A complete guide on cloud-based applications for technology leaders
Explore our in-depth guide on cloud-based apps tailored for tech leaders. Learn about cloud migration, app development, and the latest industry trends to enhance your business strategies.
Traditional applications vs. cloud-based applications vs. cloud-native applications
Cloud-based applications vs. cloud-native applications in the simplest manner
Why is cloud-based application development a good way to go for mid-sized companies?
Cloud strategy and assessment workshop
What sets any company apart in cloud application development?
Benefits of cloud-based applications across different functions within organizations
More on the impact of cloud-based applications
Best six steps for building cloud-based applications
1. Build a strong business case
2. Develop a comprehensive cloud strategy
3. Conduct thorough risk management
4. Compose and automate deployment
5. Test and validate the application
6. Optimize for continuous improvement
Join hands with the best cloud-based application development company
Aspect / Features | Traditional applications | Cloud-based applications | Cloud native applications |
---|---|---|---|
Architecture | Monolithic, tightly coupled with underlying OS | Often migrated from traditional architectures, can be monolithic or modular | Microservices, containerized, loosely coupled |
Development duration | Longer development cycles, released as one package | Faster than traditional due to cloud integration, but slower than cloud-native | Faster development with iterative releases using CI/CD pipelines |
OS dependency | High dependency on the underlying OS | Reduced dependency, often uses virtual machines or managed services | Abstracted OS layer, allowing flexibility and easier migration |
Scalability | Requires additional hardware and complex processes to scale | Easier to scale than traditional, uses cloud resources but may face some limits | Easily scalable, auto-scaling capabilities |
Cost model | High upfront costs, requires investment in hardware | Pay-as-you-go model, more cost-effective than traditional | Pay-as-you-go, cost-effective based on usage |
Release time | Slower release cycles, updates and bug fixes take longer | Faster release cycles than traditional, but not as quick as cloud native | Rapid release cycles, continuous integration and delivery |
Transition | Handoffs between development and operations teams, creating silos | Smoother than traditional, but still may involve some handoffs | Smooth transition from development to production, DevOps practices |
Development / Operations cost | High setup and maintenance costs, less efficient in resource usage | Lower costs than traditional, utilizes cloud resources | Lower operational costs, only pay for what you use, optimized resource usage |
Automation | Limited automation, more manual processes | More automated than traditional, uses cloud services | High level of automation, infrastructure as code |
Downtime | Higher downtime during updates and maintenance | Reduced downtime compared to traditional, but not as minimal as cloud-native | Minimal to zero downtime, seamless updates |
Backup and recovery | Low backup capabilities, manual and error-prone | Improved backup capabilities using cloud solutions | Automated backups, robust disaster recovery mechanisms |
Modularity | Tightly coupled components, harder to update and scale | More modular than traditional, but may still have some monolithic aspects | Highly modular, independent microservices that are easier to update and scale |
Flexibility | Limited flexibility, changes require significant effort | More flexible than traditional, but less so than cloud-native | Highly flexible, easy to deploy and redeploy resources as needed |
Testability | Manual testing processes, slower feedback cycles | Improved testability with cloud-based tools, but may not be fully automated | Automated testing, continuous integration, and delivery pipelines |
Disposability | Long startup and shutdown times, less robust to failures | Improved disposability, but not as fast as cloud-native | Fast startup and shutdown, resilient to failures, quick recovery |
User experience | Potentially slower, less responsive, dependent on hardware capacity | Improved user experience, faster and more responsive than traditional | Optimal user experience, highly responsive, seamless updates |
Resource allocation | Static resource allocation, often under or over-provisioned | Dynamic resource allocation, better utilization of cloud resources | Optimal resource allocation, automatic scaling based on demand |
Security | Security managed in-house, potentially higher risk of breaches | Enhanced security with cloud provider’s tools, but shared responsibility | Built-in security features, continuous monitoring, and updates |
Innovation | Slower pace of innovation due to longer development cycles | Increased innovation potential, faster deployment of new features | High innovation potential, rapid experimentation, and deployment of new features |
Cloud-native applications: Built for the cloud, leverage microservices, containers, and CI/CD for maximum scalability and agility.
Cloud-based applications: Traditional applications adapted to run in the cloud benefit from cloud infrastructure but do not fully utilize cloud-native features.
- As more organizations lock on its transformative power, and
- As new technologies emerge to deliver even more services and functionality.
- Instead of building a recommendation engine from scratch, e-commerce companies can now easily access and integrate an off-the-shelf engine.
- Using readily available video intelligence modules, developers can now extract actionable insights from video files. This eliminates the need to develop their own ML or computer vision models.
- Subscribe to modules that provide a fully managed service for connecting, managing, and analyzing data from globally dispersed IoT devices easily and securely. Such modules can support a wide range of services, from developing a system that detects pipeline cracks automatically to sending alerts to maintenance engineers to attend.
- Accelerated speed to market: By partnering with Amazon Web Services (AWS), Salesforce expanded globally without building its data centers. This partnership also ensured compliance with local data laws, streamlining their international growth.
- Enhanced product testing and development: Stanford researchers developed a digital method to test new drug compounds before physical trials. This innovation speeds up the development process and reduces costs associated with physical testing.
- Lower time and process costs: An insurance company processes routine claims automatically using computer vision and text analysis. This approach significantly reduces time and costs compared to manual processing.
- Higher return on assets: An industrial conglomerate optimized its wind turbines with IoT and real-time analytics. This adjustment led to a double-digit increase in energy output, maximizing asset efficiency.
- Dynamic pricing: A heavy plant manufacturer uses cloud-based dynamic pricing tools to improve dealer relationships. These tools also help increase profits and sales volume.
- Focused allocation of resources: A farm machinery manufacturer allows farmers to apply herbicide only to weeds. This method reduces herbicide usage by up to 85%, cutting costs significantly.
- Better customer segmentation: A healthcare company analyzes data from various sources to find trends. This detailed analysis improves customer segmentation and targeting.
- Extended customer reach: A private equity fund moved cattle auctions online, hosting data in the cloud. They use computer vision to automate health evaluations, expanding their market reach.
- Improved employee performance: A US bank uses AI algorithms to give real-time advice to its sales team. This guidance helps them sequence product offers more effectively, boosting sales performance.
- Reduced siloes and collaboration barriers: A life sciences company connected its global research departments via cloud data storage. This connection allows seamless collaboration on shared data sets, enhancing research efficiency.
- A fast-casual restaurant chain’s online orders surged to 400,000 a day from 50,000. A pleasant surprise? They were able to achieve this by migrating their online ordering system to the cloud.
- A biotech company used cloud computing to deliver the first clinical batch of a COVID-19 vaccine candidate for Phase I trials in 42 days. The scalable cloud data storage and cloud collaboration, which facilitate real-time access, editing, and sharing of files, boosted productivity and creativity among team members.
- An Automaker uses a common cloud platform to manage data from machines and systems, track logistics, and offer insights on shop floor processes across 124 plants, 500 warehouses, and 1,500 suppliers. This cloud adoption is expected to reduce factory costs by 30% by 2025 while fostering innovation.