Quantiphi vs EPAM Systems: full comparison for 2026
Quick verdict
Quantiphi (4.3/5) edges ahead of EPAM Systems (4.1/5) overall. Quantiphi is the better choice for large document and contact-center AI programs. EPAM Systems is the stronger option for enterprises adding AI across many products. The right choice depends on how far your pilot has got, your budget, and who will run the system after launch.
Quantiphi vs EPAM Systems: head-to-head summary
| Criterion | Quantiphi | EPAM Systems |
|---|---|---|
| Founded | 2013 | 1993 |
| HQ | Marlborough, USA | Newtown, USA |
| Team size | 4,000 | 61,000+ |
| Rating | 4.3 / 5 | 4.1 / 5 |
| Primary differentiator | Large Google Cloud and AWS engineering bench for document and speech AI in production | Engineering capacity to put AI into dozens of products under one program |
| Pricing model | Project and dedicated-team pricing; rates not published | Time & materials and dedicated teams; rates not published |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Google Cloud, Vertex AI, AWS | AWS, Azure, Google Cloud |
| Industries served | Healthcare, Insurance, Financial services, Public sector, Media | Financial services, Healthcare, Retail, Media, Manufacturing |
Quantiphi vs EPAM Systems: overview
Quantiphi
Quantiphi was founded in 2013 and is headquartered in Marlborough, Massachusetts, with around 4,000 employees. Its own materials claim top partner tiers with Google Cloud, AWS, and NVIDIA, and ISG named it a Leader for AI and machine learning services on Google Cloud in its 2024 Provider Lens report. It has a large bench for document, speech, and contact-center AI in production, especially in healthcare, insurance, and the public sector. Big programs suit it better than one small pilot.
EPAM Systems
EPAM Systems was founded in 1993, is headquartered in Newtown, Pennsylvania, and employs more than 61,000 people. It bought NEORIS and First Derivative in recent years to expand in Latin America and financial services. Its AI work sits inside a large software engineering business, so production AI usually arrives as part of a broader platform or product program. That suits enterprises with many products more than a company with one stalled pilot.
Services and capabilities: Quantiphi vs EPAM Systems
| Capability | Quantiphi | EPAM Systems |
|---|---|---|
| LLM gateway & model routing | ✗ | ✗ |
| Regression tests on prompt and model changes | ✗ | ✗ |
| Canary or staged rollout | ✗ | ✗ |
| Hallucination and drift monitoring | ✗ | ✗ |
| MLOps & model serving | ✓ | ✓ |
| Fixed-scope pilot | ✗ | ✗ |
| Managed run after launch | ✓ | ✓ |
Tech stack comparison: Quantiphi vs EPAM Systems
| Framework / platform | Quantiphi | EPAM Systems |
|---|---|---|
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | ✓ | ✓ |
| Kubernetes | N/A | ✓ |
| MLflow | N/A | N/A |
| Databricks | N/A | ✓ |
| Amazon Bedrock | N/A | N/A |
| Vertex AI | ✓ | N/A |
| LangChain | N/A | N/A |
| Snowflake | ✓ | ✓ |
Pricing comparison: Quantiphi vs EPAM Systems
| Criterion | Quantiphi | EPAM Systems |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Fixed-scope project, Dedicated team, Managed services | Time & materials, Dedicated team, Managed services |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Quantiphi vs EPAM Systems
| Dimension | Quantiphi | EPAM Systems |
|---|---|---|
| Best company size | Mid-market to enterprise | Enterprise |
| Best industries | Healthcare, Insurance, Financial services | Financial services, Healthcare, Retail |
| Best use cases | Document AI in production for insurers and healthcare payers., Contact-center AI on Google Cloud. | AI across a portfolio of enterprise products., Production AI in banking platforms. |
| Typical project type | Fixed-scope project | Time & materials |
Quantiphi vs EPAM Systems: pros and cons
| Quantiphi | |
|---|---|
| + | ISG's 2024 Provider Lens rated its Google Cloud AI work a Leader |
| + | Large team for document, speech, and contact-center systems |
| + | Experience with healthcare and public-sector data rules |
| + | Covers both Google Cloud and AWS |
| - | Partner-tier claims come mostly from its own recruiting material |
| - | Big-program orientation makes small pilots relatively costly |
| - | Rates not published |
| EPAM Systems | |
|---|---|
| + | Very large engineering bench |
| + | Strong software delivery practice to build on |
| + | Financial-services depth added through First Derivative |
| + | Can run systems after launch |
| - | Recent acquisitions (NEORIS, First Derivative) are still being absorbed |
| - | Too large and slow for one small pilot |
| - | Rates not published |
Who should choose Quantiphi?
A typical fit: document AI in production for insurers and healthcare payers.
Large Google Cloud and AWS engineering bench for document and speech AI in production. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Insurance, Financial services, Public sector, Media.
Who should choose EPAM Systems?
A typical fit: AI across a portfolio of enterprise products.
Engineering capacity to put AI into dozens of products under one program. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail, Media, Manufacturing.
Decision matrix: Quantiphi vs EPAM Systems
| Your situation | Recommended choice |
|---|---|
| Your pilot works but nobody trusts it in production yet | Ask each for its release and rollback process |
| Model API spend is growing faster than usage | Neither lists a gateway; ask how cost per feature is tracked |
| You need someone to watch quality after launch | Both offer a managed run |
| You run classic ML models that need serving and retraining | Both cover MLOps |
| You want a priced pilot before a full rollout | Neither advertises one; ask for a scoped quote |
| Your budget is at the lower end | Compare: Quantiphi (Not disclosed) vs EPAM Systems (Not disclosed) |
| You need a large team for many products at once | EPAM Systems |
Use case fit: Quantiphi vs EPAM Systems
| Use case | Quantiphi fit | EPAM Systems fit | Winner |
|---|---|---|---|
| Document AI in production for insurers and healthcare payers. | Strong | Limited | Quantiphi |
| Contact-center AI on Google Cloud. | Strong | Limited | Quantiphi |
| AI across a portfolio of enterprise products. | Limited | Strong | EPAM Systems |
| Production AI in banking platforms. | Limited | Strong | EPAM Systems |
Verdict: Quantiphi vs EPAM Systems
Quantiphi (4.3/5) is the stronger overall choice for most AI Implementation projects. Large Google Cloud and AWS engineering bench for document and speech AI in production.
EPAM Systems (4.1/5) is worth a look if you need production AI in banking platforms. If your situation matches that, EPAM Systems is a competitive option.
Related comparisons
Quantiphi vs EPAM Systems FAQ
Is Quantiphi better than EPAM Systems?
Quantiphi (4.3/5) scores higher overall, but "better" depends on your use case. Quantiphi's strongest advantage: ISG's 2024 Provider Lens rated its Google Cloud AI work a Leader. EPAM Systems's strongest advantage: very large engineering bench.
How do Quantiphi and EPAM Systems differ in pricing?
Quantiphi's pricing: project and dedicated-team pricing; rates not published. EPAM Systems's pricing: time & materials and dedicated teams; rates not published. Any hourly bands shown come from Clutch, not a published rate card, so a scoping call is still needed for a project quote.
Which is better for enterprise: Quantiphi or EPAM Systems?
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 provider before shortlisting.
What are the main differences between Quantiphi and EPAM Systems?
Quantiphi's primary differentiator is: large Google Cloud and AWS engineering bench for document and speech AI in production. EPAM Systems's primary differentiator is: engineering capacity to put AI into dozens of products under one program. They also differ in team size (4,000 vs 61,000+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Healthcare, Insurance vs Financial services, Healthcare).
Verify all details directly with each provider before making a decision.