Application Modernization

Modernize your apps without the rewrite

Your monolith still works. It just can't keep up. We decompose legacy applications into containerized microservices — incrementally, safely, and with zero downtime — so your teams ship faster without gambling on a ground-up rebuild.

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70%

Fewer production incidents

Faster release cycles

45%

Infrastructure cost reduction

99.95%

Uptime after modernization

The Pattern

The strangler fig approach to decomposition

Named after the tropical fig that gradually envelops its host tree, this pattern lets you replace a monolith piece by piece. No Big Bang. No feature freeze. The old system keeps running while the new architecture grows around it.

01 Wrap

Wrap

Intercept and proxy

Place an API gateway or facade in front of the monolith. All traffic flows through the new layer, giving you a seam to redirect individual routes without touching legacy code. The monolith keeps running. Users notice nothing.

02 Replace

Replace

Extract and containerize

Identify a bounded context — billing, auth, notifications — and rebuild it as an independent service running in its own container. Route traffic for that domain through the new service. One module at a time, the monolith shrinks while the new architecture grows.

03 Retire

Retire

Decommission dead code

Once a capability is fully handled by the new service, remove the corresponding code from the monolith. Fewer lines, smaller attack surface, lower cognitive load. Repeat the cycle until the monolith is either gone or reduced to a thin shell.

Each cycle — wrap, replace, retire — takes weeks, not months. After three to four cycles, most teams have extracted enough critical services that the remaining monolith is either trivial to maintain or ready to decommission entirely.

Our Process

Four phases from monolith to microservices

Every modernization engagement follows the same proven structure. We front-load discovery so the execution phases move fast and stay predictable.

Assess

We audit your codebase, dependencies, data stores, and deployment pipeline. Every service gets scored on modernization readiness — coupling, state management, build complexity, and operational risk. You walk away with a prioritized roadmap, not a slideshow.

Containerize

We package each service into an OCI-compliant container image with multi-stage builds, minimal base images, and reproducible pipelines. Secrets management, health checks, and graceful shutdown handlers are baked in from day one — not bolted on later.

Orchestrate

Containers land on Kubernetes with production-grade manifests: resource limits, pod disruption budgets, horizontal autoscalers, network policies, and service mesh integration. CI/CD pipelines deploy through staging gates with automated rollback on failure.

Optimize

After launch, we instrument everything. Request latency, error budgets, resource utilization, and cost-per-transaction are tracked in real time. We right-size pods, consolidate idle workloads, and tune autoscaling thresholds until your cluster runs lean.

Measurable Outcomes

What changes after modernization

These are real numbers from engagements we have completed — not theoretical projections. Your results will vary based on architecture and team maturity, but the directional improvements are consistent.

Metric	Before (Monolith)	After (Modernized)
Deployment frequency	Once every 2-4 weeks	Multiple times per day
Scaling	Vertical only (bigger box)	Horizontal per-service autoscaling
Infrastructure cost	$48K/month (over-provisioned)	$26K/month (right-sized)
Availability	99.5% (planned downtime)	99.95% (zero-downtime deploys)
Team velocity	3 features/quarter	12+ features/quarter
Incident response	45-minute MTTR	8-minute MTTR with auto-rollback

Deployment frequency

Before Once every 2-4 weeks

After Multiple times per day

Scaling

Before Vertical only (bigger box)

After Horizontal per-service autoscaling

Infrastructure cost

Before $48K/month (over-provisioned)

After $26K/month (right-sized)

Availability

Before 99.5% (planned downtime)

After 99.95% (zero-downtime deploys)

Team velocity

Before 3 features/quarter

After 12+ features/quarter

Incident response

Before 45-minute MTTR

After 8-minute MTTR with auto-rollback

Case Study

From 500K-line monolith to 35 microservices

Healthcare SaaS HIPAA Compliant

Healthcare platform reduces deployment time from two weeks to four hours

A mid-market healthcare SaaS provider had built their platform over eight years as a single .NET monolith. The codebase had grown to over 500,000 lines. Deployments required a two-week change advisory board cycle, full regression testing that took three days to complete, and a four-hour maintenance window on Saturday nights. Feature velocity had stalled — the team shipped three features per quarter while competitors moved weekly.

The engagement

Over sixteen weeks, we applied the strangler fig pattern to extract the most business-critical bounded contexts: patient scheduling, billing and claims processing, notification engine, document management, and the authentication and authorization layer. Each service was containerized, deployed to Kubernetes with full observability, and integrated into a new CI/CD pipeline using ArgoCD and GitHub Actions.

We implemented an event-driven architecture using Apache Kafka for inter-service communication and introduced a service mesh with Istio for mTLS, traffic management, and circuit-breaking. The remaining monolith was reduced to a thin adapter handling legacy integrations with external EMR systems — scheduled for phase two extraction.

Results after six months

4 hours

Deployment time (from 2 weeks)

Independent microservices

99.97%

Uptime (from 99.5%)

42%

Infrastructure cost reduction

Feature velocity increase

6 min

Mean time to recovery

FAQ

Common questions about application modernization

It depends on the size and complexity of the monolith, but most engagements follow a phased approach. The initial assessment takes two to four weeks. From there, extracting the first three to five microservices typically runs eight to twelve weeks. We prioritize high-value, low-risk modules first so you see production improvements within the first quarter — not after a year-long rewrite.

No, and that is precisely the point. A full rewrite is one of the highest-risk moves in software engineering. We use the strangler fig pattern to incrementally decompose your monolith. Each bounded context is extracted, containerized, and deployed independently. The legacy system keeps running while new services take over traffic route by route. You never bet the business on a Big Bang migration.

Data is usually the hardest part. We start by identifying which tables belong to which bounded context, then introduce database-per-service ownership where it makes sense. For tightly coupled schemas, we use change data capture (CDC) and event-driven patterns to synchronize state across services during the transition period. The goal is eventual data autonomy for each service — not a shared database that recreates the coupling you are trying to escape.

No. Every production cutover uses blue-green or canary deployment strategies. Traffic shifts happen gradually — one percent, then ten percent, then fifty — with automated rollback if error rates exceed your defined thresholds. We have completed modernization engagements for platforms serving millions of daily active users without a single user-facing outage during the transition.

Most teams we work with are running their first Kubernetes workloads through this engagement. We pair our engineers with your developers throughout the process — not just for knowledge transfer, but for hands-on pairing on real production work. By the end of the engagement, your team owns the platform, the runbooks, and the muscle memory to operate it independently. We also offer ongoing SRE support if you need a safety net.

We define success metrics before writing a single line of code. Typical targets include deployment frequency (from monthly to daily), mean time to recovery (under ten minutes), infrastructure cost per transaction (thirty to fifty percent reduction), and developer velocity (measured in cycle time from commit to production). Every metric is tracked in dashboards your team can access in real time, and we hold quarterly reviews against the baseline we captured during assessment.

Technology Partners

AWS Microsoft Azure Google Cloud Red Hat Sysdig Tigera DigitalOcean Dynatrace Rafay NVIDIA Kubecost

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