EPAM Systems vs InData Labs: full comparison for 2026
Quick verdict
EPAM Systems (4.6/5) edges ahead of InData Labs (4.1/5) overall. EPAM Systems is the better choice for large enterprises, regulated industries, multi-team AI programs. InData Labs is the stronger option for mid-sized companies adding data scientists to product teams. The right choice depends on your project size, budget, and required tech stack.
EPAM Systems vs InData Labs: head-to-head summary
| Criterion | EPAM Systems | InData Labs |
|---|---|---|
| Founded | 1993 | 2014 |
| HQ | Newtown, Pennsylvania, USA | Nicosia, Cyprus |
| Team size | 61,000+ | 50–249 |
| Rating | 4.6 / 5 | 4.1 / 5 |
| Primary differentiator | Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes | A data-science-only firm small enough that senior staff stay involved |
| Pricing model | Time and materials for augmented engineers; dedicated team and managed program contracts; rates on request | Time and materials; dedicated engineers; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, PyTorch, TensorFlow |
| Industries served | Financial services, Healthcare & life sciences, Retail & e-commerce, Software & SaaS, Travel, Manufacturing | Retail & e-commerce, Healthcare & life sciences, Financial services, Media |
EPAM Systems vs InData Labs: overview
EPAM Systems
EPAM Systems was founded in 1993 and is headquartered in Newtown, Pennsylvania, with roughly 61,000 employees across delivery centers in Europe, the Americas and Asia. It is a public company listed on the New York Stock Exchange. On its Q2 2026 earnings call, management said EPAM had more than 5,700 Anthropic-certified engineers and was among the five largest certified partners worldwide, with AI-native work making up about 11% of revenue. EPAM employs its engineers directly and sells them as augmented capacity, dedicated teams or managed programs, though most large accounts end up in the managed model.
InData Labs
InData Labs was founded in 2014 and is headquartered in Nicosia, Cyprus, with additional locations including Vilnius and Miami. Most directories put its headcount below 250 people. The company works only on data science and AI, covering predictive analytics, NLP, computer vision and generative AI, and it supplies engineers to client teams as well as delivering projects.
Services and capabilities: EPAM Systems vs InData Labs
| Capability | EPAM Systems | InData Labs |
|---|---|---|
| LLM / GenAI engineers | ✓ | ✗ |
| MLOps & deployment | ✓ | ✗ |
| Computer vision | ✗ | ✓ |
| Data engineering | ✓ | ✓ |
| AI agent development | ✓ | ✗ |
| Fractional / part-time experts | ✗ | ✗ |
| Risk-free trial period | ✗ | ✗ |
| Nearshore time-zone overlap | ✗ | ✗ |
Tech stack comparison: EPAM Systems vs InData Labs
| Framework / platform | EPAM Systems | InData Labs |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Databricks | ✓ | N/A |
| MLflow | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: EPAM Systems vs InData Labs
| Criterion | EPAM Systems | InData Labs |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Full-time dedicated engineers, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: EPAM Systems vs InData Labs
| Dimension | EPAM Systems | InData Labs |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Financial services, Healthcare & life sciences, Retail & e-commerce | Retail & e-commerce, Healthcare & life sciences, Financial services |
| Best use cases | Adding a 15-person GenAI squad to a bank's existing platform team, Rolling out agentic workflows across several business units under one master agreement | Adding a computer-vision engineer to a retail analytics team, Building churn and demand models with in-house analysts |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
EPAM Systems vs InData Labs: pros and cons
| EPAM Systems | |
|---|---|
| + | A Q2 2026 earnings call put its Anthropic-certified engineer count above 5,700, the largest verified GenAI bench on this list |
| + | Public-company reporting, audited financials and mature security reviews make vendor onboarding easier at banks and insurers |
| + | Can staff ten or more AI engineers in parallel across several time zones without running out of senior people |
| + | Deep data-platform practice means LLM work and the data engineering under it can come from one supplier |
| - | Rates are among the highest on this list and are only shared after scoping |
| - | Small requests for one or two engineers rarely get the same attention as large programs |
| - | Engagements tend to drift toward managed delivery, which moves decisions away from your own team |
| InData Labs | |
|---|---|
| + | Data science and AI are its only line of work |
| + | Experience across vision, language and predictive models |
| + | Clients deal with a small firm where senior staff stay close to the work |
| - | Headcount estimates vary widely, so confirm bench depth for your role |
| - | Limited capacity for large multi-team programs |
| - | Less visible LLM-agent work than some newer specialists |
Who should choose EPAM Systems?
A typical fit: adding a 15-person GenAI squad to a bank's existing platform team.
Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare & life sciences, Retail & e-commerce, Software & SaaS, Travel, Manufacturing.
Who should choose InData Labs?
A typical fit: adding a computer-vision engineer to a retail analytics team.
A data-science-only firm small enough that senior staff stay involved. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, Healthcare & life sciences, Financial services, Media.
Decision matrix: EPAM Systems vs InData Labs
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | EPAM Systems |
| You want the supplier to own delivery as well as staffing | Both; EPAM Systems rates higher overall |
| You need one expert part-time | Neither lists part-time experts; ask about reduced hours |
| You want to test an engineer before signing for months | Neither publishes a trial; ask for a short first term |
| Your budget is at the lower end | Compare: EPAM Systems (Not published) vs InData Labs (Not published) |
| You need overlap with U.S. working hours | Neither is nearshore; agree overlap hours up front |
| You need specialist depth in a specific vertical | EPAM Systems |
Use case fit: EPAM Systems vs InData Labs
| Use case | EPAM Systems fit | InData Labs fit | Winner |
|---|---|---|---|
| Adding a 15-person GenAI squad to a bank's existing platform team | Strong | Strong | Both equally |
| Rolling out agentic workflows across several business units under one master agreement | Strong | Limited | EPAM Systems |
| Adding a computer-vision engineer to a retail analytics team | Strong | Strong | Both equally |
| Building churn and demand models with in-house analysts | Limited | Strong | InData Labs |
Verdict: EPAM Systems vs InData Labs
EPAM Systems (4.6/5) is the stronger overall choice for most AI Staff Augmentation projects. Thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes.
InData Labs (4.1/5) is worth a look if you need building churn and demand models with in-house analysts. If your situation matches that, InData Labs is a competitive option.
Related comparisons
EPAM Systems vs InData Labs FAQ
Is EPAM Systems better than InData Labs?
EPAM Systems (4.6/5) scores higher overall, but "better" depends on your use case. EPAM Systems's strongest advantage: a Q2 2026 earnings call put its Anthropic-certified engineer count above 5,700, the largest verified GenAI bench on this list. InData Labs's strongest advantage: data science and AI are its only line of work.
How do EPAM Systems and InData Labs differ in pricing?
EPAM Systems uses time and materials for augmented engineers; dedicated team and managed program contracts; rates on request pricing. InData Labs uses time and materials; dedicated engineers; rates on request pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: EPAM Systems or InData Labs?
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 company before shortlisting.
What are the main differences between EPAM Systems and InData Labs?
EPAM Systems's primary differentiator is: thousands of certified GenAI engineers inside a publicly listed firm with enterprise security and procurement processes. InData Labs's primary differentiator is: a data-science-only firm small enough that senior staff stay involved. They also differ in team size (61,000+ vs 50–249), minimum engagement (Not published vs Not published), and primary industries served (Financial services, Healthcare & life sciences vs Retail & e-commerce, Healthcare & life sciences).
Verify all details directly with each company before making a decision.