Top AI Consulting Agencies

KPMG vs EPAM Systems: full comparison for 2026

Quick verdict

KPMG (4.1/5) edges ahead of EPAM Systems (4.1/5) overall. KPMG is the better choice for enterprises wanting named AI products alongside Big Four advisory. EPAM Systems is the stronger option for enterprises wanting AI advisory paired directly with engineering delivery. The right choice depends on your project size, budget, and required tech stack.

KPMG vs EPAM Systems: head-to-head summary

Criterion KPMG EPAM Systems
Founded 1987 1993
HQ London, United Kingdom Newtown, United States
Team size 251,000-275,000 62,000+
Rating 4.1 / 5 4.1 / 5
Primary differentiator Named AI products, aIQ and Mystro, instead of purely bespoke advisory engagements An engineering-heavy advisory model that pairs strategists with the actual build team
Pricing model Retainer, enterprise contracting Retainer or dedicated team, enterprise contracting
Min. engagement Not disclosed Not disclosed
Primary tech stack Python, AWS, Azure Python, AWS, Azure
Industries served Financial services, Healthcare, Manufacturing, Government Financial services, Healthcare, Retail & e-commerce, Media & entertainment

KPMG vs EPAM Systems: overview

KPMG

KPMG was formed in 1987 by the merger of Peat Marwick International and Klynveld Main Goerdeler, though its roots trace back to 1897, and runs out of London today. Headcount estimates range between roughly 251,875 and 275,288 depending on the reporting period. Its AI service line includes named products, aIQ and Mystro, for AI transformation and digital labor optimization, more productized than some Big Four peers, though how much staff is specifically dedicated to AI hasn't been disclosed.

EPAM Systems

EPAM Systems was co-founded in 1993 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. AI advisory and transformation engineering runs as a marketed practice across the firm, distinguished from pure Big Four strategy firms by EPAM's engineering-heavy delivery model: advisors sit alongside the technical staff who actually build what gets recommended.

Services and capabilities: KPMG vs EPAM Systems

Capability KPMG EPAM Systems
AI strategy consulting
Generative AI
Machine learning
Data engineering
MLOps
Fixed-price projects
Dedicated team model

Tech stack comparison: KPMG vs EPAM Systems

Framework / platform KPMG EPAM Systems
Python
AWS
Azure
Google Cloud
Kubernetes
LangChain N/A N/A
PyTorch N/A N/A

Pricing comparison: KPMG vs EPAM Systems

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

Target audience comparison: KPMG vs EPAM Systems

Dimension KPMG EPAM Systems
Best company size Mid-market to enterprise Startup to mid-market
Best industries Financial services, Healthcare, Manufacturing Financial services, Healthcare, Retail & e-commerce
Best use cases Adopting a named, productized AI tool rather than commissioning a fully bespoke build., Running an AI workforce transformation program alongside existing KPMG advisory work. Running an AI strategy engagement that needs to move directly into technical build with the same team., Needing a publicly-traded agency for audit or procurement compliance reasons.
Typical project type Retainer Dedicated team

KPMG vs EPAM Systems: pros and cons

KPMG
+ Scale at 251,000-plus people supports the largest enterprise engagements.
+ Named, productized AI tools give clients something more concrete to evaluate than a generic strategy deck.
+ Nearly 130 years of institutional history dating back to 1897.
+ A London headquarters simplifies EU and UK contracting.
- Reported headcount varies by roughly 25,000 depending on which reporting period is cited
- Big Four pricing and minimum engagement sizes exclude most small and mid-size buyers
EPAM Systems
+ Public-company financial disclosure that no privately held agency on this list can match.
+ The engineering-heavy delivery model avoids the strategy-to-build handoff gap common at pure advisory firms.
+ Enough scale to staff several large AI advisory and build programs across regions at once.
+ S&P 500 membership lets enterprise procurement teams vet the firm through standard due diligence.
- AI advisory sits inside an enormous engineering business rather than functioning as a dedicated specialty
- Enterprise scale generally means slower onboarding and a higher minimum engagement than boutique agencies

Who should choose KPMG?

A typical fit: adopting a named, productized AI tool rather than commissioning a fully bespoke build.

Named AI products, aIQ and Mystro, instead of purely bespoke advisory engagements. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Manufacturing, Government.

Who should choose EPAM Systems?

A typical fit: running an AI strategy engagement that needs to move directly into technical build with the same team.

An engineering-heavy advisory model that pairs strategists with the actual build team. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail & e-commerce, Media & entertainment.

Decision matrix: KPMG vs EPAM Systems

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 KPMG
Your budget is at the lower end Compare: KPMG (Not disclosed) vs EPAM Systems (Not disclosed)
You need specialist depth in a specific vertical KPMG
You need staff augmentation or team extension Neither; consider alternatives that offer staff aug
You need consulting before committing to a build KPMG

Use case fit: KPMG vs EPAM Systems

Use case KPMG fit EPAM Systems fit Winner
Adopting a named, productized AI tool rather than commissioning a fully bespoke build. Strong Limited KPMG
Running an AI workforce transformation program alongside existing KPMG advisory work. Strong Strong Both equally
Running an AI strategy engagement that needs to move directly into technical build with the same team. Strong Strong Both equally
Needing a publicly-traded agency for audit or procurement compliance reasons. Strong Strong Both equally
Fixed-price project Limited Limited Both equally
Dedicated team model Limited Limited Both equally

Verdict: KPMG vs EPAM Systems

KPMG (4.1/5) is the stronger overall choice for most AI Consulting projects. Named AI products, aIQ and Mystro, instead of purely bespoke advisory engagements.

EPAM Systems (4.1/5) is worth a look if you need needing a publicly-traded agency for audit or procurement compliance reasons. If your situation matches that, EPAM Systems is a competitive option.

Related comparisons

KPMG vs EPAM Systems FAQ

Is KPMG better than EPAM Systems?

KPMG (4.1/5) scores higher overall, but "better" depends on your use case. KPMG's strongest advantage: scale at 251,000-plus people supports the largest enterprise engagements. EPAM Systems's strongest advantage: public-company financial disclosure that no privately held agency on this list can match.

How do KPMG and EPAM Systems differ in pricing?

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

Which is better for enterprise: KPMG or EPAM Systems?

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

What are the main differences between KPMG and EPAM Systems?

KPMG's primary differentiator is: named AI products, aIQ and Mystro, instead of purely bespoke advisory engagements. EPAM Systems's primary differentiator is: an engineering-heavy advisory model that pairs strategists with the actual build team. They also differ in team size (251,000-275,000 vs 62,000+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Financial services, Healthcare vs Financial services, Healthcare).

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