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What tools are used for application modernization?
Published:
8 minute read
TL;DR
- If you’re thinking about tools before you name business outcomes, you’re introducing undue risk in your application modernization work. The most successful modernizations start with business goals like reducing technical debt, accelerating software delivery, improving resilience, strengthening security, and enabling AI adoption.
- Tooling matters and changes frequently, but process matters more. Teams that focus on repeatable workflows, multi-tiered context architecture, and evidence-based decisions outperform those chasing the latest tools.
- This post explains the tool ecosystem that supports those outcomes and how the categories work together to modernize applications at scale.
JUMP TO:
- Application modernization tool categories and examples
- Cloud platforms for modernization
- Top containerization and orchestration tools
- DevOps, CI/CD, and infrastructure automation tools
- Monitoring vs. APM vs. observability tools
- Integration and API management tools
- 20 questions to ask when choosing application modernization tools
What categories of tools are used in modernization?
Several categories of tools work together in application modernization. Traditionally, organizations focused on cloud migration, DevOps, containers, and monitoring. Today, many modernization programs also include AI-powered analysis and automation tools that help teams understand and transform legacy applications more quickly.
Meanwhile, nearly two-thirds of IT leaders rank cost management as a top priority, making modernization outcomes more important than modernization activity, including focusing too narrowly on tools instead of strategies.
14 Application modernization tool categories
1. Agentic workflow and orchestration
Why it matters
Accelerates software delivery and modernization program outcomes
Examples
- LangGraph & LangSmith
- Microsoft Copilot Studio
- Microsoft Agent Framework
- Amazon Bedrock: Agent Core
- Google Agent Development Kit (ADK)
2. AI-assisted software engineering
What it does
Supports developers with code generation, code review, documentation, testing, and workflow automation
Why it matters
Accelerates the software delivery life cycle (SDLC)
Examples
- Claude Code
- Cursor
- GitHub Copilot
- Kiro
- Codex
3. AI-powered modernization
What it does
Uses AI to analyze legacy code, document business logic, identify dependencies, generate test cases, and accelerate activities
Why it matters
Improves speed, traceability, and confidence
Examples
- Amazon Bedrock
- OpenAI GPT-based modernization accelerators
- Microsoft Foundry
- Google Gemini Enterprise Agent Platform
4. Assessment and discovery
What it does
Analyzes applications, dependencies, infrastructure, and technical debt
Why it matters
Exposes current-state complexity and prioritize what should be modernized, how
Examples
- Azure Migrate
- AWS Transform
- CAST
- GitHub Copilot Modernization Agent
- Mechanical Orchard
- Moderne
5. Change management and adoption
What it does
Helps manage workforce transition, skills development, communication, and adoption
Why it matters
Aligns people, not just process and tool improvements
Examples
- Pendo
- WalkMe
- Whatfix
6. Cloud migration
What it does
Moves apps, workloads, databases, and infrastructure from on-premises environments to cloud platforms
Why it matters
Enables scalability, flexibility, and access to modern cloud services
Examples
- AWS Application Migration Service
- Azure Migrate
- Google Cloud Migrate
- Cloud Hound
- CAST
7. Containerization and orchestration
What it does
Packages apps into portable containers and manages them across environments
Why it matters
Offers scalability, resilience, and cloud-native operations
Examples
- Docker
- Kubernetes
- Red Hat
- OpenShift
8. Data modernization
What it does
Transforms legacy data environments into modern analytics and AI-ready platforms
Why it matters
Enables real-time insights and decision-making
Examples
- Databricks
- Microsoft Fabric
- Snowflake
9. DevOps and automation
What it does
Automates software development, testing, deployment, and infrastructure management
Why it matters
Improves delivery speed and consistency
Examples
- GitHub Actions
- GitLab CI/CD
- Jenkins
- Azure DevOps
10. Infrastructure as code (IaC)
What it does
Automates infrastructure provisioning and management using code
Why it matters
Minimizes costly, risky manual configuration
Examples
- AWS CloudFormation
- Terraform
- Pulumi
- Bicep
11. Integration and API management
What it does
Connects modern apps with legacy systems, cloud services, databases, and external partners
Why it matters
Enables seamless connectivity across modern and legacy environments
Examples
- Apigee
- Boomi
- MuleSoft
12. Logging and analytics
What it does
Collects, stores, searches, and analyzes operational and security data across apps and infrastructure
Why it matters
Provides the operational insights needed for troubleshooting, optimization, and security monitoring
Examples
- Elastic (ELK Stack)
- Grafana Loki
- Splunk
13. Monitoring, APM, and observability
What it does
Gives visibility into app performance, infrastructure health, reliability, and user experience
Why it matters
Powers proactive issue identification and resolution
Examples
- Datadog
- Dynatrace
- New Relic
14. Security and risk management
What it does
Helps secure modernized environments and meet compliance requirements
Why it matters
Reduces operational risk throughout modernization programs
Examples
- Microsoft Defender for Cloud
- Palo Alto Prisma Cloud
- Wiz
- SonarQube
- Snyk
- ChainGuard
How do cloud platforms support modernization?
Cloud platforms serve as the foundation for zero legacy and app modernization efforts because they provide scalability, flexibility, and access to advanced services such as AI, analytics, and automation.
The three leading platforms are:
- Amazon Web Services (AWS)
- Microsoft Azure
- Google Cloud Platform (GCP)
Many organizations also adopt hybrid and multi-cloud strategies to balance cost, security, compliance, and vendor flexibility. Modernization leaders increasingly evaluate cloud platforms based on how well they support future AI initiatives, not just current infrastructure needs.
In 2024, 60% of large enterprises repatriated at least one workload from public cloud. The broader shift toward hybrid modernization strategies that optimize for cost, resilience, and control rather than cloud adoption as a catch-all solution continue.
What are the top containerization and orchestration tools?
Containers have become a standard modernization pattern because they make applications easier to deploy, scale, and maintain.
The most widely used tools include:
- Docker
- Kubernetes
- Red Hat OpenShift
- Rancher
These technologies help organizations move away from tightly coupled legacy architectures toward more flexible cloud-native environments. The goal is not simply rehosting legacy software or capex reduction; it’salso to create platforms that can evolve as business needs change.
What DevOps tools are commonly used in application modernization?
Popular developer operations (DevOps) tools include GitHub Actions, GitLab CI/CD, Jenkins, and Azure DevOps, which support continuous integration/continuous delivery, automated testing, and release management. Terraform enables infrastructure as code, helping teams provision and manage cloud environments consistently, securely, and at scale.
Why are these tools prioritized?
DevOps tools help organizations automate the entire software delivery life cycle (SDLC)—from planning and building to testing, deployment, operations, and feedback. By automating the SDLC, teams can release new features faster, improve reliability, and reduce maintenance burdens (aka time, money, drudgery).
Most organizations still get the most value from CI/CD automation, automated testing, IaC, security integration, and release reliability. AI engineering capabilities accelerate those practices rather than replace them. AI-assisted development capabilities, including test generation and execution, documentation support, and software engineering copilots pull three powerful levers. They:
- Accelerate delivery
- Improve developer productivity
- Help teams maintain quality and consistency
However, the tools are only as good as the strategy behind their use and the competence of your team has using them. Organizations achieve superior results when automation comes together with clear architectural standards, embedded security practices, and effective governance. You need a delivery model that aligns technology and talent investments to measurable business outcomes.
What monitoring, APM, and observability tools are needed?
Modern applications span cloud platforms, APIs, containers, databases, and microservices. To maintain performance and reliability across these distributed environments, organizations rely on monitoring, application performance monitoring (APM), and observability tools.
What’s the difference between monitoring, APM, and observability tools?
Monitoring tools are for detection, including detecting outages, performance degradation, and operational issues in real time. They ask, Is something wrong? An example outcome might be that "Response times are increasing and error rates are rising."
APM tools are for diagnosis. They help teams diagnose application bottlenecks and improve the speed and reliability of business-critical services. They ask, Why is the application slow or failing? An example outcome might be, "The payment API is causing transaction delays."
Observability tools are for root-cause analysis. They provide end-to-end visibility across applications, infrastructure, APIs, and dependencies to help teams truly, completely resolve complex issues. They ask, What is happening across the entire system and why? An example outcome might be, "A cloud networking change is affecting multiple services and creating downstream performance issues."
What do observability platforms help IT teams understand?
Modern observability platforms help teams answer questions across five areas critical areas:
- Application performance: Is the application responding quickly and reliably?
- Infrastructure health: Are servers, containers, databases, and cloud resources operating as expected?
- User experience: Are customers experiencing slowdowns, errors, or outages?
- Security events: Are security threats or configuration issues affecting system performance?
- System dependencies: Which APIs, services, databases, or integrations are causing bottlenecks?
Common monitoring, APM, and observability platforms
Datadog
Gives visibility into application performance, infrastructure health, cloud services, containers, logs, and distributed systems
Top questions it answers:
- Where is application performance degrading?
- Which infrastructure component is causing the issue?
- How is performance trending across environments and services?
Dynatrace
Gives visibility into end-to-end application performance, user experience, service dependencies, infrastructure, and AI-driven root cause analysis
Top questions it answers:
- What is the root cause of this performance issue?
- Which service dependency is creating the bottleneck?
- How is user experience affected?
New Relic
Gives visibility into application performance, user transactions, APIs, infrastructure, logs, and distributed traces
Top questions it answers:
- Which transactions are performing poorly?
- Where are users experiencing latency or errors?
- Which application changes impacted performance?
OpenTelemetry
Gives visibility into traces, metrics, and logs across applications, services, APIs, and infrastructure
Top questions it answers:
- How does a request move across systems and services?
- Where are latency and failure points occurring?
- Which dependencies are impacting application performance?
Splunk
Gives visibility into logs, events, operational data, security activity, and system telemetry
Top questions it answers:
- What happened before, during, and after the incident?
- Are there security or operational anomalies affecting performance?
- What patterns can help prevent future issues?
What integration and API management tools are important?
The strategy based on your modernization goals will always be more important than the tools.
Most modernization programs connect new systems with existing applications and data sources. It’s rarely work that happens on a blank slate. This makes integration and API management one of the most critical—and often most underestimated—parts of modernization. It’s tricky to pull something entangled out and embed something new.
Typical modernization goals and integration solutions
If your goal is to modernize without rewriting everything, integration platforms and tool help expose legacy functionality through APIs instead of refactoring systems.
If your goal is to accelerate cloud migration, integration platforms and tool help connect cloud and on-premises applications during transition periods.
If your goal is to Improve customer experience, integration platforms and tool help enable real-time data sharing across channels and applications.
If your goal is to reduce technical debt, replace brittle point-to-point integrations with governed APIs.
If your goal is to enable AI and analytics, create consistent access to trusted enterprise data across systems.
If your goal is to improve security and compliance, centralize API governance, monitoring, and access controls.
But, in most enterprises, the goal isn’t one of these outcomes—it’s all of these outcomes. Tool and platform capabilities overlap, so it can be hard to suss out the tradeoffs. Keep in mind that the best platform is rarely the one with the most capabilities. The best platform is the one that best aligns with your modernization strategy, governance model, operating model, and long-term architecture vision.
Questions technology leaders should ask before selecting modernization tools
Avoid favoring the tool that has the longest feature list. Focus, instead, on uncovering the operational and architectural consequences related to each tool.
Questions about architecture and future readiness
- Will this simplify our architecture or create more complexity (and indebt us down the road)?
- Will this accelerate access to trusted enterprise data?
- How resilient is this architecture as our business, vendors, and technology landscape change (even in ways we can’t imagine yet)?
- How well does this scale (what breaks) as integrations, data, and business capabilities grow?
Questions about operational complexity
- Which capabilities become simpler, and which become more complex?
- What visibility will we gain? Lose?
- What new operational burden are we taking on, and is that the right trade?
- What skills does our team have, and how do we close gaps?
Questions about governance and ownership
- Can we govern, secure, and manage this consistently across the enterprise?
- How will we prevent API sprawl, dupes, and security risk?
- Who owns this after implementation, and do we have the skills to operate it successfully?
- How dependent will we become on this platform, its ecosystem, and its vendor or services?
Questions about business value
- Where are we creating future lock-in (and adding long-term costs)?
- How quickly can we prove business value?
- How will we measure success today, tomorrow, and three years from now?
- What is the cost of waiting vs. choosing not to modernize?
