Hello everyone. I'm Shika Kamp. Welcome to the Site Reliability Engineering Conference of 2025. Today we are gonna discuss about transforming monolithic SaaS, the serverless evolution on AWS. Welcome to our comprehensive exploration of serverless migration strategies for SaaS applications using AWS. This presentation will guide you through the journey from only the architectures to flexible microservices that can dramatically reduce your. Operational costs. While accelerating development cycles, we'll examine practical implementation steps, real world success, and critical concentrations for security business intelligence integration. By the end of this presentation, you will have a clear roadmap for your own serverless transformation. Now, let's say why migrate to serverless architectures? Serverless architectures deliver compelling benefits that extend far beyond the simple cost savings organization May. Organizations migrating from monolithic systems report dramatically reduced operational overhead. So with typical cost reductions ranging from 30 to 60%, in some cases, organizations leveraging event driven serverless patterns per specific workloads have seen cost reductions exceeding 70% due to the precise resource allocation and paper execution models. So perhaps more importantly, these organizations experience a 40% boost in the developer productivity by eliminating infrastructure management tasks. This translates to fast innovation cycles and significantly reduce time to market for new features. So studies indicate that serverless adoption can accelerate feature deployment by up to 50%, enabling business to respond rapidly to market changes. Furthermore, the inherent scalability of serverless architecture leads to a 20 to 30% improvement in application resilience, including downtime and enhancing the USR experience. So beyond the cost and productivity, serverless architectures contribute to improved scalability. So which means like applications automatically scale based on demand handling certain spikes in traffic with minimal manual intervention, or in cases, no manual intervention at all. This can lead to a five times more increase in peak traffic handling capacity compared to traditional architectures. So reduced time to market developers focus on code not infrastructure leading to a faster development cycles. Companies have reported to 25% decrease in time, in the time required to launch these new products or features enhanced operational efficiency. With managed services handling tasks like patching and scaling operation teams can focus on strategic initiatives. This can result in a 15 to 20% reduction in operational burden, increased agility and flexibility. Serverless enables rapid prototyping and experimentation facilitating agile development practices. Organizations find that they're able to test and deploy these new ideas 30% faster. Environment sustainability, paper use models, reduced waste wasted resources contributing to a more sustainable computing practices. So this one steady estimated a potential 10 to 20% decrease in the energy consumption, but a certain workloads when migrated to serverless. Global reach the server. The serverless platform often provide a built in global distribution, simplifying the deployment of applications to multiple regions. Some services report latency reductions of 40% when moving to a global second. A global edge based service. S serverless location, simplified microservices implementation. Serverless functions are ideal for building microservices, allowing for a granular scaling and independent deployments. Organizations see up to 35% decrease in complexity of managing microservice deployments, even driven architecture adoption. So serverless aligns naturally with event driven architectures enabling real time data processing. And asynchronous workflows. This has been shown to improve real time processing speeds by 20 to 50% in certain use cases. These benefits collectively contribute to more agile, cost effective and scalable development environment, empowering organizations to innovate and compete effectively in the digital landscape. So when we un, let's try to understand a bit more about the monolith. Challenge monolith. SaaS applications create significant obstacles that severely constrain the business agility and market responsiveness. They're tightly coupled components Establish a critical development bottleneck. Where even minor modifications require comprehensive testing across the entire application, dramatically. Ex extending release cycles from days to weeks or months. Specifically, a study found that companies utilizing monolithic architecture experienced and average release cycle of 12 weeks for new features compared to three weeks for those using microservices. This translates to a 75. Person reduction in deployment speed. Furthermore regression testing for a single feature change in a monolithic application can consume up to 40% of development time. Diverting resources from innovation. The fundamental inability to scale independent components leads to substantial resource inefficiency, forcing organization to over-provision infrastructure. To accommodate peak demand scenarios. For example, a typical e-commerce platform using a monolithic architecture might allocate a server capacity for a black Friday level traffic year around resulting in an average of th 60 to 70% of ideal resource utilization during normal operations. This over provisioning leads to 32. 50% increase in infrastructure cost compared to architectures that will allow for granular scaling. Moreover, the lack of independent scaling often results in degraded performance during peak loads with reported latency increases up to five seconds leading up to a 20% drop in. User engagement. So additional challenges include technology lock-in monolithic architectures often rely on a single technology stack, making it difficult to adopt new technologies or integrated more than cloud services. This limits innovation and can lead up to 25% increase in maintenance overhead as legacy systems become harder to support increased risk of failure. A failure in one component can bring down the entire application leading to a significant downtime. So studies have shown that monolithic applications experience a 40% more downtime incidents compared to microservices based architectures difficulty in onboarding new developers. The complexity of a large code banks makes a challenge for new developers to understand and contribute, resulting in a 30% increase in onboarding time. Limited ability to implement continuous delivery. So the long release cycles associated with monolithic applications hinder the adoption of continuous delivery, PR delivery practices, slowing down the feedback loop and delaying the time to market. Organizations report a 50% decrease in the ability to deliver small frequent updates. Reduced innovation velocity. The complexity of changing the code and the risk of associated with changes slows down the rate at which the new features can be added. One study indicated a decrease of roughly 20% in the number of new features released per year. Database contention. Monolithic applications often rely on a single database, which can become bottleneck as the application scales leading to the performance degradation. So this can limit the applications ability to handle high transition, high transaction volumes. Database contention can, has been shown to cause 15 to 25% reduction in transaction throughput. These limitations underscore the need for more flexible and scalable architecture, such as microservices or serverless to meet our demands for the modern SaaS applications. Now let's take a look at decomposing the monoliths into microservices. So these transformation of microservices require a thoughtful decomposition based on business domains rather than technical layers. The study suggests that organizations that focus on business domain decompositions here, 30 to 40% reduction in integration complexities, post migration, identify the service boundaries. I. Let's add map. So we need to map the functional domains and business capabilities to establish clear service boundaries using domain driven design principles and event storming techniques, effective boundary identification through event storming and context mapping reveals and natural service. Demarcations that aligns with organizational structures and business capabilities. Specifically utilizing event storming workshops can reduce service boundary definition by the time of by 20 to 25% and improve alignment with business stakeholders. Research indicates that well defined service boundaries result in 15 to 20% improvement in the. Team autonomy. So define the service interfaces. Create robust well controlled version APIs with well-documented contracts that enforce loose couplings and enable independent service evolution organizations. Implementing API first strategies report a 35 to 40% increase in the developments. P due to reduced dependency, adopting open API specifications can reduce the API documentation efforts by up to 50%. And improve developer onboarding. Further mode version control of APIs has shown to decrease breaking changes up to by 25%. So refactor incrementally extract services systematically through the strangler pattern, prioritizing high value low risk components while maintain the maintaining the system stability. Successful migrations leverage the STR strangler pattern methodology systematically replacing the monolith functionality while preserving the system, integrating implement a service mesh. Deploy. Sophisticated service discovery and communication layer that enables resilient interservice communication, circuit breaking and observability. A single mesh can improve latency back to 10 to 20% and reduce service value rates by 90, 50, 30% through features like circuit breaking and retry policies. Implementing comprehensive observability with the service me leads to a 40 to 50 person reduction in the mean time to resolution. It's called S-M-T-T-R for service related incidents. Additional points to consider while decomposing the monoliths into microservices could be organizational alignment. Should the microservice architecture designs aligns with the organization structure and culture that's fit as more important automation. Automate deployment, testing and monitoring of update to enable the continuous delivery and reduce the manual error. So implementing the CICD pipelines can decrease the deployment times up to by 60 to 70% security. Implement robust security measures including authentication, authorization, and encryption to protect the interservice communication. Monitoring and observability. Implement comprehensive monitoring and logging to gain insights into the service performance by identifying potential issues. Database management. Address database management challenges by considering patterns such as database per service or shared. Database with careful schema design. So by focusing on these principles and incorporating data driven designs and organizations can effectively navigate the complexity complexities of microservices, navi migration, and unlock the benefits of increased agility, scalability, and resilience. Now let's take a look at a couple of the AWS services. The first one would be the AWS Lambda, the Serverless Foundation. AWS Lambda serves as the computational cornerstone of the modern serverless architectures executing code in response to events with zero server provisioning or management overhead. This paradigm shift fundamentally transforms the economics of cloud computing by implementing a consumption based pricing model that changes precisely for the compute resources consumed. Studies indicate that the organizations are shifting to Lambda based architecture experience, a 60 to 80% reduction in the operational overhead related to server management. Event, even executions functions instantly activate in response to triggers from AWS services completely eliminating idle resource consumption and delivering true paper use computing with millisecond precision. In typical event driven scenarios, lambda functions demonstr a cold stock, cold start latency of under a hundred milliseconds and 90% of invocations. And subsequent worm invocations achieve latencies below 10 milliseconds. This precise, this precision in resource allocation leads to a 20 to 30% reduction in overall compute cost compared to traditional VM based event processing. Automatic scaling lambda seamlessly in scales, concurrent executions to match the real time workload demands without any configuration overhead, effortlessly handling everything from a single request to thousands per second. Lambda can scale to handle certain spikes in traffic, achieving a 99.99% availability rate, and can scale from zero to thousands of concurrent executions in seconds. Auto-scaling capabilities have been shown to reduce the peak load latency by 40 to 50% compared to manually scaled systems. Polyglot support developers can gain freedom by leveraging multiple programming languages across different functions. Empowering teams to select the optimal technology stack for each specific microservice. The flexibility has been reported to decrease the development time by 15 to 25% as teams can utilize preferred languages and libraries. Furthermore, teams reported 10 to 15% increase in code maintainability by utilizing the right tool for the job. With Lambda millisecond billing granularity and zero friction scaling capabilities, organizations can achieve unprecedented optimization. Compared to traditional always on server instances, enterprises typically realizes 70 to 90% reduction in compute costs for appropriate workloads after migration, while simultaneously improving responsiveness and eliminating capacity planning concerns. Additional data points and benefits include reduced time to market, improving default tolerance, simplified operational complexity, enhanced security. Like cost optimization for intermittent workloads. Integration with AWS ecosystem and global scalability. By leveraging these benefits, organizations can achieve a significant cost savings and improve operational efficiency and accelerate innovation. Now let's take a look at the key serverless, a key AWS serverless services. So AWS builds a robust ecosystem of serverless services that work harmoniously with Lambda to create powerful cloud native solution. So let's take a look at Aurora server as dynamically adjust the database capacity in response to application demands without server management overhead. So studies have shown that the Aurora serverless can reduce database cost by 30 to 50% for applications with variable workloads. It auto scales database capacity when within seconds maintaining a consistent performance and reducing the need for manual capacity Planning for application with unpredictable traffic patterns. Aurora Serverless has been reported to decrease database administration time. By up to 60% API gateway simplifies the creation of secure scalable APIs that seamlessly trigger Lambda functions. API Gateway can handle millions of API calls per second, ensuring high availability and low latency. Utilizing the API Gateway reduces development time for APAC creation by 20 to 30%, and provides building in features for authentication, authorization, and request violation. API gateway Caching features can reduce can reduce a backend load up to 80% for frequently accessed data, even breach. So event bridge transform application architecture by providing serverless event bus that intelligently routes events between decoupled services and third parties as providers with minimal configuration. Even Bridge can process millions of events per second with. Near real time latency, typically under 500 milliseconds. It simplifies integration with third party SaaS applications, reducing integration time by 40 to 50% even bridge filtering and routing capabilities. Reduce the complexity of managing event driven architectures, decreasing development efforts by 25 to 30% S3. S3 is called a simple storage service. It provides durable object storage that integrates seamlessly with Lambda and other server service S three's. High durability with 99.9 nines and availability of 99.99% make it ideal for storing and streaming large volumes of data. S3 when notifications can trigger Lambda functions, enabling real time data processing and analysis. Organization utilizing the serverless architectures with S3 for data storage have reported a 20 to 30% reduction in storage cost compared to traditional storage solutions. CloudWatch offers sophisticated observability capabilities providing monitoring, logging analytics for serverless applications, CloudWatch logs allows for real time log analysis. Reducing troubleshooting time by 30 to 40%. CloudWatch alarms can automatic electrical lambda functions or other AWS services in response to performance metrics, enabling automate automated remediation. CloudWatch dashboards provide a centralized review of application performance, improving the visibility and reducing meantime to detection by 20, meantime to de detection by 25 to 35%. So additional benefits and data points to consider would be simply simplified microservices architecture, the cost optimizations, increased development velocity, enhance security, streamline, CICD pipelines. So by leveraging these comprehensive ecosystems, organizations can build highly scalable, cost effective and resilient serverless applications that drive the innovation and business business growth. Let's take a look at the real world success stories. A FinTech transformation, a leading financial service provided slash infrastructure cost by 45 percent, while accelerating the time to market for a new features from months to mere days by migrating their transaction processing system to lamb and animal db. They achieved unprecedented operational efficiency and customer responsiveness. So a health healthcare analytics, a healthcare analytics platform revolutionized their operations by implementing serverless architecture that scales instantaneously during peak reporting periods. This eliminated persistent performance bottlenecks while delivering a remarkable 52% reduction in operational expenditure. Allowing resources to be redirected toward page patient care initiatives. Retail in recommendation engine, an e-commerce retailer reimagine their recommendation engine using serverless microservices dramatically reducing the response time from two weeks to just 200 milliseconds. This 10 times performance improvement drove the substantial 30% increase in conversion rates through hyper personal shopping experience that adapt in real real time to customer behavior. These transformative case studies illustrates the profound impact of serverless architecture across diverse industries. Beyond mere technical improvements organizations consistently report enhanced business agility, substantial cost savings, and new fund capacity to rapidly innovate and respond to market opportunities that were previously unattainable with traditional infrastructure. Let's take a look at DevOps cis. Continuous improvement and continuous delivery. First, serverless. So server architectures requires sophisticated DevOps approach that fundamentally embraces infrastructure as a code principles and fully automate development pipeline. So AWS code pipeline seamless integrated with Lambda to enable comprehensive continuous delivery workflows that deploy. Function. So a PA configurations and database key changes are as unified atomic unit. Studies shown that the organization adopting to mature server DevOps practices CF 40 to 50% reduction in deployment related errors. Infrastructure has code codify services resources with AWS cloud formation or the serverless framework to guarantee reproducible version called as. Infrastructure deployment across environment they are version controlled and it reduces manual intervention errors up to 60 to 70% and accelerates environment provisioning by 50 to 60%. Version controlling IAC templates with GI ensures auditability and enables rollbacks reducing recovery time by up to 30% Automated build pipeline to establish a robust continuous integration with AWS code build to automatically compiled package and validate lambda functions with the dependencies code build reduces bill times by 70 to 80% compared to manual process. Automated dependency management system ensures consistency and reduces the reduces works on my machine issues by 40 to 50%. Static code analysis and security scanning with pipeline have been shown to reduce vulnerabilities by 25 to 30%. Comprehensive testing. Deploy a thermal test environments for thorough unit integration and performance testing. Of individual functions end to end workflows. Thermal testing environments reduce the test environments reduce test environments set up by 80 to 90% and improve test reliability. Automated performances can detect re regressions early reducing the latency issues in production by 20 to 30%. Implementing contract testing for a PS ensures service compatibility, reducing integration edit by 15 to 20%. Stage deployments. Leverage AWS code pipeline with canary deployment strategies to methodo methodically introduce changes while continuously monitoring for anomalies and performance impacts. Canary deployments reduce the risk of widespread failures by 90 to 95% and enable rapid rollbacks in case of issues. Automated anomaly detection with CloudWatch alarms reduce meantime to detection by 35 to 45%. Stage deployments combined with feature flags, reduce the impact of fail deployments by up to 50%. Industry leading organizations implement sophisticated stage deployment strategies with intelligent rollback capabilities, preserving the system integrity while dramatically accelerating release cadence. This transformative approach has enabled the forward thinking companies to achieve deployment frequencies measured in hours. Sometimes minutes compared to the weeks or months required with traditional infrastructure models, specifically organizations using automated server as CICD pipelines report a 70 to 80% reduction in deployment times. Let's take a look at securing the serverless applications. Securing serverless applications require a shift in approach from traditional infrastructure protection to function level controls. Each lambda function should operate with minimal permissions, allowing the principle of leased privilege. With carefully defined IAM rules, studies indicate that the organization implementing a frying grained IAM roll reduce the risk of lateral movement after the security breach. B two 40 to 50%. Function level IAM rules implement fine grain permission boundaries by using the principle of leash privilege for each lambda function, restricting access to only required resources. Implementing function level IM roles reduces the attack surface by 20 to 30% compared to broad service wide roles. Utilizing the IM policies with resource level permissions reduces the impact of compromise Credentials by up to 35%. Secrets management restored the sensitive configuration in AWS Secrets Manager with automatic rotation. And secure retrieval by author authorized functions only. Secrets Manager reduces the risk of hard coded credentials by 60 to 70% and automates secret rotation, reducing the window of vulnerability, implement the least. Privilege access to Secrets Manager ensures that only authorized function can retrieve more sensitive data, input, validation, implement strict schema validation of API at API level boundaries. Using the API gateway request validators to prevent injection attacks. API gateway validation reduces the risk of a SQL injection and cross site. The scripting attacks by 30 to 40%. Implement input validation at the API layer reduces the load on Lambda functions, improving performance and security dependency scanning, integrate automated vulnerability scanning into CICD pipelines to detect known vulnerabilities in third party dependencies. Dependency scanning reduces the risk of exploiting known vulnerabilities by 25 to 35%. Implementing software composition analysis, SEA tools in CICD pipelines reduces the time to identify remediate vulnerabilities. Data protection remains critical with encrypted requirements for both data in transit and at rest. AWS provides tools like KMS for, encrypting key management partners and partner store for securing configuration, enabling a comprehensive security posture for serverless applications. So the additional data points and security practices to consider would be networks segmentation, runtime, security and logging and monitoring security audits, immutable deployments, ensuring that the Lambda function deployments are immutable. Preventing unauthorized modifications, web application, firewall rules encryption address, and in transit, regular security updates. This is keep all the dependencies and runtime environments up to date with the latest security patches. Automated patches reduces these vulnerability windows as well. By implementing the security practices, organizations can build secure and resilient serverless applications that protect sensitive data. And minimize the risk of security incidents. Business intelligence integration. Integrating business intelligence tools with serverless architectures creates opportunities for real time decision making. Analytics pipelines are built on event principles can process data as it generate, as it gets generated. Eliminating the batch processing delays and providing immediate visibility into business operations. Studies show that the organizations adopting serverless analytics pipelines, reduced data, progressing processing latency by 60 to 80% compared to traditional batch processing, real time analytics pipeline. Serverless architectures enable event driven analysis pipelines that process real time data delivering immediate insights rather than delayed batch processing. Even capture via even bridge even bridge allows to capture the e realtime events from various AWS services and SaaS applications. Streaming processing with kinesys enables realtime data ingestion and processing kinesys data stream scan, ingest, and process terabytes of data per hour. Providing real time data flow. Kinesys data analytics allows for a real time SQL IES on streaming data, reducing the need for complex transformation. Transformation with Lambda Transform Lambda functions, perform realtime transformations and enrichments. Lambda functions can process events within milliseconds, enabling realtime data processing. Lambda functions can integrate with machine learning morals for realtime data analysis, storage in data lakes or warehouses. S3 data lakes and Redshift. Data warehouses provide scalable and cost effective storage for process data. S3 Data Lake Scan Store petabytes of data, enabling large scale data analysis. Reshift Data Warehouse provide fast query performance for complex analytical queries. Visualization with QuickSight. QuickSight provides interactive dashboards and visualizations for real time data exploration. QuickSight can generate interactive dashboards in seconds, enabling real time data analysis. Amazon QuickSight. Paper session pricing model, quick site's, paper session pricing model reduces cost. Intermittent users organizations reported 20 to 30% reduction in bi infrastructure compared to traditional licensing model. Direct integration with AWS data sources QuickSight seamlessly integrates with AWS data sources, reducing the data preparation time. ML powered insights. QuickSight machine learning capabilities provide automated insights and anomaly detection. ML powered insights reduce the time to identify anomalies by 30 to 40% embedded analytics capabilities. QuickSight embedded analytics features enable seamless integration of dashboards and applications. Embedded analytics improve user engagement by providing data insights within the application context. Business outcomes. The integration of server serverless analytics deliver a tangible benefits beyond technical improvement, driving a better business decisions, 30% faster time to insight, reduced analytics infrastructure, cost democratized data access. Serverless analytics enables self data exploration, empowering business to access data without IT intervention improved customer experience. Real time. Insights enable personalized com customer experience and proactive service improvements. Real time data driven responses. Improve customer satisfaction score by an average of 50 to 20%. By leveraging these ties, organizations can unlock the power of real time data and significance business values. So let's talk about our serverless migration roadmap. So begin your serverless journey with a comprehensive assessment of your current architecture. Identifying the components suitable for initial migration Studies indicate that the organizations that perform a thorough assessment before migrating ca 20 to 30% reduction in migration related issues. The ideal candidates are stateless services with variable workloads. That benefit from Lambda automa automatic scaling and paper use pricing model, specifically services with intermittent traffic patterns or even driven workflows have shown to yield a 40 to 60% cost reduction when migrated to serverless initial assessment and component selection. Identify the stateless services such as a p. Points of data transformation tasks are ideal for la. Analyze work work workload patterns, identify services with variable or un unpredictable workloads services with the peak to peak to throw traffic ratios of fi five to one or higher benefit or higher benefit significantly from serverless scalability. Evaluate cost savings potential. Calculate the co potential cost savings by comparing current infrastructure costs with estimated lambda execution, cost, assess complexity and dependence and consider the data processing pipelines pilot project. And the next step would be the pilot project and foundational elements. Start with the pilot project. Begin with the pilot project to end to build team experience and validate your approach Before broader adoption, build a team experience. Pilot project helps teams to gain hands-on experience with the serverless technologies and best practices, validate your approach. Use the pilot project to validate your infrastructure as code templates and CICD pipelines create reusable patterns. Focus on creating reusable patterns and infrastructure as code templates that aate future migrations. Establish foundational elements, define standards and best practices for serverless development, securing and operation standardized practices. Reduce operational overhead by 20 to 30%. Implement observability, implement cloud, CloudWatch, and other observability tools to gain insights into application performance. Organizational change and upskilling. Invest in upskilling. Remember that serverless transformation is both a technical and an organizational change. Invest in upskilling your teams to maximize the benefits of this architectural shift. Foster a DevOps culture promoted DevOps culture that emphasizes automation, collaboration and continuous improvement. Empower autonomous teams, organize teams around business capabilities, fostering autonomy and ownership. Encourage experimentation, create a culture of experimentation and learning, allowing teams to explore new serverless technologies. Establish a center of excellence, create a serverless center of excellence to provide to provide guidance, best practices, and support for teams adopting serverless. Address security concerns proactively Address security concerns by implementing function level, IAM roles, and other security best practices. By following these guidelines, organizations can navigate the serverless journey to unlock full potential of this transformational or architectural approach. Yeah. Thank you so much. To conclude, we have seen how AWS serverless architecture empowers us to. Move beyond the limitations of monolithic SaaS. By embracing the microservices lambda and automated pipelines, we unlock potential substantial cost reductions, accelerate the development, and enhance security and scalability. This transformation requires a strategic approach. Accessing your architecture, piloting key services and investing in team upskilling. The data speaks for itself. Organizations adopting serverless experience, significant improvements, have seen significant improvements in efficiency and speed. Let's leverage the power of AWS to build agile, resilient, and cost effective SaaS applications driving the innovations and achieving tangible business outcomes. Thank you so much. For listening through my presentation, I hope you have gained some insights into the world of transforming monolithic into a cloud based approach like AWS Amazon Web Services. If you have any questions, you can always reach out to me at reach ika comp@gmail.com. I'll be more than happy to talk about this presentation or anything in general. Thank you so much. Thanks again.