Modernization pathways

AWS modernization pathways provide organizations with structured approaches to modernize their application portfolios after migration. Each pathway provides a proven route to specific business outcomes. These include reducing licensing costs, increasing developer velocity, enabling data-driven decisions, and embedding AI into existing workflows.

Organizations rarely follow a single pathway. Most combine multiple pathways across their portfolio based on the specific needs of each workload, team readiness, and business priorities. The pathways that follow are ordered from foundational infrastructure changes to higher-order application transformations, but you can adopt them in any sequence or in parallel.

Move to AI

With this modernization pathway, you can identify and implement high-impact AI opportunities within your existing application portfolio. Rather than building AI initiatives from scratch, this pathway uses current applications as a foundation for AI integration.

When you adopt this pathway, you systematically assess your portfolio for AI opportunities, implement graduated AI capabilities—from basic inference to autonomous agents—and establish the governance frameworks needed for responsible AI adoption.

This pathway provides the following benefits:

• Unlock trapped value – Existing applications contain data, workflows, and domain logic that AI can enhance without requiring full rewrites.

• Accelerate decision-making – Embed intelligent recommendations, predictions, and automation directly into business workflows where decisions are made.

• Enhance customer experiences – Add natural language interfaces, personalization, and predictive capabilities to customer-facing applications.

• Automate manual processes – Replace repetitive human judgment tasks (classification, extraction, routing) with AI that improves over time.

• Competitive differentiation – Transform commodity applications into intelligent systems that deliver unique value competitors cannot easily replicate.

• AI-ready architecture – Modernized applications with clean APIs, proper state management, and observability are ready for safe AI agent integration.

This pathway applies to the following situations:

• Organizations with large application portfolios that contain untapped data and process automation opportunities

• Companies whose competitors are gaining advantage through AI-enhanced experiences

• Teams that have completed initial cloud migration and want to unlock the next wave of business value

Example: A property management company processes thousands of maintenance requests monthly, each requiring manual classification, prioritization, and routing to the appropriate contractor. By integrating AI into the existing request management application, the system automatically classifies incoming requests by urgency, matches them to available contractors based on skill and proximity, and prioritizes them based on tenant impact. Processing time drops from hours to minutes, tenant satisfaction improves, and staff focus shifts from administrative routing to exception handling and quality oversight.

Move to cloud native

Moving to cloud native involves decomposing monolithic applications into smaller, independently deployable services that leverage fully managed cloud capabilities for compute, messaging, storage, and orchestration.

Monolithic architectures have limitations: slow release cycles, cascading failures, and scaling constraints. This pathway addresses each by enabling teams to own discrete business capabilities end-to-end. Applications become more resilient, scalable, and evolvable without requiring coordinated releases across the entire system.

This pathway provides the following benefits:

• Independent deployability – Teams ship changes to their service without waiting for or impacting other parts of the system, dramatically increasing release velocity.

• Granular scaling – Scale individual components based on demand rather than scaling the entire application, optimizing costs for uneven workload patterns.

• Fault isolation – A failure in one service does not cascade to the entire application, improving overall system reliability and customer experience.

• Technology flexibility – Each service can use the language, framework, or data store best suited to its problem domain, rather than being constrained by a single technology stack.

• Faster innovation – Smaller, focused services are easier to understand, test, and modify, reducing the time from idea to production.

This pathway applies to the following situations:

• Monolithic applications where release cycles are measured in weeks or months

• Systems where one component's failure brings down the entire application

• Organizations where multiple teams are blocked by shared codebases

Example: An insurance company's claims processing system is a single monolith deployed quarterly. Any change—no matter how small—requires a full regression cycle. By decomposing into services aligned to business domains (intake, adjudication, payment, notification), each team deploys independently. The claims intake team ships improvements weekly, while the payment team maintains its own release cadence. A bug in notifications no longer delays claims processing.

Move to containers

Containerization provides a consistent runtime environment that decouples applications from the underlying infrastructure, enabling teams to build, ship, and run software faster and more reliably.

Organizations adopting this pathway package existing applications into containers and deploy them using managed orchestration services. This eliminates environment inconsistencies between development and production, simplifies scaling, and standardizes deployment across teams—regardless of the programming language or framework in use.

This pathway provides the following benefits:

• Operational consistency – Containers ensure the same artifact runs identically across development, staging, and production, eliminating the "works on my machine" problem and reducing deployment failures.

• Resource efficiency – Multiple containers share the same host, increasing utilization compared to dedicated virtual machines and reducing infrastructure costs.

• Faster deployments – Container images start in seconds rather than minutes, enabling rapid scaling and frequent releases without extended maintenance windows.

• Portability – Containerized applications are not tied to specific infrastructure, making it straightforward to move workloads across environments or regions.

• Team autonomy – Each team can own their deployment pipeline independently, shipping updates without coordinating with other teams or waiting for shared release cycles.

This pathway applies to the following situations:

• Organizations with large VM fleets running diverse applications that want a common deployment standard

• Teams seeking to adopt CI/CD without rewriting applications

• Companies exiting costly virtualization platforms

Example: A retail company operates 200 applications across multiple data centers, each deployed differently depending on when it was built. By containerizing these applications, all teams adopt a single deployment standard. New developers onboard faster, deployments happen multiple times per day instead of monthly, and infrastructure costs drop because workloads share resources more efficiently.

Move to managed databases

Moving to managed databases eliminates the undifferentiated heavy lifting of database administration—patching, backups, replication, failover—and lets teams focus on optimizing data models and queries for their applications.

This pathway covers both migrating from self-managed databases to their managed equivalents and transitioning from commercial engines to open-source alternatives. Organizations gain automated operations, built-in high availability, and pay-as-you-go pricing without sacrificing performance or data integrity.

This pathway provides the following benefits:

• Reduced operational burden – Automated patching, backups, point-in-time recovery, and failover eliminate manual DBA tasks that consume engineering time.

• High availability by default – Multi-AZ deployments and automated failover provide resilience without complex custom configurations.

• Cost optimization – Right-size database instances dynamically, use reserved capacity for predictable workloads, and eliminate over-provisioned hardware.

• Licensing freedom – Transitioning from commercial database engines removes per-core or per-socket licensing costs that scale unpredictably with growth.

• Purpose-built options – Choose the right database for each workload pattern (relational, document, key-value, graph, time-series) rather than forcing all data into a single engine.

This pathway applies to the following situations:

• Organizations spending significant DBA time on maintenance rather than optimization

• Companies with expiring commercial database licenses

• Teams that need automated disaster recovery and point-in-time restore without custom scripts

Example: A financial services firm runs dozens of self-managed database instances across two data centers, requiring a dedicated team for patching, backup verification, and failover testing. After moving to managed database services, automated backups and cross-region replication run without intervention. The DBA team shifts focus from maintenance to query optimization and data modeling, and the company eliminates annual commercial license renewals.

Move to open source

This pathway helps organizations reduce dependency on proprietary software by migrating to open-source alternatives running on managed cloud infrastructure. It applies to application runtimes, databases, operating systems, and development frameworks.

Beyond cost savings, moving to open source gives organizations transparency into the software they depend on, access to broader talent pools, and freedom from vendor-specific lock-in that constrains architectural decisions.

This pathway provides the following benefits:

• License cost elimination – Remove per-seat, per-core, or per-server licensing fees that grow unpredictably as the organization scales.

• Talent availability – Open-source skills are more widely available in the market, reducing hiring difficulty and onboarding time for new team members.

• Community innovation – Benefit from rapid feature development, security patches, and ecosystem tooling driven by global open-source communities.

• Architectural freedom – Make technology decisions based on technical merit rather than license constraints or vendor roadmaps.

• Transparency and auditability – Inspect, modify, and audit source code directly, meeting security and compliance requirements without relying on vendor attestations alone.

This pathway applies to the following situations:

• Organizations with significant annual licensing spend on proprietary runtimes or databases

• Companies whose vendor contracts restrict architectural choices

• Teams that struggle to hire for proprietary technology skills

Example: A media company spends millions annually on proprietary runtime licenses across its web platform. Developer hiring is constrained because candidates rarely have experience with the proprietary stack. By migrating to an open-source runtime on managed cloud infrastructure, the company eliminates license costs, attracts from a larger talent pool, and gains access to a rich ecosystem of open-source libraries and tools that accelerate feature development.

Move to modern analytics

This pathway transforms how organizations collect, store, process, and derive insights from data. It replaces fragmented, batch-oriented data pipelines with unified, near-real-time analytics architectures that bring data closer to decision-makers.

Organizations adopting this pathway build modern data foundations that support advanced analytics, machine learning, and business intelligence—breaking down data silos and enabling self-service access across the organization.

This pathway provides the following benefits:

• Faster time to insight – Move from batch processing (hours or days) to near-real-time analytics, enabling decisions based on current rather than stale data.

• Data democratization – Self-service tools let business users explore data without waiting for engineering teams to build custom reports.

• Cost-effective storage at scale – Decouple storage from compute, storing vast amounts of data affordably and scaling processing independently based on query needs.

• Unified data view – Consolidate data from disparate sources into a single platform, eliminating inconsistencies between reports from different systems.

• ML-ready foundations – Modern data architectures provide the clean, accessible datasets that machine learning initiatives require to succeed.

This pathway applies to the following situations:

• Organizations where reporting takes days due to batch pipelines

• Companies with data locked in silos across business units

• Teams that want to enable self-service analytics without building custom dashboards for every request

Example: A logistics company has shipment data in one system, customer feedback in another, and financial data in a third. Generating a cross-functional report requires manual data extraction by multiple teams. After building a modern analytics foundation, all data flows into a centralized platform accessible to business analysts across the organization. Delivery performance, customer satisfaction, and profitability are visible in a single dashboard updated continuously.

Move to modern DevOps

This pathway modernizes how software is built, tested, and delivered by establishing automated pipelines, infrastructure as code, and continuous feedback loops. It addresses the gap between development speed and operational reliability.

Modern DevOps practices ensure that code changes flow from development to production safely and rapidly, with automated testing, security scanning, and compliance checks built into the pipeline rather than bolted on after the fact.

This pathway provides the following benefits:

• Accelerated delivery – Automated pipelines reduce the path from commit to production from weeks to hours or minutes, enabling rapid response to business needs.

• Shift-left quality – Automated testing and security scanning catch issues early in the development cycle when they are cheapest to fix.

• Infrastructure consistency – Infrastructure as code ensures environments are reproducible, auditable, and drift-free, eliminating configuration-related failures.

• Reduced toil – Automation eliminates repetitive manual tasks (deployments, environment provisioning, compliance checks), freeing engineers for higher-value work.

• Continuous feedback – Monitoring, alerting, and observability integrated into the delivery pipeline create tight feedback loops between production behavior and development decisions.

This pathway applies to the following situations:

• Organizations where deployments are manual, infrequent, and error-prone

• Teams that lack visibility into production behavior until customers report issues

• Companies where environment provisioning takes days or weeks

Example: A healthcare company deploys software quarterly through a manual checklist involving multiple teams over a weekend maintenance window. Failures during deployment cause extended outages. After adopting modern DevOps practices, deployments happen multiple times per week through automated pipelines. Every change passes through automated tests, security scans, and compliance checks before reaching production. Rollbacks that once took hours now happen in minutes.

For more information, contact your AWS account team.