EPAM Systems
Global software engineering leader with AI-native engineering and a 50,000-strong delivery organisation
Founded 1993 | Newtown, PA | 50,000+ employees
custom-mlgenerative-aimlopsdata-engineeringai-strategystaff-aug
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 |
|---|
| Custom ML Development |
| Generative AI |
| MLOps & Deployment |
| Data Engineering |
| AI Strategy |
| Staff Augmentation |
EPAM Systems use cases
Short answer: EPAM Systems is best suited for global enterprises, complex AI products, governance at scale.
| Use case |
|---|
| Global AI transformation programme for Fortune 100 enterprise with multi-year delivery scope |
| Enterprise GenAI platform with strict governance and compliance for regulated financial institution |
| Complex ML product build requiring hundreds of engineers across multiple technical disciplines |
| Agentic AI system for large enterprise with orchestration, memory, and multi-step automation |
| AI strategy and architecture for multinational company entering ML at scale |
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 teams, specialist CV/time-series/LLM delivery. | Boutique ML depth, with access to the 25-year enterprise delivery experience of its parent company — a rare combination in the ML services market | 4.9 | Full comparison |
| LeewayHertz | Businesses, GenAI/LLM integration plus custom ML. | Among the earliest boutique firms to build a structured GenAI delivery framework — deep LLM orchestration and RAG pipeline experience | 4.1 | Full comparison |
| Scopic | Companies needing genuinely custom ML architectures. | 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 | Complex ML problems, deep data-science expertise. | 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, full ML pipeline ownership. | 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 finance/insurance firms, audit-ready ML governance. | 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, production-accountable ML partner. | 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, cloud-native ML, top-tier AWS/GCP credentials. | 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 | EU/US enterprises, large dedicated teams, competitive rates. | 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, ML/GenAI embedded, fast time-to-demo. | 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, cloud data lakehouse... | 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, tightly integrated ML plus MLOps. | 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, AWS-native ML, validated CV/NLP/RAG results. | 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/finance orgs, HIPAA/PCI-DSS/SOC2-compliant 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 | Manufacturing/logistics enterprises, cloud-native ML plus IoT. | AWS Premier Partner specialising in connecting physical IoT sensor data to cloud-based ML models for predictive maintenance | 4.2 | Full comparison |
| Oxagile | Healthcare/media/retail enterprises, cost-effective Eastern European ML. | 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 for edge and embedded hardware. | Hardware-to-cloud ML engineering — a rare full-stack capability covering embedded device AI through cloud model serving | 4.1 | Full comparison |
| Aimprosoft | SMBs, AI consulting and custom ML at accessible... | 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, embedded senior... | Senior-only ML engineer staffing — embedded in your stack, working in your tools, without agency overhead | 4.1 | Full comparison |
| Ciklum | FinTech, Retail, Healthcare enterprises — AI product engineering... | 25+ AI products in production combined with 3,000+ global engineers — enterprise AI scale without the big-four overhead | 4.1 | Full comparison |
| Iflexion | ML newcomers, AI strategy and scoping first. | Consulting-first model ensures the ML problem is correctly defined before engineering investment begins | 4.0 | Full comparison |
| Itransition | EU-operating enterprises, GDPR/EU AI Act-compliant ML. | EU regulatory compliance depth for ML — GDPR-aligned data architecture and EU AI Act readiness built into delivery | 4.0 | Full comparison |
| DataToBiz | Startups taking an ML idea to market-ready delivery. | 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, fast teams, US time-zone aligned. | 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, large-scale ML, Fortune-500 references, EU footprint. | Named client references including Siemens, S&P Global, and Ryanair — enterprise ML track record at the highest scale | 4.0 | Full comparison |
| Intuz | SMBs, US-headquartered AI/ML at accessible rates. | 1,700+ delivered projects for SMBs — the broadest SMB ML delivery track record in this list | 3.9 | Full comparison |
| Tredence | Fortune 500 enterprises, AI analytics and supply-chain ML. | 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 orgs, end-to-end ML delivery. | 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, massive-scale MLOps, Hitachi-backed. | 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, 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, governed GenAI and agentic AI at... | 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, governed AutoML platform. | 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, complex AI products, governance at scale. 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, with access to the 25-year enterprise delivery experience of its parent company — a 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