DigitalOcean vs Azure in 2026: Key Features and Pricing

When choosing a cloud platform, it’s about more than picking the most recognizable hyperscaler cloud on the market. Switching services later and initiating a cloud migration can be a hassle, so it’s essential to consider the option that can grow with your needs while staying within budget.

It’s common to compare DigitalOcean vs Azure when it comes to cloud platform providers. Both offer a suite of cloud-based resources—including compute instances and managed services—but ultimately cater to different user demographics and needs. Azure offers native support for Windows-based virtual machines, making it suitable for enterprises that leverage Microsoft technologies. For open-source users and AI development workflows, DigitalOcean offers a robust, cost-effective alternative.

What is DigitalOcean?

DigitalOcean, founded in 2011, is the go-to choice for users seeking a robust, straightforward cloud platform. Its developer-centric approach shines through in a user-friendly platform that prioritizes simplicity and ease of use, and is the go-to platform for building AI applications and agents. DigitalOcean offers an integrated platform for all of your infrastructure needs, including Droplets for virtual machines, Managed Kubernetes, and scalable cloud storage options, and the DigitalOcean GradientTM AI Agentic Cloud, all designed to enhance developer productivity.

The brand is renowned amongst developers due to its strong community and a wealth of cutting-edge tutorial topics, which facilitate a smooth learning curve, as well as a transparent pricing model with predictable cloud billing.

DigitalOcean highlights:

• Droplets: Scalable virtual machines that offer comprehensive, cost-effective cloud computing for generalized or specialized workloads with CPU and GPU configuration options.

• Storage: Spaces Object Storage for unstructured data like images and videos, as well as Volumes Block Storage for database storage and other I/O-intensive applications, and network file storage.

• Managed Databases: Fully-managed database administration for MongoDB, Kafka, PostgreSQL, MySQL, Valkey, and OpenSearch databases.

• App Platform: A robust PaaS offering to develop and ship applications in one place.

• AI/ML: The DigitalOcean GradientTM AI Agentic Cloud simplifies the AI development process with tools to train models, run inference, and create AI applications.

DigitalOcean features

1. Droplets

DigitalOcean Droplets are Linux virtual machines with computing resources to power your applications, available in various configurations to meet your workload requirements. Options include:

• Basic: for low-traffic applications and small databases.

• General Purpose: for SaaS applications and e-commerce setups.

• Memory-Optimized: for high-performance databases and in-memory caches.

• CPU-Optimized: for media streaming and analytics.

• Storage-Optimized: for NoSQL databases and monitoring software.

• GPU: for AI/ML training and high-performance computing.

Each Droplet comes with monitoring, firewalls, and 500 GB of outbound data transfer per month.

2. Storage

DigitalOcean offers Spaces and Volumes storage types, as well as Network File Storage. Spaces Object Storage stores large amounts of static data with 1TB of outbound data transfer, a built-in global CDN, and an S3-compatible API. It’s designed for high request-per-second use cases, with parameters supporting up to 1500 RPS per client IP address. The CDN can cache assets across 200+ servers worldwide. Volumes Block Storage offers low-latency, NVMe-based storage for Droplets and Managed Kubernetes deployments, making it ideal for distributed web applications, machine learning, big data, data backup, and deduplication. Network File Storage makes it simple to store and scale files for AI/ML workloads, as well as data-intensive applications. It is offered as a managed service for DigitalOcean users on GPU Droplets and DOKS.

3. Managed databases

DigitalOcean offers comprehensive support for all your managed database needs. This includes database migration assistance, database setup, as well as backups and regular updates. Managed databases are highly scalable, include daily backups, provide automated failover with standby nodes, feature end-to-end security, and automatic placement on a VPC network. Available database engines include MongoDB, Kafka, PostgreSQL, MySQL, Valkey, and OpenSearch.

4. App Platform

Supporting full-stack application development, DigitalOcean’s App Platform provides the tools to integrate source code from popular code repository storage providers (including Git, GitLab, and Docker), built-in CI/CD functionality, and resources to autoscale infrastructure as workloads fluctuate over time. Use it to build applications with Node.js, Python, PHP, Ruby, Go, Java, .NET, and Static Sites. The App Platform also integrates with AI-based coding assistants through the DigitalOcean MCP server to interact with your code (i.e. get deployment status, instance size, and available apps) and take actions to run deployments, delete code, or update applications.

5. Gradient AI Agentic Cloud

DigitalOcean offers the Gradient AI Agentic Cloud for AI development, which supports the integration of model training and fine-tuning, AI inference, AI agent creation, and AI workflows. Each agent you create with the Gradient AI Platform comes with knowledge bases for retrieval-augmented generation (RAG), multi-agent routing, and guardrails. Use DigitalOcean hardware (GPU Droplets and Bare Metal) to power workloads with the Gradient AI Platform to access AI model endpoints from OpenAI, Anthropic, and Meta for your serverless inference applications. These endpoints eliminate the need for account management, multiple APIs, or access keys—all management is handled within the Gradient Platform.

What is Microsoft Azure?

Microsoft Azure, released in 2010, is a hyperscaler cloud for large-scale enterprises. It delivers an extensive range of services and an enterprise-grade infrastructure that integrates natively with the Microsoft ecosystem.

Azure has the capability to handle complex, hybrid cloud architectures. However, navigating this introduces a level of complexity that may be excessive for smaller teams. The pricing, although flexible, can be unpredictable and may present a challenge for those who require a more straightforward cost structure with no hidden costs—Azure is known to have fees for outbound storage transfers.

Azure highlights:

• Virtual Machines: Azure virtual machines support Linux and Windows OSes with automatic scaling, built-in monitoring, and backup capabilities.

• Storage: Azure Blob Storage and Data Lake Storage for handling both structured and unstructured data in the cloud.

• Managed databases: Azure databases are available for PostgreSQL, MySQL, Apache Cassandra, and MongoDB.

• Web Apps: For PaaS application development, Azure provides both Static Web Apps and App Service to create full-stack applications and APIs.

• Azure Machine Learning: Azure Machine Learning offers functionality for the entire machine learning lifecycle, supporting generative AI, automated ML, and MLOps use cases.

Azure features

1. Azure virtual machines

Azure VMs run either Linux or Windows OSes to host your applications, from open-source software to high-performance computing. These VMs are available across 60+ global regions and offer a 99.99% availability service-level agreement. Use VM scale sets to scale the number of necessary machines based on specific demand requirements or a defined schedule. There are also options for reserved and hybrid VM instances, which may reduce overall VM costs.

2. Azure storage

Microsoft Azure offers two main storage types: Azure Blob and Data Lake Storage. Azure Blob Storage is a scalable object storage for text and binary data. This makes it ideal for storing files for distributed access, streaming audio and video, data backup, data analysis storage, writing to log files, and serving images or documents into a browser. Data Lake Storage is designed for cloud-based, enterprise-level data storage for massive amounts of data in any format. It supports Hadoop-compatible access, massive scalability, and a hierarchical directory structure.

3. Managed databases

Azure’s managed databases are limited to PostgreSQL, MySQL, Apache, and MongoDB, compared to DigitalOcean’s offerings of MongoDB, Kafka, PostgreSQL, MySQL, Valkey, and OpenSearch. These hyperscale databases can dynamically scale to support application requirements, have multiple layers of threat detection, and encryption. You can also integrate with Azure Copilot to help manage database interactions with applications and get information about databases (such as data sources, dynamic management views, and database issues). Common use cases include implementing cloud-scale databases for AI applications and modernizing applications.

4. PaaS application development

Azure’s PaaS portfolio includes Static Web Apps and App Service. Choosing between these options depends on the application you want to create and specific frameworks to implement.

Static Web Apps features:

• Brings source code to a full-stack, globally available web application via GitHub integrations.

• Automates application building and deployment API functions, as well as staging environments.

App Service features:

• Supports ASP.NET, ASP.NET Core, Java, Tomcat, JBoss EAP, Node.js, PHP, and Python frameworks.

• Automates app deployment and scaling.

• Compatible with Maven, Gradle, Azure, Developer CLI, Azure CLI, and PowerShell command lines.

Additionally, if you’re not sure where to start, you can browse application templates from the Azure Marketplace for WordPress, Django, Node.js, and Drupal programs.

5. Azure Machine Learning

Azure Machine Learning is for full machine learning lifecycle development. Its capabilities support data preparation, automated machine learning tasks (classification, regression, and computer vision), model fairness assessment, and prompt flow creation. With Azure Machine Learning, you can access GPU-based infrastructure, InfiniBand networking, and a comprehensive model catalog. With Machine Learning Studio, you have features to run Jupyter Notebook servers, visualize run metrics, design and deploy ML models, and automate experiments.

Comparing DigitalOcean vs Azure: Key differences

Notable differences between DigitalOcean and Azure include product offerings and pricing. These factors determine the suitability of each platform for various business sizes and needs, from startups to global enterprises.

Compliance and security

DigitalOcean maintains a strong commitment to protecting customer data and ensuring our services comply with global standards. DigitalOcean has achieved compliance certifications such as ISO/IEC 27001, SOC 1, and SOC 2 Type II. The platform secures customer workloads with a suite of security features that include Cloud Firewalls, DDoS protection, and cloud monitoring tools that provide insights into potential vulnerabilities. DigitalOcean ensures data encryption at rest and in transit and offers private networking capabilities to reduce exposure to potential threats.

Azure offers industry-standard compliance certifications, including GDPR, HIPAA, FedRAMP, and numerous region-specific certifications, such as IRAP and GxP. Azure’s infrastructure is designed to secure data and applications for businesses of all sizes, offering a layered security model that includes identity management through Azure Active Directory, threat protection with Azure Security Center, and network security through Azure Firewall and Virtual Network. They also provide advanced security features such as Microsoft Sentinel, a scalable, cloud-native SIEM system, and Azure Key Vault for managing cryptographic keys and other sensitive assets.

Pricing

When comparing DigitalOcean pricing vs Azure, pricing reflects their respective target markets and the complexity of their services.

DigitalOcean offers a predictable and transparent pricing model geared towards developers and digital-native enterprises. Its straightforward cost structure is a key advantage, with flat-rate pricing that’s easy to understand and plan around, which includes:

• Predictable monthly caps, avoiding unexpected costs

• No additional charges for outbound data transfer within the monthly transfer limits

• Competitive pricing for standard compute instances and additional services

Azure targets a broader market, including enterprise clients, resulting in a more complex pricing structure:

• Pay-as-you-go pricing with a broader range of services and scalability options

• Detailed billing can become complex, with charges for additional features like networking and data transactions

• Enterprise Agreement option for large organizations seeking standardized pricing

To provide a direct comparison of specific services, consider how a basic instance of compute service differs by each provider:

Beyond the pricing for the virtual machines themselves, you should also be aware of egress costs and how they can incur additional charges on your invoice.

For Azure pricing, let’s look at what it would cost to send 15TB of business transfers from North America with the Microsoft Business Network. The egress pricing structure is as follows:

• First 100GB: Free

• Next 10TB/month: $0.087 per GB

• Next 40TB/month $0.083 per GB

Data transfer calculation:

• Cost for the first 100GB: Free

• Cost for the next 10TB: (10,000GB - 100GB) * $0.087 = $867.03

• Cost for the remaining 5TB: 5,000GB * $0.083 = $415.00

Total Cost for North America:

Total: $867.03 + $415.00 = $1,282.03

The total cost for transferring 15TB of data with Azure is approximately $1,282.03.

With DigitalOcean, not only is there a much more generous bandwidth allowance, but you can also utilize bandwidth pooling to increase your total included bandwidth before incurring an egress charge. Each Droplet includes 500 GB of outbound data transfer and each additional GB is $0.01.

Data transfer calculation:

That means the same 15TB of data transfer with DigitalOcean would be:

• Cost for the first 500 GB: Free

• Cost for the next 10TB: (10,000 GB - 500 GB) *$0.01 = $95.00

• Cost for the remaining 5TB: 5,000 GB * $0.01 = $50.00

Total cost for transferring 15TB of data with DigitalOcean is approximately $145.00

Please note that the prices are estimated as of December 2025, and can vary based on region, current pricing adjustments, and specific configurations. Azure may also provide discounts through its various purchasing options, such as reserved instances or hybrid benefits, which are not reflected in this basic comparison and you should use the egress cost calculator for more accurate estimates.

Ease of use of documentation

DigitalOcean’s documentation stands out with its approachable, succinct, and actionable content. The platform also offers well-organized tutorials and API guides, supporting an easy onboarding experience and rapid infrastructure deployment.

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Read top DigitalOcean developer tutorials:

• How to Set Up a Node.js Application for Production on Ubuntu

• Mistral 3 Models on DigitalOcean

• How to Remove Docker Images, Containers, and Volumes

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Azure’s documentation can be overwhelming, given its vast ecosystem and technical depth. Users often encounter a steep learning curve navigating through the extensive documentation, which can be intricate and sometimes convoluted for those seeking quick solutions or straightforward guidance. This can make Azure’s comprehensive documentation appear less approachable for users seeking the same ease of use that DigitalOcean offers.

Data centers

DigitalOcean maintains a strategic selection of 15 data centers across key global locations, including New York, San Francisco, Bangalore, London, and Sydney. Our newest Atlanta data center incorporates the latest advancements in technology and network architecture, providing high performance and reduced latency across the Southern U.S. Despite a smaller footprint than hyperscaler clouds, DigitalOcean prioritizes quality and performance, delivering swift response times and low latency to its customers. That includes a robust 99.99% Service Level Agreement (SLA) for their Droplets and Volumes.

Azure has one of the largest global networks of data centers. This expansive coverage allows Azure to offer geographical flexibility and redundancy to its customers, ensuring improved latency for a global audience. Azure’s SLA promises 99.99% availability for single-instance virtual machines and 99.995% for multi-instance virtual machines using Premium Storage.

Customer support

DigitalOcean offers a customer support experience tailored to the needs of diverse AI startups and digital native enterprises:

• Starter, Developer, Standard, and Premium plans to suit the needs of different businesses.

• Starter is available to all users with general troubleshooting and technical support with the option to upgrade to more extensive support plans.

• Premium Support services are purpose-built for mission-critical applications, ensuring consistent uptime and personalized support.

• Convenient communication options, including direct calls via Google Meet and dedicated Slack channels with Technical Account Managers.

• Additional benefits, such as business consultation, co-marketing, and architecture review meetings with product teams, emphasize their commitment to their customers’ growth and success.

Azure, catering to a wide range of customers from individuals to large enterprises, offers a tiered support system:

• Basic, Developer, Standard, and Professional Direct support plans for different business needs.

• Basic support is available to all users with options to upgrade to more comprehensive support plans.

• Professional Direct support for critical workloads, offering faster response times and access to a pool of Azure experts.

• A Premier support plan designed for large or global enterprises, providing personalized service with account management, on-site training, and proactive guidance.

When to use DigitalOcean vs Azure

Though DigitalOcean and Azure’s portfolios are comparable, it’s helpful to evaluate how each of these vendors is suitable for specific use cases, such as:

DigitalOcean vs Azure: VMs

DigitalOcean Droplets offer developers an easy way to launch Linux-based VMs with flat-rate pricing and minimal configuration. They’re ideal for open-source projects, AI/ML workflows, and scalable initiatives. Azure VMs, on the other hand, support both Linux and Windows, with advanced scaling capabilities, high availability, and options for global regions. They’re suited for enterprise applications or workloads requiring Windows support.

DigitalOcean vs Azure: Storage

DigitalOcean offers straightforward storage options, including Spaces (object storage with CDN), Volumes (high-speed block storage), and Network File Storage. These are well-suited for hosting media, app assets, and database storage. Azure offers Blob Storage and Data Lake Storage, which scale to handle large datasets, analytics, and enterprise use cases.

DigitalOcean vs Azure: Managed databases

DigitalOcean offers fully managed databases like Postgres, MySQL, MongoDB, and Kafka, with built-in security, backups, and high availability, making deployment fast and low-maintenance. Azure supports the same engines plus hyperscale features, deep integration with other Azure tools, and support for large, distributed applications.

DigitalOcean vs Azure: Platform as a Service (PaaS)

DigitalOcean’s App Platform is a streamlined PaaS for deploying code from Git repositories with autoscaling and CI/CD out of the box. It supports the most popular frameworks and is ideal for solo developers or small teams who want to launch quickly. Azure’s App Service and Static Web Apps offer more advanced configuration, staging environments, and integration with enterprise tools.

DigitalOcean vs Azure: AI/ML

DigitalOcean’s Gradient AI tools provide an accessible path to train models, run inference, and deploy AI apps on GPU-powered VMs—great for digital-native enterprises and ML use cases. Azure Machine Learning provides a comprehensive suite of tools for data preparation, training, MLOps, and deployment at scale, backed by high-end infrastructure.

DigitalOcean vs Azure: Vendor lock-in

DigitalOcean uses a host of open source technologies (Linux, Ceph, Kubernetes, Rsyslog, Packer, and CloudInit) to create its portfolio, which reduces the amount of vendor lock-in required to use DigitalOcean products and makes migration to its products potentially easier. Azure is all based on Microsoft products and technologies, which can provide specific functionalities and programs, but often requires prolonged use of the Microsoft-only ecosystem.

DigitalOcean vs Azure FAQ

How does DigitalOcean compare to Microsoft Azure?

Like Microsoft Azure, DigitalOcean’s portfolio provides VMs, storage, managed databases, PaaS, and AI/ML development platforms. However, DigitalOcean is more cost-effective, provides more robust support, and is favored by the developer community and digital-native enterprises.

What is the top cloud provider for developer-focused infrastructure?

DigitalOcean is a well-established and esteemed cloud provider for developer-focused infrastructure. Its extensive portfolio makes it easy to run highly scalable, complex workloads in the cloud using open-source technology and develop applications for AI/ML.

Which is more cost-effective: DigitalOcean or Microsoft Azure?

DigitalOcean is more cost-effective with its tiered, transparent pricing system. Microsoft Azure requires custom pricing for each project or infrastructure configuration, resulting in varying and hard-to-predict costs across projects.

What AI/ML offerings do DigitalOcean and Azure have?

DigitalOcean offers its Gradient AI Agentic Cloud for AI testing, development, and deployment via GPU Droplets. Azure offers Azure Machine Learning for fine-tuning and deploying language models at scale.

Is DigitalOcean or Azure better?

Both DigitalOcean and Azure offer extensive product portfolios, so the best option will depend on your project and infrastructure requirements, as well as your budget. DigitalOcean is ideal if you want a platform that focuses on simple execution, has a wide variety of integration options, and transparent pricing. Azure might be more expensive, but it can make sense if you are primarily working within a Microsoft ecosystem.

Accelerate your AI projects with DigitalOcean GradientAI GPU Droplets

Accelerate your AI/ML, deep learning, high-performance computing, and data analytics tasks with DigitalOcean GradientAI GPU Droplets. Scale on demand, manage costs, and deliver actionable insights with ease. Zero to GPU in just 2 clicks with simple, powerful virtual machines designed for developers, startups, and innovators who need high-performance computing without complexity.

Key features:

• Powered by NVIDIA H100, H200, RTX 6000 Ada, L40S, and AMD MI300X GPUs

• Save up to 75% vs. hyperscalers for the same on-demand GPUs*

• Flexible configurations from single-GPU to 8-GPU setups

• Pre-installed Python and Deep Learning software packages

• High-performance local boot and scratch disks included

• HIPAA-eligible and SOC 2 compliant with enterprise-grade SLAs

Sign up today and unlock the possibilities of DigitalOcean GradientAI GPU Droplets. For custom solutions, larger GPU allocations, or reserved instances, contact our sales team to learn how DigitalOcean can power your most demanding AI/ML workloads.

*Up to 75% cheaper than AWS for on-demand H100s and H200s with 8 GPUs each. As of April 2025.