The Top 20 Docker Containers for Software Development in 2026
Introduction
Docker containers have fundamentally transformed the landscape of software development and deployment. By encapsulating applications and their dependencies into lightweight, portable units, containers enable developers to build, ship, and run software consistently across any environment. The adoption of Docker has skyrocketed in recent years, with a 2022 survey by Datadog revealing that 59% of organizations use Docker in production, up from just 29% in 2018.[^1]
As artificial intelligence (AI) and machine learning (ML) workloads become increasingly prevalent, containers offer a compelling solution for packaging and deploying these complex applications. Containers provide the isolation, reproducibility, and scalability necessary for AI/ML development and deployment. A 2023 report by Gartner predicts that by 2025, 75% of organizations will be running containerized AI/ML workloads in production, up from less than 10% in 2021.[^2]
In this article, we‘ll explore the top 20 Docker containers that every software developer should know in 2024, with a particular focus on containers that enable AI/ML development. We‘ll cover key categories such as web servers, databases, programming frameworks, tools for machine learning and deep learning, and more. Along the way, we‘ll provide examples, best practices, and insights from Docker experts to help you get the most out of containers.
Web Servers and Content Management
Nginx
Nginx is a high-performance web server, reverse proxy, and load balancer that has gained widespread adoption in containerized environments. Its event-driven, asynchronous architecture allows it to handle large volumes of traffic with minimal resource consumption. In a benchmark by Dropbox, Nginx was able to serve over 50,000 requests per second on a single container.[^3] Nginx is commonly used as a frontend proxy for web applications and APIs, providing features such as SSL termination, caching, and request routing.
Apache HTTP Server
Apache HTTP Server is a powerful, flexible web server that has been a mainstay of the open-source community for over 25 years. While not as lightweight as Nginx, Apache offers a vast ecosystem of modules and configurations that make it suitable for a wide range of web applications. Apache‘s modular architecture allows developers to customize the server to their specific needs, such as enabling server-side scripting languages like PHP or integrating with content management systems like WordPress.
WordPress
WordPress is the most popular content management system in the world, powering over 40% of all websites.[^4] WordPress can be easily containerized using the official WordPress Docker image, which includes the PHP runtime and Apache web server. By running WordPress in a container, developers can create portable, reproducible environments for building and testing WordPress sites. Containers also simplify the process of deploying WordPress in production, enabling easy scaling and updates.
Databases
MySQL
MySQL is the most widely used open-source relational database, known for its simplicity, reliability, and performance. According to the Stack Overflow Developer Survey 2022, MySQL is the second most popular database among professional developers, used by 47.5% of respondents.[^5] Running MySQL in a container provides a consistent, isolated environment for development and testing, and enables easy scaling and replication in production.
PostgreSQL
PostgreSQL is a powerful, open-source object-relational database that offers advanced features such as ACID compliance, transactional integrity, and support for complex data types. PostgreSQL is often used for large-scale, high-volume applications that require strong consistency and durability guarantees. In a benchmark by EnterpriseDB, PostgreSQL was able to handle over 1 million transactions per second on a 64-core server.[^6] Running PostgreSQL in a container simplifies the process of deploying and managing the database across different environments.
MongoDB
MongoDB is a popular NoSQL document database that provides horizontal scalability, flexible data models, and rich query language. MongoDB is often used for web applications, content management systems, and real-time analytics. According to the DB-Engines database ranking, MongoDB is the most popular NoSQL database, with a popularity score more than double that of its nearest competitor.[^7] Running MongoDB in a container enables easy deployment, scaling, and portability across different environments.
Redis
Redis is an open-source, in-memory data structure store that is commonly used as a caching layer, message broker, and real-time analytics engine. Redis supports a variety of data structures, including strings, hashes, lists, sets, and sorted sets, and provides high performance and low latency. In a benchmark by Redis Labs, Redis was able to process over 1 million requests per second with sub-millisecond latency.[^8] Running Redis in a container simplifies deployment and enables easy scaling and high availability.
Search and Analytics
Elasticsearch
Elasticsearch is a distributed, RESTful search and analytics engine that provides real-time search, aggregations, and analytics capabilities. Elasticsearch is commonly used for log analytics, full-text search, and real-time monitoring and alerting. According to the DB-Engines database ranking, Elasticsearch is the most popular search engine, with a popularity score more than triple that of its nearest competitor.[^9] Running Elasticsearch in a container simplifies deployment and scaling, and enables integration with other containerized tools such as Logstash and Kibana.
Programming Frameworks and Tools
Node.js
Node.js is a popular JavaScript runtime built on the Chrome V8 JavaScript engine, enabling developers to build scalable, high-performance web applications and APIs. Node.js provides an event-driven, non-blocking I/O model that makes it well-suited for real-time, data-intensive applications. According to the Stack Overflow Developer Survey 2022, Node.js is the most popular web framework among professional developers, used by 47.1% of respondents.[^5] Running Node.js applications in containers provides consistency, portability, and ease of deployment across different environments.
Python
Python is a versatile, high-level programming language that is widely used for web development, scientific computing, data analysis, and machine learning. Python provides a rich ecosystem of libraries and frameworks, such as Django for web development, NumPy and Pandas for data manipulation, and scikit-learn and TensorFlow for machine learning. Running Python applications in containers enables easy packaging and deployment of dependencies, and provides reproducibility and portability across different environments.
TensorFlow
TensorFlow is an open-source machine learning framework developed by Google, used for building and deploying ML models. TensorFlow provides a comprehensive ecosystem of tools and libraries for data preprocessing, model training, and deployment, and supports a wide range of ML tasks, from image classification to natural language processing. Running TensorFlow in a container simplifies the process of managing dependencies and enables reproducibility and portability of ML workflows.
PyTorch
PyTorch is an open-source machine learning library developed by Facebook, used for building and training deep learning models. PyTorch provides a dynamic computational graph and a simple, expressive API for defining complex neural networks. PyTorch is known for its ease of use, flexibility, and strong community support. Running PyTorch in a container enables easy management of dependencies and reproducibility of ML experiments.
Continuous Integration and Deployment
Jenkins
Jenkins is a popular open-source automation server used for continuous integration and delivery (CI/CD) of software applications. Jenkins provides a wide range of plugins and integrations for building, testing, and deploying applications across different platforms and environments. Running Jenkins in a container simplifies the process of setting up and managing the CI/CD pipeline, and enables scalability and fault tolerance.
GitLab
GitLab is a web-based DevOps platform that provides a complete set of tools for managing the software development lifecycle, from version control to CI/CD and monitoring. GitLab provides a containerized architecture that enables easy deployment, scaling, and management of the platform. Running GitLab in a container simplifies the process of setting up and managing a self-hosted Git repository and CI/CD pipeline.
Monitoring and Logging
Prometheus
Prometheus is an open-source monitoring and alerting system that provides a flexible, scalable, and reliable way to collect and analyze metrics from containerized applications and infrastructure. Prometheus uses a pull-based model to scrape metrics from instrumented applications and services, and provides a powerful query language and visualization capabilities. Running Prometheus in a container simplifies deployment and scaling, and enables integration with other containerized tools such as Grafana.
Grafana
Grafana is an open-source data visualization and monitoring platform that provides a flexible and customizable way to create and share dashboards and alerts. Grafana integrates with a wide range of data sources, including Prometheus, Elasticsearch, and InfluxDB, and provides a rich set of visualizations and annotations. Running Grafana in a container simplifies deployment and enables easy scaling and integration with other containerized tools.
GPU-Accelerated Containers
GPU-accelerated containers are a powerful tool for accelerating AI/ML workloads and other compute-intensive applications. By leveraging the parallel processing capabilities of GPUs, developers can achieve significant speedups in model training and inference. Popular GPU-accelerated containers include NVIDIA GPU Cloud (NGC), which provides a catalog of pre-built, optimized containers for deep learning and scientific computing, and AMD ROCm, which provides a containerized platform for GPU-accelerated computing on AMD GPUs.
Conclusion
Docker containers have revolutionized the way software is developed, packaged, and deployed, providing a flexible, scalable, and reproducible way to build and run applications across any environment. As AI and ML workloads become increasingly prevalent, containers offer a compelling solution for managing the complexity and scale of these applications.
In this article, we‘ve explored the top 20 Docker containers for software development in 2024, covering key categories such as web servers, databases, programming frameworks, machine learning tools, and more. We‘ve provided examples, best practices, and insights from Docker experts to help you get the most out of containers in your own projects.
As the adoption of containers continues to grow, it‘s clear that they will play an increasingly important role in the future of software development. By leveraging the power of containers, developers can build more efficient, portable, and scalable applications that can run consistently across any environment. Whether you‘re building a web application, a machine learning model, or a large-scale data processing pipeline, Docker containers provide a powerful tool for simplifying and accelerating your development workflow.
[^1]: Datadog. (2022). Docker Adoption Statistics.[^2]: Gartner. (2023). Forecast Analysis: Containers as a Service, Worldwide.
[^3]: Dropbox. (2022). How Dropbox Supercharged Its Web Servers with Nginx.
[^4]: W3Techs. (2023). Usage statistics of content management systems.
[^5]: Stack Overflow. (2022). Developer Survey Results.
[^6]: EnterpriseDB. (2023). Achieving High Transaction Rates with PostgreSQL.
[^7]: DB-Engines. (2023). DB-Engines Ranking of Relational Databases.
[^8]: Redis Labs. (2022). Redis Benchmark Results.
[^9]: DB-Engines. (2023). DB-Engines Ranking of Search Engines.
