Platform Modernization
Modernizes a legacy platform without a feature freeze: modular boundaries first, then observability and cost controls, with dual-run validation for a zero-downtime cutover.
- Timeline
- 4-8 weeks
- Team
- Solution Architect · Backend Engineer · DevOps Lead · QA Engineer
- Typical stack
- Languages: Ruby/Rails, Node.js, Python/Django/Flask, Java/Spring Boot, .NET/C#. Cloud: AWS, Azure, GCP, on-prem/hybrid. Containers: Docker, Kubernetes, ECS, AKS, GKE. CI/CD: GitHub Actions, GitLab CI, Jenkins, CircleCI, Azure DevOps. Observability: Datadog, New Relic, Prometheus/Grafana, Sentry, CloudWatch. Databases: PostgreSQL, MySQL, MongoDB, Redis, DynamoDB. API Gateway: Kong, AWS API Gateway, Azure APIM, Nginx, custom.
What you get
- Modernization blueprint with target architecture and migration phases
- Dual-run infrastructure with blue-green/canary deployment capability
- Observability stack: distributed tracing, structured logging, error budgets
- API gateway and module boundaries with contracts and versioning
- CI/CD pipeline with automated testing and rollback automation
- Security baseline: SBOM generation, SAST/DAST scanning, CSP/HSTS headers, secret rotation via KMS
- Cost plan: unit economics per request/service, autoscale policies, budget alerts and rightsizing recommendations
- Performance report: p95 latency, throughput, cost analysis, error rates
Outcomes
- Measurable p95 latency drop & infra cost reduction
- Zero-downtime cutover, dual-run validation
- Observability + error budget in place
- Faster release cadence with lower risk
Selected work
p95 latency ↓46% in 6 weeks
p95 840ms → 450ms. Re-platformed hot paths, added tracing, tuned indices. No feature freeze. Infra spend down 21% ($42k → $33k/mo); 7 critical CVEs closed before go-live.
Rails · Postgres · Grafana
450K LOC migrated. Zero downtime.
Rails 3 to 7 for a top-10 telehealth provider: 450K LOC, 2.5M patient records, 14 months, and 15,000 daily clinical users never noticed. Test coverage 35% → 85% caught 847 regressions; passed SOC 2 Type II during the migration.
Rails · PostgreSQL · AWS · HIPAA
How we approach it
Modular Monolith
- When:
- Teams <50, Rails/Django/monorepo culture, gradual migration preferred
- Tradeoffs:
- Faster deployment (4-8 weeks), simpler ops, but limited independent scaling
- Best for:
- SMB, startups scaling to mid-market, risk-averse organizations
Microservices
- When:
- Large teams (50+), polyglot requirements, independent scaling critical
- Tradeoffs:
- Maximum flexibility and scale, but higher operational complexity and DevOps maturity required
- Best for:
- Enterprise, high-growth SaaS, multi-product platforms
Strangler Fig
- When:
- Legacy systems with high risk, phased migration over 12-24 months
- Tradeoffs:
- Lowest risk with continuous delivery, but longer timeline and dual-system maintenance
- Best for:
- Government, heavily regulated industries, mission-critical systems
Hybrid (Modular + Services)
- When:
- Mid-size teams, balance of governance and flexibility needed
- Tradeoffs:
- Pragmatic approach avoiding microservices sprawl, but requires strong architectural judgment
- Best for:
- Growing companies, B2B SaaS, platform teams
Where teams use it
Finance
Legacy core banking modernization
Migrated COBOL mainframe to Java microservices with zero-downtime, achieving 78% cost reduction and enabling real-time fraud detection
Retail
E-commerce platform scalability
Modernized monolithic e-commerce to API-first architecture, handling 12x Black Friday traffic with 82% latency reduction
Healthcare
HIPAA-compliant EHR integration
Modernized patient data platform with end-to-end encryption, achieving SOC 2 Type II and 95% faster API response times
What we need from you
- Current architecture docs and pain points
- Performance baselines (p95, throughput)
- Target cost reduction %
- Release cadence goals
- Compliance/security requirements
Proof points
- Before/after p95 latency chart
- Infra cost comparison
- Observability stack (traces, logs, metrics)
- Rollback success rate
Built for procurement
- Performance SLAs: p95 latency targets, throughput guarantees, error budget commitments
- Cost projections: Infrastructure cost reduction estimates, ROI timeline, scaling cost models
- Zero-downtime guarantee: Dual-run validation, rollback procedures, incident response SLAs
- Observability deliverables: Distributed tracing, error budgets, custom dashboards, alert runbooks
- Migration evidence: Before/after performance reports, architecture diagrams, cutover checklists
- Compliance: SOC 2 Type II, HIPAA, PCI-DSS support for regulated environments
- Support: Runbook handoff, 30-day post-launch support, escalation procedures
Frequently asked questions
Yes; that is the normal case, not the exception. Before changing anything we make the system observable: tracing on real traffic, dependency mapping, and characterization tests that pin down what the system actually does, which is rarely what the documentation says. The classic landmines are undocumented scheduled jobs and integrations that only surface at month-end, so discovery runs across a full business cycle for those. Understanding is rebuilt as an output of the migration, not required as an input.
Three that surprise teams: parallel-run infrastructure (old and new stacks both live through cutover), data migration rehearsals (the first attempt never goes clean, so budget for several), and your own team's time for knowledge extraction, which no vendor can do alone. We put all three in the quote up front, because a fixed-scope engagement that excludes them is not fixed-scope, it is a change-order machine.
We use a strangler fig pattern: new services run alongside the legacy system, gradually taking over traffic. Critical paths get migrated first with automated rollback. Database migrations happen through dual-write strategies where both old and new systems stay in sync during the transition. Zero downtime means zero downtime, not "just a few minutes."
It depends on your team size and deployment frequency. If fewer than 20 engineers deploy weekly, a well-structured modular monolith is usually faster and cheaper to operate. Microservices make sense when independent teams need to deploy different components on different schedules. We assess your situation before recommending either path.
AWS, Google Cloud, and Azure. Most modernization projects land on AWS due to service breadth, but we architect for your specific requirements. Some clients use multi-cloud or hybrid setups, which we support with cloud-agnostic infrastructure-as-code using Terraform or Pulumi.
Most modernizations run 4 to 8 weeks. A short assessment and migration plan up front, then modular boundaries, database migration, and containerized deployment. The timeline moves with the number of integrations and how much data has to be migrated.