FinServe Capital
Cloud Migration & Modernization
Industry
Financial Services
Category
Cloud Infrastructure
Uptime
99.999%
Deployments
Daily
From Monolith to Microservices on AWS
iDOWS Apex partnered with FinServe Capital to migrate a legacy monolith to a microservices architecture on AWS.
The migration improved system uptime to 99.999% and enabled daily deployments — a shift that also reduced FinServe's deployment time by 80%, according to FinServe's own VP Engineering.
99.999%
System Uptime
Daily
Deployments Enabled
AWS
Cloud Platform
80%
Faster Deployments
The Problem
Financial services companies running legacy monolithic systems commonly face slow, high-risk release cycles, difficulty scaling individual parts of the system independently, and uptime pressure that only intensifies as the business grows. These are general industry pressures — the specific internal architecture FinServe ran prior to this engagement beyond "a legacy monolith" is not detailed in available materials.
- Migrate a legacy monolith without disrupting live operations
- Move to an architecture that supports independent, frequent releases
- Improve system uptime to meet financial-services reliability expectations
- Adopt AWS as the cloud platform for the modernized system
The Approach
iDOWS Apex migrated FinServe's legacy monolith to a microservices architecture on AWS, applying the same DevOps discipline we bring to client engagements of this kind.
The Result
99.999% system uptime, daily deployments, and an 80% reduction in deployment time.
Microservices Migration
The legacy monolith was migrated to a microservices architecture, decoupling components so they could be built, tested, and released independently.
AWS Cloud Platform
AWS was the confirmed cloud platform underpinning the new architecture. Specific AWS service selections beyond this are not publicly disclosed for this engagement.
Uptime Improvement
The modernized system reached 99.999% uptime, a confirmed, measured outcome of this migration.
Daily Deployments
The new architecture enabled daily deployments, replacing the slower release cadence typical of monolithic systems.
DevOps Integration
Delivered using the same DevOps methodology we apply across engagements of this kind — see below for how we typically approach problems in this class.
Faster Delivery
According to FinServe's VP Engineering, the migration reduced deployment time by 80% and their team integrated smoothly with ours.
Specific architecture diagrams and infrastructure details for the FinServe engagement beyond "microservices on AWS" are not publicly disclosed. What follows describes our standard, general approach to monolith-to-microservices migrations — it is a methodology description, not a specific claim about FinServe's implementation.
- Service DecompositionThe monolith is typically broken down into independently deployable services along clear business-domain boundaries.
- Cloud InfrastructureServices are provisioned on cloud infrastructure — AWS in this engagement — designed for independent scaling.
- CI/CD PipelinesAutomated release pipelines are typically built so each service can ship on its own schedule, supporting daily deployments.
- Reliability EngineeringRedundancy, health checks, and rollback-capable deployments are typically layered in to support high-uptime targets.
How We Typically Approach This
The steps below describe iDOWS Apex's standard delivery methodology for cloud migration engagements generally, not specific, disclosed details of the FinServe project timeline or team.
Assessment & Planning
Audit the legacy monolith and plan service boundaries and migration sequencing.
Infrastructure Setup
Provision AWS cloud infrastructure to host the new microservices architecture.
Incremental Migration
Migrate services incrementally to minimize disruption to production traffic.
CI/CD Enablement
Build automated pipelines that support independent, frequent deployments.
Reliability Validation
Validate uptime and rollback behavior before full cutover.
"The cloud migration and DevOps implementation by IDOWS reduced our deployment time by 80%. Their team integrated perfectly with ours, making the transition smooth."
Emily Watson
VP Engineering, FinServe
Measurable Outcomes
The migration delivered three confirmed, measured business outcomes for FinServe Capital.
99.999% Uptime
System uptime improved to 99.999% following the migration to microservices on AWS.
Daily Deployments
The new architecture enabled daily deployments, up from the slower cadence of the legacy monolith.
80% Faster Deployments
Per FinServe's VP Engineering, deployment time was reduced by 80% after the migration.
Beyond FinServe's own account that the team "integrated perfectly with ours, making the transition smooth," detailed lessons-learned documentation for this engagement is not publicly available. As a general principle, monolith-to-microservices migrations of this type typically reinforce the value of incremental cutover and strong CI/CD foundations in achieving both high uptime and frequent, low-risk deployments.
Related Guide
Learn more about our general approach to cloud engineering.
Read the Cloud Engineering GuideWhat does migrating a monolith to microservices on AWS typically involve?
It generally involves decomposing a legacy monolithic codebase into independently deployable services, standing up AWS infrastructure to host and orchestrate them, and building out CI/CD pipelines so each service can ship independently rather than through a single, coordinated release.
How much downtime should we expect during a cloud migration?
Our standard methodology favors phased, incremental migration strategies designed to minimize disruption to production traffic. In this engagement, the migration ultimately improved system uptime to 99.999%.
Can microservices architecture really enable daily deployments?
Yes — decoupling services so each can be built, tested, and released independently removes the bottleneck of a single monolithic release train. In this engagement, that shift enabled daily deployments and reduced deployment time by 80%.
What AWS services are commonly used in this type of migration?
AWS was the confirmed cloud platform for this engagement's microservices architecture. Beyond that, specific AWS service selections (compute, orchestration, networking, etc.) are tailored per client and are not publicly disclosed for this engagement.
How do you ensure high availability during and after migration?
Our standard approach layers in redundancy, automated health checks, and rollback-capable deployment pipelines so that individual service failures don't cascade into system-wide outages, which is the kind of practice that supports uptime targets like the 99.999% achieved here.
How long does a legacy-to-microservices migration usually take?
Timelines depend heavily on the size and complexity of the legacy system being migrated. Specific engagement timeline and team composition are not publicly disclosed for this client.