Best Generative AI Development Services

EPAM Systems vs Andersen: full comparison for 2026

Quick verdict

EPAM Systems (4.1/5) edges ahead of Andersen (4.1/5) overall. EPAM Systems is the better choice for global enterprises running generative AI at massive scale. Andersen is the stronger option for enterprises wanting generative AI paired with broad platform engineering. The right choice depends on your project size, budget, and required tech stack.

EPAM Systems vs Andersen: head-to-head summary

Criterion EPAM Systems Andersen
Founded 1993 2007
HQ Newtown, United States Warsaw, Poland
Team size 62,000+ 3,500+
Rating 4.1 / 5 4.1 / 5
Primary differentiator Public-company scale (NYSE: EPAM) with financial transparency few competitors offer 3,500-plus specialists across 20 global offices with a named AI and data practice
Pricing model Retainer or dedicated team, enterprise contracting Dedicated team or retainer
Min. engagement Not disclosed Not disclosed
Primary tech stack Python, OpenAI API, AWS Python, OpenAI API, .NET
Industries served Financial services, Healthcare, Retail & e-commerce, Media & entertainment Financial services, Healthcare, Logistics, Automotive

EPAM Systems vs Andersen: overview

EPAM Systems

EPAM Systems dates to 1993, co-founded in New Jersey and Minsk by Arkadiy Dobkin and Leo Lozner, and has traded on the NYSE as an S&P 500 constituent since 2012. It employed roughly 62,850 people across more than 55 countries at the end of 2025, a scale category no other agency on this list approaches. Generative AI transformation engineering is a marketed practice area, but at this size it functions as part of a much larger digital engineering business rather than a standalone specialty.

Andersen

Andersen was founded in 2007 and lists its headquarters in Warsaw, Poland, with more than 3,500 specialists across 20 office locations and 16 development centers globally. Its named AI and data practice covers generative AI consulting, machine learning, data engineering, and robotic process integration, alongside a broader stack spanning .NET, Java, Python, PHP, and Go. Industries served include financial services, healthcare, logistics, automotive, and media.

Services and capabilities: EPAM Systems vs Andersen

Capability EPAM Systems Andersen
Generative AI
Machine learning
AI agents
MLOps
AI consulting
Fixed-price projects
Dedicated team model

Tech stack comparison: EPAM Systems vs Andersen

Framework / platform EPAM Systems Andersen
Python
OpenAI API
PyTorch N/A N/A
LangChain N/A N/A
AWS
Azure N/A
Kubernetes N/A N/A

Pricing comparison: EPAM Systems vs Andersen

Criterion EPAM Systems Andersen
Minimum engagement Not disclosed Not disclosed
Engagement models Dedicated team, Retainer Dedicated team, Retainer
Rate transparency Not public Not public
Price tier Mid-market Mid-market

Target audience comparison: EPAM Systems vs Andersen

Dimension EPAM Systems Andersen
Best company size Startup to mid-market Startup to mid-market
Best industries Financial services, Healthcare, Retail & e-commerce Financial services, Healthcare, Logistics
Best use cases Running a generative AI transformation program spanning multiple business units and regions., Needing a publicly-traded vendor for audit or procurement compliance reasons. Running a generative AI initiative that needs to plug into an existing multi-technology enterprise stack., Adding robotic process integration alongside a generative AI project.
Typical project type Dedicated team Dedicated team

EPAM Systems vs Andersen: pros and cons

EPAM Systems
+ Public-company financial disclosure that no private agency on this list can match.
+ Scale to staff several large generative AI programs across regions simultaneously.
+ S&P 500 membership lets enterprise procurement teams vet it through standard due diligence.
+ Partnerships span all three major cloud hyperscalers.
- Generative AI sits inside an enormous engineering business rather than as a dedicated specialty
- Scale generally means slower onboarding and higher minimum engagement than boutique firms
Andersen
+ Large global footprint (20 offices, 16 development centers) supports concurrent enterprise programs.
+ Named AI and data practice, not a generic add-on to broader software services.
+ Nearly two decades of software delivery history across multiple technology stacks.
+ Vertical coverage spans financial services, healthcare, logistics, and automotive.
- Generative AI is one practice area within a much larger, multi-stack engineering business
- Scale typically means a more formal sales and onboarding process than boutique firms

Who should choose EPAM Systems?

A typical fit: running a generative AI transformation program spanning multiple business units and regions.

Public-company scale (NYSE: EPAM) with financial transparency few competitors offer. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail & e-commerce, Media & entertainment.

Who should choose Andersen?

A typical fit: running a generative AI initiative that needs to plug into an existing multi-technology enterprise stack.

3,500-plus specialists across 20 global offices with a named AI and data practice. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Logistics, Automotive.

Decision matrix: EPAM Systems vs Andersen

Your situation Recommended choice
You need full-ownership delivery on a defined project scope Both offer fixed-price models
You need a large dedicated team for an ongoing programme EPAM Systems
Your budget is at the lower end Compare: EPAM Systems (Not disclosed) vs Andersen (Not disclosed)
You need specialist depth in a specific vertical EPAM Systems
You need staff augmentation or team extension Neither; consider alternatives that offer staff aug
You need consulting before committing to a build EPAM Systems

Use case fit: EPAM Systems vs Andersen

Use case EPAM Systems fit Andersen fit Winner
Running a generative AI transformation program spanning multiple business units and regions. Strong Strong Both equally
Needing a publicly-traded vendor for audit or procurement compliance reasons. Strong Strong Both equally
Running a generative AI initiative that needs to plug into an existing multi-technology enterprise stack. Strong Strong Both equally
Adding robotic process integration alongside a generative AI project. Limited Strong Andersen
Fixed-price build Limited Limited Both equally
Staff augmentation Limited Limited Both equally

Verdict: EPAM Systems vs Andersen

EPAM Systems (4.1/5) is the stronger overall choice for most Generative AI Development projects. Public-company scale (NYSE: EPAM) with financial transparency few competitors offer.

Andersen (4.1/5) is worth a look if you need adding robotic process integration alongside a generative AI project. If your situation matches that, Andersen is a competitive option.

Related comparisons

EPAM Systems vs Andersen FAQ

Is EPAM Systems better than Andersen?

EPAM Systems (4.1/5) scores higher overall, but "better" depends on your use case. EPAM Systems's strongest advantage: public-company financial disclosure that no private agency on this list can match. Andersen's strongest advantage: large global footprint (20 offices, 16 development centers) supports concurrent enterprise programs.

How do EPAM Systems and Andersen differ in pricing?

EPAM Systems uses retainer or dedicated team, enterprise contracting pricing. Andersen uses dedicated team or retainer pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.

Which is better for enterprise: EPAM Systems or Andersen?

EPAM Systems is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.

What are the main differences between EPAM Systems and Andersen?

EPAM Systems's primary differentiator is: public-company scale (NYSE: EPAM) with financial transparency few competitors offer. Andersen's primary differentiator is: 3,500-plus specialists across 20 global offices with a named AI and data practice. They also differ in team size (62,000+ vs 3,500+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Financial services, Healthcare vs Financial services, Healthcare).

Verify all details directly with each company before making a decision.