umami are lightweight alternatives.
LLM
Large language model providers and foundation models used to add AI capabilities to applications. LLMs serve as the inference layer for natural language understanding, generation, and reasoning — accessed either via a hosted API or by running open-weight models directly on your own infrastructure.
Subcategories
- Proprietary — Hosted large language models accessible via API, offered by commercial providers. OpenAI and Google Gemini are the leading options; Claude from Anthropic is also in this group.
- Open-weight — Open-weight models available for self-hosting, fine-tuning, and local deployment. Meta Llama, Mistral, and Qwen are examples in this category.
You can browse any category from the tools catalogue, or open a category's tools directly in the stack builder sandbox. For a broader overview of how categories fit into the platform, see the How Tekyous Works guide.
