EPAM Systems
Global engineering firm with the bench to run AI programs across many products
What is 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.
EPAM Systems works primarily with clients in Financial services, Healthcare, Retail, Media, Manufacturing sectors. Its primary differentiator is: Engineering capacity to put AI into dozens of products under one program.
EPAM Systems tech stack and services
| Service area |
|---|
| Pilot to Production |
| MLOps & Model Serving |
| Data Engineering |
| Agentic AI |
| AWS Partner |
| Microsoft Partner |
EPAM Systems pricing
Short answer: EPAM Systems prices its work as follows: time & materials and dedicated teams; rates not published. Minimum engagement is not publicly disclosed; a discovery call is required.
| Engagement model | Typical range | Best for |
|---|---|---|
| Time & materials | Hourly; see pricing model above | Scope that will change as you learn |
| Dedicated team | Monthly team rate; not public | Continuing the build after a pilot |
| Managed services | Monthly fee; not public | Running and monitoring live systems |
EPAM Systems pros and cons
| Advantages | Things to consider |
|---|---|
| +Very large engineering bench | -Recent acquisitions (NEORIS, First Derivative) are still being absorbed |
| +Strong software delivery practice to build on | -Too large and slow for one small pilot |
| +Financial-services depth added through First Derivative | -Rates not published |
| +Can run systems after launch |
EPAM Systems vs alternatives
How EPAM Systems compares to the other top AI Implementation providers.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Teams whose AI pilot works but can't ship. | Model gateway with routing, provider fallback, and regression tests, released through a canary rollout | 4.8 | Full comparison |
| Provectus | AWS shops taking GenAI pilots to production. | AWS Premier Tier MLOps and generative AI engineering on Bedrock and SageMaker | 4.6 | Full comparison |
| Grid Dynamics | Enterprises moving several AI pilots to production. | Forward-deployed engineers working from ready-made GAIN platforms for agentic and retail AI | 4.5 | Full comparison |
| deepsense.ai | Teams that need measured accuracy before launch. | Evaluation-first engineering that proves accuracy on your data before release | 4.5 | Full comparison |
| Neurons Lab | Banks and insurers shipping AI on AWS. | Financial-services AI built inside existing compliance controls on AWS | 4.4 | Full comparison |
| Datatonic | Google Cloud users needing MLOps and managed run. | Vertex AI MLOps with a managed service to run models after launch | 4.4 | Full comparison |
| Quantiphi | Large document and contact-center AI programs. | Large Google Cloud and AWS engineering bench for document and speech AI in production | 4.3 | Full comparison |
| ML6 | European firms needing EU-compliant production AI. | European AI engineering with EU AI Act and GDPR requirements built into delivery | 4.3 | Full comparison |
| Thoughtworks | Engineering orgs wanting disciplined AI delivery. | Continuous delivery practice applied to models and prompts, with tool views published in its Technology Radar | 4.3 | Full comparison |
| N-iX | Mid-size and large firms wanting nearshore MLOps. | Dedicated MLOps practice backed by a large nearshore engineering bench | 4.2 | Full comparison |
| DS Stream | Teams wanting packaged LLMOps at nearshore rates. | Packaged LLMOps setup with evaluation, prompt versioning, guardrails, and model routing | 4.2 | Full comparison |
| Addepto | Mid-size firms wanting tested LLM apps cheaply. | Open-source LLM testing (ContextCheck) and a low $10,000+ Clutch entry point | 4.2 | Full comparison |
| RTS Labs | US mid-market firms on a small budget. | The lowest entry point on this page, with a committed ship date for production code | 4.1 | Full comparison |
| Accenture | Multinationals rolling AI out across many countries. | Staffing and compliance depth to run production AI in many countries at once | 4.1 | Full comparison |
| Winder.ai | Teams needing senior MLOps advice and review. | Senior MLOps expertise from a recognized author, at boutique scale | 4.0 | Full comparison |
| NineTwoThree AI Studio | Funded companies launching an AI product. | Product design and engineering together for customer-facing AI launches | 4.0 | Full comparison |
| Globant | US firms wanting nearshore AI engineering. | Nearshore Latin American engineering teams in US time zones | 4.0 | Full comparison |
| SoftServe | Existing clients extending engineering into AI. | Google Cloud Premier engineering firm with an MLOps and generative AI practice | 4.0 | Full comparison |
| Capgemini | European multinationals pairing AI with outsourcing. | Production AI bundled with outsourced business operations after the WNS deal | 3.9 | Full comparison |
| InData Labs | Mid-size firms with one focused AI project. | Discovery, proof of concept, and production build from a small data science team | 3.9 | Full comparison |
| Instinctools | Palantir users building agentic workflows. | Palantir Foundry and AIP implementation alongside custom software engineering | 3.9 | Full comparison |
| Netguru | Product teams adding AI features to apps. | AI features shipped as part of web and mobile product work | 3.9 | Full comparison |
| STX Next | Python teams needing extra AI engineers. | Python engineering depth for AI that lives inside your codebase | 3.9 | Full comparison |
| 10Clouds | Smaller banks and lenders adding AI. | A dedicated financial-institutions AI unit with its own AIConsole platform | 3.8 | Full comparison |
| LeewayHertz | Buyers wanting a platform-based AI build. | Builds on its own ZBrain platform, now backed by The Hackett Group | 3.7 | Full comparison |
EPAM Systems FAQ
What is EPAM Systems?
Global engineering firm with the bench to run AI programs across many products
How much does EPAM Systems charge?
Pricing model: time & materials and dedicated teams; rates not published. Minimum engagement is not publicly disclosed. A discovery call is required to get project-specific quotes.
What tech stack does EPAM Systems use?
EPAM Systems works with AWS, Azure, Google Cloud, Kubernetes, Databricks, Snowflake. Primary industries served include Financial services, Healthcare, Retail, Media, Manufacturing.
Is EPAM Systems right for enterprise?
Best for: enterprises adding AI across many products. Team size: 61,000+. Key consideration: Recent acquisitions (NEORIS, First Derivative) are still being absorbed.
What are the best EPAM Systems alternatives?
The best alternatives to EPAM Systems depend on your use case. Top options are:
- Tensorway: model gateway with routing, provider fallback, and regression tests, released through a canary rollout
- Provectus: AWS Premier Tier MLOps and generative AI engineering on Bedrock and SageMaker
- Grid Dynamics: forward-deployed engineers working from ready-made GAIN platforms for agentic and retail AI
Compare EPAM Systems with other AI Implementation providers
Verify all details directly with EPAM Systems before making a decision.