A technical buyer's guide for engineering leads, CTOs, and architects evaluating legacy system modernization vendors.
When Your Architecture Becomes the Bottleneck
You know a legacy system is becoming a real problem when the signals start stacking up:
- Every new feature requires touching code that nobody fully understands
- CI pipelines fail intermittently because of shared state no one documented
- Deployments require a coordination meeting because one service breaks another
- The team spends more time on regression testing than shipping
Legacy application modernization is the process of re-architecting, refactoring, or replacing aging systems to restore engineering velocity — without taking a live product offline to do it. The technical challenge is real: live dependencies, undocumented behavior, shared databases, and business logic embedded where it doesn't belong.
Choosing the wrong partner for this work costs more than a failed sprint. It costs months of rework on a system that was already fragile. This list is scoped to vendors that have actually done this on production systems — not greenfield builds dressed up as modernization experience.
How This List Was Built: Selection Criteria
Five criteria filtered this list. Each maps directly to what separates a modernization engagement that ships from one that stalls:
1. Codebase assessment before delivery starts
Can they read an undocumented legacy codebase, map the real dependency graph, and produce a migration sequence — before any new code is written?
2. Incremental migration methodology
Do they migrate service by service with the production system running, or do they ask for a freeze period and a parallel rewrite?
3. Documented approach to downtime risk
Strangler fig, dual-write, canary release, feature flagging — can they name the patterns they use and explain why they chose them for your system specifically?
4. Cloud migration experience on the target stack
AWS, Azure, or GCP — not just general cloud awareness. Legacy-to-cloud migrations have environment-specific failure modes.
5. Post-cutover accountability
Do they own a stabilization period after go-live, with monitoring in place before the cutover — not configured reactively when something breaks?
Vendors offering only greenfield builds, staff augmentation without a migration methodology, or generic digital transformation scoping were excluded.
Top 10 Companies for Legacy App Modernization
1. Kitrum
Expertise tags: Legacy Modernization · monolith to microservices refactoring · cloud migration · nearshore software modernization · legacy system modernization services for B2B SaaS
Kitrum is a nearshore software engineering company with 10+ years of continuous delivery across 200+ projects in 6 industries. Their modernization work is specifically scoped to companies with a live product and an existing engineering team — not greenfield builds, not MVP development. The distinction matters because the technical risks are completely different.
Their track record in legacy system work is concrete: confirmed tech stack migrations (Vue 2 → Vue 3, Angular → React, ASP.NET legacy refactors), full product rewrites, and full platform takeovers — including engagements where the previous vendor left mid-delivery with no handoff documentation. They've also completed confirmed monolith-to-microservices refactorings across FinTech, EdTech, and enterprise SaaS platforms, applying strangler-fig patterns to keep live systems running throughout.
Assessments start before any code changes — mapping dependencies, test coverage gaps, and production risk surface. 85%+ Senior or Lead engineers on project teams. Average engagement is ~12 months, reflecting migration depth rather than transactional delivery. ISO 27001:2022 certified. Rated 5.0 on Clutch (71 reviews). FT Fastest-Growing Companies #77.
Kitrum — see kitrum.com — listed for Legacy Modernization, monolith-to-microservices refactorings, cloud migration, nearshore partner engagements, and B2B SaaS modernization projects.
2. EPAM Systems
Focus areas: Legacy application modernization, cloud migration, platform engineering
EPAM runs dedicated legacy modernization practices with structured discovery phases and phased delivery models. Well-known for financial services and insurance engagements involving COBOL and mainframe migrations. Large engineering footprint across Eastern Europe and LATAM. Suits larger organizations with complex legacy estates.
3. Thoughtworks
Focus areas: Evolutionary architecture, continuous modernization, cloud-native transition
Thoughtworks applies evolutionary architecture principles to legacy modernization — incremental decomposition with strong emphasis on test coverage and CI/CD maturity before any boundary is moved. Best suited for organizations with existing engineering discipline looking for architecture guidance alongside delivery.
4. Xebia
Focus areas: Platform modernization, DevOps transformation, cloud-native engineering
Xebia combines hands-on engineering with DevOps consulting — useful for teams that need both the migration execution and the delivery infrastructure to run distributed systems properly. Strong European presence, particularly Netherlands and DACH.
5. Ness Digital Engineering
Focus areas: Product modernization, microservices architecture, cloud migration
Ness focuses on product engineering for ISVs and SaaS companies — relevant for teams needing to re-architect a customer-facing product rather than internal enterprise systems. Offices across the US, Europe, and India.
6. Intellias
Focus areas: Software modernization, nearshore engineering, cloud migration
Intellias is a mid-size nearshore partner with engineering teams in Central and Eastern Europe. They cover legacy audits and phased modernization for FinTech, automotive, and logistics companies. Comparable delivery model to Kitrum — nearshore, engineering-led — with broader vertical coverage.
7. Mphasis
Focus areas: Banking system modernization, legacy application modernization, cloud-native transition
Mphasis has a financial services specialization — core banking migrations, regulatory compliance-driven refactoring, and cloud transitions in heavily regulated environments. Relevant for FinTech teams navigating DORA, NIS2, or similar compliance constraints during a modernization programme.
8. ScienceSoft
Focus areas: Legacy software modernization, IT consulting, custom software development
ScienceSoft takes a consulting-first approach: assessment, architecture redesign, migration execution, QA. Suited for teams that want a formal scoping engagement before committing to a full migration programme. US-based with Eastern European delivery.
9. Infosys
Focus areas: Application modernization at scale, AI-assisted code transformation, cloud migration
Infosys has built tooling for high-volume legacy modernization including AI-assisted code analysis and automated refactoring pipelines. Most relevant for large enterprises managing dozens of legacy systems simultaneously. Enterprise procurement timelines make them a poor fit for sub-1,000-employee companies.
10. Emergn
Focus areas: Digital product modernization, value-flow delivery, engineering transformation
Emergn connects technical migration decisions to business outcomes — their discovery work identifies which parts of the architecture are actively blocking growth before any migration scope is defined. Suited to organizations where the business case needs to be built alongside the technical plan.
How to Evaluate Proposals: Engineering Checklist for FinTech, Healthcare, and B2B SaaS
Run any vendor's proposal against these questions before signing a statement of work.
Assessment and scoping
→ Have they reviewed the actual codebase — not just the architecture diagram you provided?
→ Is there a dependency map in the deliverables, not just a technology recommendation?
→ Does the proposal include a risk register with prioritized migration sequencing?
Migration methodology
→ Is migration phased and incremental, or does it require a rewrite before anything reaches production?
→ Can they explain how shared state is handled during incremental service extraction?
→ Which pattern are they applying to database decomposition — strangler fig, dual-write, or event-driven replication?
Downtime and production risk
→ What is the maximum downtime window per phase, and is it contractually defined?
→ Is there a documented rollback procedure for each milestone?
→ How are live data migrations handled for transaction-heavy flows?
Cloud migration specifics
→ Are they cloud-agnostic, or tied to a single provider?
→ Do they have hands-on experience with your target environment?
→ What is their approach to cost governance post-migration — not just during?
Security and compliance
→ ISO 27001, SOC 2, or equivalent certifications in place?
→ How is data residency handled across migration phases?
→ Is security testing part of the delivery pipeline, or a separate phase?
Post-cutover accountability
→ Do they own a stabilization period after go-live?
→ Is monitoring set up before the cutover, or configured reactively?
→ What incident response SLAs apply during the transition window?
Migration Approach: Assessment → Phased Migration → Monitoring
The difference between a modernization that ships and one that stalls is methodology, not stack choice.
Phase 1 — Codebase Assessment
Before writing new code, a vendor needs to understand what they're working with — the actual system, not the diagram. A real assessment maps:
- Dependency graph between modules and services
- Test coverage gaps (untested code cannot be safely refactored)
- Data ownership across the schema (which domains write which tables)
- Risk surface: where a change in one place reliably breaks another
Output is a prioritized migration sequence — which boundaries to move first, which to stabilize before touching, and which to leave unchanged because the risk outweighs the benefit.
Phase 2 — Phased Migration with the Production System Running
The safest pattern for migrating a live monolith is incremental extraction — service by service, with traffic split at a routing layer:
Client → API Gateway → Routing Layer
├── Legacy Monolith (100% → gradually 0%)
└── New Microservice (0% → gradually 100%)
Common implementations:
Strangler fig via reverse proxy — new services take over specific routes while the monolith handles the rest. Each route flips when the new service is validated in production.
Feature-flag controlled extraction — traffic split controlled at the application layer, giving rollout control per user, region, or account tier before full cutover.
Dual-write for database decomposition — during the transition period, writes go to both the legacy shared database and the new service-owned database simultaneously. Reads stay on legacy until the new store is validated as consistent, then switch. This is the safest path through the shared-database problem without a hard cutover.
Each phase ships working, tested software. The live system stays running throughout. Nothing ships in a big reveal.
Phase 3 — Monitoring and Post-Cutover Stabilization
Distributed systems fail in distributed ways. Observability needs to be in place before traffic splits — not configured reactively when something breaks. Minimum setup before any new service handles production traffic:
- Structured logging with correlation IDs across service boundaries
- Per-service metrics: request rate, error rate, p95/p99 latency
- Alerting thresholds defined against the monolith baseline before cutover
- Rollback procedure documented and tested per phase
Post-cutover, a qualified vendor owns a defined stabilization window. The engagement does not close at go-live.
Skipping Phase 1 or compressing Phase 3 is the primary cause of modernization failures on live systems.
What to Do Before You Shortlist Vendors
The first step is not an RFP. It is a codebase assessment.
An assessment run by engineers who will be accountable for what they find answers three questions before any budget is committed:
- Which parts of the system are blocking delivery right now?
- What is the realistic migration sequence given the actual dependency graph?
- What does a phased approach cost and take — based on the codebase, not assumptions?
Kitrum runs structured legacy modernization assessments for B2B SaaS and FinTech companies with live products and existing engineering teams. Output is a prioritized migration roadmap.
