EPAM Systems
Publicly traded (NYSE: EPAM) software engineering company founded in 1993, named 2025 AWS Global Innovation Partner of the Year.
Founded 1993 | Newtown, USA | 10,000+ employees
custom-model-trainingmlops-pipelinemodel-deploymentml-infrastructure
What is EPAM Systems?
EPAM Systems was founded in 1993 in Newtown, Pennsylvania by Arkadiy Dobkin and Leo Lozner, and has grown into a publicly traded (NYSE: EPAM) global engineering company with more than 53,000 employees. EPAM's AI/ML practice includes model development and deployment on Amazon SageMaker and Amazon Bedrock, MLOps, and its proprietary DIAL platform, an enterprise AI orchestration layer. The company was named AWS Global Innovation Partner of the Year in 2025 and holds AWS Premier Tier Services Partner status, reflecting deep hyperscaler-certified delivery capability at very large scale.
EPAM Systems was founded in 1993 and is headquartered in Newtown, USA. The firm employs 10,000+ people and works primarily with clients in Financial services, Life sciences, Media, Travel and hospitality sectors. Its primary differentiator is: Proprietary EPAM DIAL platform for enterprise AI orchestration, combined with the 2025 AWS Global Innovation Partner of the Year distinction, an award-level differentiator not held by most peers.
EPAM Systems tech stack and services
AWS SageMakerAmazon BedrockEPAM DIAL (proprietary)PythonKubernetes
| Service area |
|---|
| Custom Model Training |
| MLOps Pipeline |
| Model Deployment & Serving |
| ML Infrastructure Management |
EPAM Systems use cases
Short answer: EPAM Systems is best suited for very large enterprises, proprietary AI orchestration platform.
| Use case |
|---|
| Very large enterprises needing an AWS Global Partner of the Year-caliber vendor for ML platform work |
| Deploying models on Amazon SageMaker or Bedrock with EPAM's proprietary DIAL orchestration layer |
| Running a global-scale MLOps program spanning multiple business units |
| Financial services and life sciences companies needing audited, publicly traded vendor accountability |
EPAM Systems pricing
Short answer: EPAM Systems uses a not published; enterprise project engagements pricing approach. Minimum engagement starts at Not published.
| Engagement model | Typical range | Best for |
|---|---|---|
| Enterprise project engagement | Variable; depends on team size | Large programmes or team augmentation |
| Managed AI services | Variable; depends on team size | Large programmes or team augmentation |
EPAM Systems does not publish a public rate card. Contact them directly via their website to get project-specific pricing.
EPAM Systems pros and cons
| Advantages | Things to consider |
|---|---|
| +2025 AWS Global Innovation Partner of the Year, an independently awarded distinction from AWS itself. | -Very large, generalist software engineering brand means ML/AI is one of many practice areas, not a dedicated specialization. |
| +Proprietary DIAL orchestration platform provides a differentiated technical asset beyond standard consulting delivery. | -No clearly located aggregate Clutch/G2 star rating specific to its AI practice in available public sources. |
| +Publicly traded (NYSE: EPAM) with substantial financial transparency and scale (53,000+ employees). | -Pricing model and minimum engagement are not published, and enterprise minimums are typically substantial. |
| +AWS Premier Tier Services Partner status confirms deep, audited hyperscaler certification. | -Named client-specific ML case studies were not clearly surfaced in available search results. |
EPAM Systems vs alternatives
How EPAM Systems compares to the other top ML Model Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Mid-market fintech and SaaS, hybrid forecasting models. | Combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in. | 4.8 | Full comparison |
| Neurons Lab | Financial services, engineering-led production AI. | End-to-end delivery model from use-case scoping to continuous production support, with declared depth in financial services. | 4.6 | Full comparison |
| DataRoot Labs | Startups needing a senior AI-only team, LLM/CV projects. | Has never diversified beyond AI/ML services, and backs its delivery bench with an in-house ML training program (DataRoot University). | 4.6 | Full comparison |
| Miquido | Companies wanting ML/CV bundled with mobile/web engineering. | Combines a large, review-verified product engineering practice with a dedicated AI/ML/CV specialization, useful for teams needing both app and model work from one vendor. | 4.6 | Full comparison |
| Provectus | Mid-market companies, cloud data plus ML in one. | Grew out of cloud and big-data engineering roots, giving it particular strength in the data infrastructure layer underneath ML models, not just the models themselves. | 4.5 | Full comparison |
| Neoteric | Organizations wanting a structured AI feasibility phase. | Two-decade operating history combined with a formal upfront feasibility-assessment stage before any model-building work begins. | 4.5 | Full comparison |
| Addepto | Cost-conscious teams, MLOps consulting or advisory-only. | Dedicated MLOps-consulting service line and Clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option. | 4.4 | Full comparison |
| N-iX | Enterprise buyers, certified data-platform plus ML partner. | Broadest cloud certification footprint in this comparison (350+ across five major platforms), backed by a 200+ person dedicated data practice. | 4.4 | Full comparison |
| InData Labs | Companies needing predictive-analytics or CV, documented accuracy. | Publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims. | 4.3 | Full comparison |
| MobiDev | Small/mid-size companies, dedicated ML consulting arm. | Historical Clutch #1 ranking for machine learning development (2021) combined with a specifically SME-oriented service model. | 4.3 | Full comparison |
| Sciforce | Research-oriented boutique, NLP, DSP, computer vision. | R&D-first culture with named specializations in digital signal processing and NLP that are less commonly offered as distinct practice areas by peers. | 4.2 | Full comparison |
| Sigmoid | Enterprises with data-infrastructure bottlenecks ahead of ML. | Data-engineering-first approach with 950+ multi-cloud certified engineers, positioning it as an infrastructure specialist that also delivers ML rather than the reverse. | 4.2 | Full comparison |
| Tredence | Enterprises, vertical analytics for supply chain at scale. | Venture-backed growth trajectory ($205M raised) with named specialization in supply chain and customer analytics rather than generic horizontal AI consulting. | 4.2 | Full comparison |
| Quantiphi | AWS-standardized enterprises, deepest AWS AI/ML credentials. | Deepest AWS-specific partnership credentials among firms researched, including AWS GenAI Innovation Center preferred-partner status. | 4.2 | Full comparison |
| Sigma Software Group | Companies wanting a Snowflake-certified data platform plus ML. | Snowflake AI Data Cloud partnership combined with unusually broad industry diversification (AdTech through aviation to gaming). | 4.1 | Full comparison |
| Intellectsoft | Companies wanting enterprise clients, dedicated AI Lab. | Unusually strong roster of large, publicly named enterprise clients (EY, Qualcomm, London Stock Exchange) for a company of its relatively modest team size. | 4.1 | Full comparison |
| ELEKS | Enterprises, established European engineering plus data science. | One of the longest operating histories (since 1991) among firms researched for this list, predating the AI consulting boom by decades. | 4.1 | Full comparison |
| Fractal Analytics | Large enterprises, scaled AI partner with a research... | Maintains a dedicated internal foundational AI research team alongside client delivery work, and is now a publicly listed company (NSE/BSE) rather than privately held like most peers of similar size. | 4.1 | Full comparison |
| Xebia | Enterprises, craftsmanship-rooted production AI consultancy. | Quarter-century software craftsmanship and technical training heritage now applied specifically to production AI/ML delivery rather than AI strategy alone. | 4.0 | Full comparison |
| Grid Dynamics | Fortune 1000 companies, publicly-traded ML partner. | The only publicly traded company (NASDAQ: GDYN) in this comparison among the mid-to-large tier, giving buyers audited financial transparency unavailable from private peers. | 4.0 | Full comparison |
| Iterate.ai | Regulated enterprises, fully private-infrastructure AI deployment. | Purpose-built for on-premise/private-infrastructure AI deployment, so client data and proprietary code never leave the client's own environment. | 4.0 | Full comparison |
| Modus Create | Distributed orgs, remote-first data plus AI/ML delivery. | Structured AI Data Foundation assessment methodology that explicitly evaluates data readiness before committing to model development. | 4.0 | Full comparison |
| Aptus Data Labs | Boutique India-based data engineering, AWS AI depth. | Combines core data engineering consulting with specific AWS AI service implementation expertise in a boutique-sized team. | 4.0 | Full comparison |
| SoftServe | Enterprises, edge CV and asset-monitoring ML at scale. | Only company in this list simultaneously holding AWS Premier, Google Cloud AI/ML Specialization, and NVIDIA Elite Consulting Partner status, reflecting particular strength in edge and GPU-accelerated computer vision. | 4.0 | Full comparison |
| DataRobot | Enterprises standardizing on one automated ML platform. | The only platform-first vendor in this comparison, meaning model development work happens on and around DataRobot's own automated ML software rather than being platform-agnostic. | 3.9 | Full comparison |
| Persistent Systems | Mid-market/enterprise buyers, MLOps plus explainable-AI accelerators. | Purpose-built DxH accelerator suite for MLOps and bias detection, plus a specific Everest Group Leader ranking in the mid-market Data & AI segment rather than only the largest enterprise tier. | 3.9 | Full comparison |
| Globant | Large enterprises, pre-packaged industry "AI Pods". | Only company in this list organized around a formal "studio + AI Pods" delivery model, and the only one with an IDC MarketScape Worldwide Leader in AI Services designation. | 3.9 | Full comparison |
| LTIMindtree | BFSI and tech enterprises, dedicated ModelOps and governance. | Explicit ModelOps templates and model-governance/responsible-AI tooling as named, productized capabilities rather than only bespoke consulting delivery, backed by an IBM watsonx Center of Excellence. | 3.9 | Full comparison |
| Cognizant | Healthcare-focused enterprises, large AI/analytics consulting bench. | Dedicated, named MLOps platform specifically built for healthcare, combined with one of the largest disclosed data/AI consultant headcounts (23,000+) in this comparison. | 3.9 | Full comparison |
| HCLTech | Very large enterprises, full-stack AI from chip to... | Unusually broad "chip-to-cloud" AI stack claim backed by two named proprietary platforms (Graviton for ML development, AION for AI lifecycle management), a combination not matched by most peers in this list. | 3.9 | Full comparison |
| Infosys | Global enterprises, large library of reusable AI assets. | Largest disclosed library of reusable, pre-trained AI assets in this comparison (12,000+ assets, 150+ pre-trained models), positioned to accelerate delivery versus fully bespoke builds. | 3.9 | Full comparison |
| Accenture | Largest global enterprises, AI within multi-year transformation. | By far the largest scale of any company in this comparison (approximately 779,000 employees, $69.67B FY2025 revenue), trading breadth and compliance maturity for less niche, hands-on model-engineering depth than boutique specialists. | 3.9 | Full comparison |
| Devbridge (a Cognizant company) | Clients wanting Devbridge's model within Cognizant's structure. | The clearest ownership-change disclosure in this comparison: a formerly independent boutique now operating explicitly as a Cognizant subsidiary, combining boutique delivery heritage with large-parent-company backing. | 3.8 | Full comparison |
EPAM Systems FAQ
What is EPAM Systems?
EPAM Systems was founded in 1993 in Newtown, Pennsylvania by Arkadiy Dobkin and Leo Lozner, and has grown into a publicly traded (NYSE: EPAM) global engineering company with more than 53,000 employees. EPAM's AI/ML practice includes model development and deployment on Amazon SageMaker and Amazon Bedrock, MLOps, and its proprietary DIAL platform, an enterprise AI orchestration layer. The company was named AWS Global Innovation Partner of the Year in 2025 and holds AWS Premier Tier Services Partner status, reflecting deep hyperscaler-certified delivery capability at very large scale.
How much does EPAM Systems charge?
EPAM Systems uses not published; enterprise project engagements pricing. Minimum engagement starts at Not published. A discovery call is required to get project-specific quotes.
What tech stack does EPAM Systems use?
EPAM Systems works with AWS SageMaker, Amazon Bedrock, EPAM DIAL (proprietary), Python, Kubernetes. Primary industries served include Financial services, Life sciences, Media, Travel and hospitality.
Is EPAM Systems right for enterprise?
Very large enterprises, proprietary AI orchestration platform. 10,000+ team size. Key consideration: Very large, generalist software engineering brand means ML/AI is one of many practice areas, not a dedicated specialization.
What are the best EPAM Systems alternatives?
The best alternatives to EPAM Systems depend on your use case. Top options are:
- Tensorway: combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in.
- Neurons Lab: end-to-end delivery model from use-case scoping to continuous production support, with declared depth in financial services.
- DataRoot Labs: has never diversified beyond ai/ml services, and backs its delivery bench with an in-house ml training program (dataroot university).
Compare EPAM Systems with other ML Model Development companies
EPAM Systems vs Tensorway EPAM Systems vs Neurons Lab EPAM Systems vs DataRoot Labs EPAM Systems vs Miquido EPAM Systems vs Provectus EPAM Systems vs Neoteric EPAM Systems vs Addepto EPAM Systems vs N-iX EPAM Systems vs InData Labs EPAM Systems vs MobiDev EPAM Systems vs Sciforce EPAM Systems vs Sigmoid EPAM Systems vs Tredence EPAM Systems vs Quantiphi EPAM Systems vs Sigma Software Group EPAM Systems vs Intellectsoft EPAM Systems vs ELEKS EPAM Systems vs Fractal Analytics EPAM Systems vs Xebia EPAM Systems vs Grid Dynamics EPAM Systems vs Iterate.ai EPAM Systems vs Modus Create EPAM Systems vs Aptus Data Labs EPAM Systems vs SoftServe EPAM Systems vs DataRobot EPAM Systems vs Persistent Systems EPAM Systems vs Globant EPAM Systems vs LTIMindtree EPAM Systems vs Cognizant EPAM Systems vs HCLTech EPAM Systems vs Infosys EPAM Systems vs Accenture EPAM Systems vs Devbridge (a Cognizant company)