EPAM Systems
Global software engineering leader with AI-native engineering and a 50,000-strong delivery organisation
Founded 1993 | Newtown, PA | 50,000+ employees | Last updated: July 2026
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What is EPAM Systems?
EPAM Systems is a global software engineering and IT services company founded in 1993 and headquartered in Newtown, PA, with 50,000+ professionals. The firm offers AI-native engineering services with a focus on scaling ML, generative AI, and agentic systems across large enterprises with strict governance requirements. EPAM is a powerhouse for building complex, software-heavy AI products from scratch, though it comes at a premium price point.
EPAM Systems was founded in 1993 and is headquartered in Newtown, PA. The firm employs 50,000+ people and works primarily with clients in Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial, Media & Entertainment, Retail & E-commerce sectors. Its primary differentiator is: AI-native engineering practice at 50,000-person scale — the broadest talent pool and delivery capacity of any firm on this list.
EPAM Systems tech stack and services
PythonTensorFlowPyTorchAWSAzureGCPKubernetesApache SparkJava.NET
| Service area | Details |
|---|---|
| Global AI transformation programme for Fortune 100 enterprise with multi-year delivery scope | Available for Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial, Media & Entertainment, Retail & E-commerce clients |
| Enterprise GenAI platform with strict governance and compliance for regulated financial institution | Available for Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial, Media & Entertainment, Retail & E-commerce clients |
| Complex ML product build requiring hundreds of engineers across multiple technical disciplines | Available for Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial, Media & Entertainment, Retail & E-commerce clients |
| Agentic AI system for large enterprise with orchestration, memory, and multi-step automation | Available for Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial, Media & Entertainment, Retail & E-commerce clients |
| AI strategy and architecture for multinational company entering ML at scale | Available for Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial, Media & Entertainment, Retail & E-commerce clients |
EPAM Systems use cases
Short answer: EPAM Systems is best suited for global enterprises building complex, software-heavy AI products that require governance, scalability, and a large-team delivery organisation.
| Use case | Industries | Approach |
|---|---|---|
| Global AI transformation programme for Fortune 100 enterprise with multi-year delivery scope | Financial Services, Healthcare & Life Sciences | Python, TensorFlow |
| Enterprise GenAI platform with strict governance and compliance for regulated financial institution | Financial Services, Healthcare & Life Sciences | Python, TensorFlow |
| Complex ML product build requiring hundreds of engineers across multiple technical disciplines | Financial Services, Healthcare & Life Sciences | Python, TensorFlow |
| Agentic AI system for large enterprise with orchestration, memory, and multi-step automation | Financial Services, Healthcare & Life Sciences | Python, TensorFlow |
| AI strategy and architecture for multinational company entering ML at scale | Financial Services, Healthcare & Life Sciences | Python, TensorFlow |
EPAM Systems pricing
Short answer: EPAM Systems uses a dedicated team, t&m pricing approach. Minimum engagement starts at ~$200K+.
| Engagement model | Typical range | Best for |
|---|---|---|
| Dedicated team | Variable; depends on team size | Large programmes or team augmentation |
| Time & materials | 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 |
|---|---|
| +50,000+ professionals — unmatched delivery scale for global multi-stream AI programmes | -~$200K+ minimum makes EPAM inaccessible for all but the largest enterprise budgets |
| +AI-native engineering practice purpose-built for scaling ML, GenAI, and agentic systems | -Large-firm overhead — procurement, contracting, and ramp-up timelines are significantly longer than boutiques |
| +Strict governance and compliance frameworks for regulated enterprise AI delivery | -Generalist breadth means less niche ML depth than boutiques in specific domains like healthcare imaging or time-series |
| +Full-stack capability from hardware infrastructure through ML models to frontend AI products | |
| +Strong US and Eastern European delivery mix for cost-performance balance at enterprise scale |
EPAM Systems vs alternatives
How EPAM Systems compares to the other top Machine Learning Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Mid-market and enterprise teams needing specialist computer vision,... | Boutique ML depth combined with Anadea's 25-year enterprise delivery foundation — rare combination in the ML services market | 4.9 | Full comparison |
| LeewayHertz | Businesses that need generative AI or LLM integration... | Among the earliest boutique firms to build a structured GenAI delivery framework — deep LLM orchestration and RAG pipeline experience | 4.7 | Full comparison |
| Scopic | Companies that need genuinely custom ML architectures rather... | Engineers custom ML architectures from the ground up — not fine-tuned wrappers — with 20 years of production delivery discipline | 4.6 | Full comparison |
| InData Labs | Businesses with complex, highly specific ML problems requiring... | Boutique firm with a track record of solving atypical, high-complexity ML problems that generalist shops decline or under-deliver on | 4.6 | Full comparison |
| DATAFOREST | Mid-market companies that need a single vendor to... | Structured MLaaS delivery model — one team owns data engineering, model development, and post-deployment monitoring end-to-end | 4.5 | Full comparison |
| Forte Group | Regulated mid-market firms in financial services, insurance, or... | ML delivery built for regulated environments — model risk governance, audit trails, and compliance-aligned architecture are built in, not bolted on | 4.5 | Full comparison |
| RTS Labs | High-growth US companies that have done ML experiments... | Small by choice, senior by design — every project is staffed with senior practitioners accountable for post-launch performance, not just the plan | 4.5 | Full comparison |
| Quantiphi | Enterprises that need cloud-native ML at scale on... | AWS Premier and four-time Google Cloud Partner of the Year — the highest independently verified cloud ML credentials in the market | 4.4 | Full comparison |
| N-iX | European and US enterprises that need large dedicated... | Scale and depth in one package — 2,000+ engineers with a mature ML practice and competitive EU delivery rates | 4.4 | Full comparison |
| Miquido | Product companies that need ML or GenAI embedded... | GenAI and mobile ML integration in one team — a rare combination for companies building AI-native products for end users | 4.4 | Full comparison |
| Algoscale | Fortune 500 and growth-stage companies that need ML... | 100+ production ML deployments on AWS, Azure, and Snowflake — proven at enterprise scale with multiple cloud stacks | 4.3 | Full comparison |
| STX Next | Python-stack product companies that need ML tightly integrated... | Europe's largest Python shop — ML is embedded in full-stack Python systems with MLOps, not delivered as an isolated model | 4.3 | Full comparison |
| Intellias | Enterprises that need AWS-native ML with independently validated... | AWS AI Services Competency with verified production benchmarks — 10x TCO reduction in aerial imagery and sub-8-second NLP query latency | 4.3 | Full comparison |
| ScienceSoft | Healthcare and financial services organisations that need ML... | Over 35 years of regulated IT delivery — compliance-aligned ML architecture is a core competency, not an add-on | 4.2 | Full comparison |
| Simform | Enterprises that need cloud-native ML with IoT sensor... | AWS Premier Partner specialising in connecting physical IoT sensor data to cloud-based ML models for predictive maintenance | 4.2 | Full comparison |
| Oxagile | Enterprises in healthcare, media, or retail seeking cost-effective... | Strong connected-care and healthcare AI track record combined with 40–60% cost advantage versus US equivalents | 4.2 | Full comparison |
| Softeq | Companies building AI that must run on hardware... | Hardware-to-cloud ML engineering — a rare full-stack capability covering embedded device AI through cloud model serving | 4.1 | Full comparison |
| Aimprosoft | Small and mid-sized businesses that need AI consulting... | Full-cycle AI delivery from consulting through implementation, optimised for SMB budgets and timelines | 4.1 | Full comparison |
| Uvik Software | Teams with an existing ML codebase that need... | Senior-only ML engineer staffing — embedded in your stack, working in your tools, without agency overhead | 4.1 | Full comparison |
| Ciklum | Digital enterprises in FinTech, Retail, or Healthcare that... | 25+ AI products in production combined with 3,000+ global engineers — enterprise AI scale without the big-four overhead | 4.1 | Full comparison |
| Iflexion | Organisations new to ML that need AI strategy... | Consulting-first model ensures the ML problem is correctly defined before engineering investment begins | 4.0 | Full comparison |
| Itransition | European enterprises and US companies with EU operations... | EU regulatory compliance depth for ML — GDPR-aligned data architecture and EU AI Act readiness built into delivery | 4.0 | Full comparison |
| DataToBiz | Startups and growth-stage companies that need to take... | Product-oriented ML delivery — combines AI strategy with full-cycle engineering to produce launchable products, not just models | 4.0 | Full comparison |
| BairesDev | Enterprises and scale-ups that need large dedicated ML... | Latin American engineering delivery with US time-zone alignment — faster team ramp than Asian offshore with significant rate advantage versus US onshore | 4.0 | Full comparison |
| Andersen Lab | Enterprises needing large-scale ML delivery with named Fortune-500-level... | Named client references including Siemens, S&P Global, and Ryanair — enterprise ML track record at the highest scale | 4.0 | Full comparison |
| Intuz | Small and mid-size companies needing AI and ML... | 1,700+ delivered projects for SMBs — the broadest SMB ML delivery track record in this list | 3.9 | Full comparison |
| Tredence | Fortune 500 enterprises needing large-scale AI analytics, MLOps... | Large specialised analytics and AI firm — enterprise supply chain ML and CX analytics depth with Fortune 500 client delivery track record | 3.9 | Full comparison |
| Codiant | Budget-conscious organisations needing end-to-end ML delivery from discovery... | Cost-efficient end-to-end ML delivery covering all phases — discovery, build, integration, and optimisation — in a single engagement | 3.9 | Full comparison |
| GlobalLogic (Hitachi) | Global enterprises requiring MLOps at massive scale with... | Hitachi Group backing with 27,000 engineers — the scale and compliance posture of a major industrial conglomerate applied to enterprise ML | 3.9 | Full comparison |
| Cognizant | Fortune 500 enterprises running multi-year AI transformation programmes... | One of the world's largest AI & Analytics practices — Fortune 500 industry vertical depth and compliance credentials at 350,000-person delivery scale | 3.8 | Full comparison |
| Accenture | Global enterprises with strict governance requirements scaling GenAI,... | Accenture's global AI practice applies consulting strategy, industry domain expertise, and engineering delivery at 700,000-person scale — designed exclusively for enterprise | 3.8 | Full comparison |
| DataRobot | Enterprise data science teams that want a governed... | Platform-driven ML — DataRobot's AutoML engine and MLOps governance layer enable internal data science teams to build and manage models at scale without per-project custom development | 3.8 | Full comparison |
EPAM Systems FAQ
What is EPAM Systems?
EPAM Systems is a global software engineering and IT services company founded in 1993 and headquartered in Newtown, PA, with 50,000+ professionals. The firm offers AI-native engineering services with a focus on scaling ML, generative AI, and agentic systems across large enterprises with strict governance requirements. EPAM is a powerhouse for building complex, software-heavy AI products from scratch, though it comes at a premium price point.
How much does EPAM Systems charge?
EPAM Systems uses dedicated team, t&m pricing. Minimum engagement starts at ~$200K+. A discovery call is required to get project-specific quotes.
What tech stack does EPAM Systems use?
EPAM Systems works with Python, TensorFlow, PyTorch, AWS, Azure, GCP, Kubernetes, Apache Spark, Java, .NET. Primary industries served include Financial Services, Healthcare & Life Sciences, Manufacturing & Industrial, Media & Entertainment, Retail & E-commerce.
Is EPAM Systems right for enterprise?
Global enterprises building complex, software-heavy AI products that require governance, scalability, and a large-team delivery organisation. 50,000+ team size. Key consideration: ~$200K+ minimum makes EPAM inaccessible for all but the largest enterprise budgets.
What are the best EPAM Systems alternatives?
The best alternatives to EPAM Systems depend on your use case. Top options are:
- Tensorway: boutique ml depth combined with anadea's 25-year enterprise delivery foundation — rare combination in the ml services market
- LeewayHertz: among the earliest boutique firms to build a structured genai delivery framework — deep llm orchestration and rag pipeline experience
- Scopic: engineers custom ml architectures from the ground up — not fine-tuned wrappers — with 20 years of production delivery discipline
Compare EPAM Systems with other Machine Learning Development companies
EPAM Systems vs Tensorway EPAM Systems vs LeewayHertz EPAM Systems vs Scopic EPAM Systems vs InData Labs EPAM Systems vs DATAFOREST EPAM Systems vs Forte Group EPAM Systems vs RTS Labs EPAM Systems vs Quantiphi EPAM Systems vs N-iX EPAM Systems vs Miquido EPAM Systems vs Algoscale EPAM Systems vs STX Next EPAM Systems vs Intellias EPAM Systems vs ScienceSoft EPAM Systems vs Simform EPAM Systems vs Oxagile EPAM Systems vs Softeq EPAM Systems vs Aimprosoft EPAM Systems vs Uvik Software EPAM Systems vs Ciklum EPAM Systems vs Iflexion EPAM Systems vs Itransition EPAM Systems vs DataToBiz EPAM Systems vs BairesDev EPAM Systems vs Andersen Lab EPAM Systems vs Intuz EPAM Systems vs Tredence EPAM Systems vs Codiant EPAM Systems vs GlobalLogic (Hitachi) EPAM Systems vs Cognizant EPAM Systems vs Accenture EPAM Systems vs DataRobot
Last reviewed: July 2026. Verify all details directly with EPAM Systems before making a decision.