EPAM Systems vs InData Labs: full comparison for 2026
Quick verdict
EPAM Systems (4.1/5) edges ahead of InData Labs (3.9/5) overall. EPAM Systems is the better choice for enterprises adding AI across many products. InData Labs is the stronger option for mid-size firms with one focused AI project. The right choice depends on how far your pilot has got, your budget, and who will run the system after launch.
EPAM Systems vs InData Labs: head-to-head summary
| Criterion | EPAM Systems | InData Labs |
|---|---|---|
| Founded | 1993 | 2014 |
| HQ | Newtown, USA | Nicosia, Cyprus |
| Team size | 61,000+ | 80+ |
| Rating | 4.1 / 5 | 3.9 / 5 |
| Primary differentiator | Engineering capacity to put AI into dozens of products under one program | Discovery, proof of concept, and production build from a small data science team |
| Pricing model | Time & materials and dedicated teams; rates not published | Project-based; rates on request |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | AWS, Azure, Google Cloud | AWS, Azure, Python |
| Industries served | Financial services, Healthcare, Retail, Media, Manufacturing | Retail, Healthcare, Fintech, Logistics, Media |
EPAM Systems vs InData Labs: overview
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.
InData Labs
InData Labs was founded in 2014, is headquartered in Nicosia, Cyprus, and has around 80 engineers and data scientists. It builds machine learning, computer vision, and language model systems for mid-size companies, usually starting with a discovery phase and a proof of concept. Its size suits one focused production project better than a long program.
Services and capabilities: EPAM Systems vs InData Labs
| Capability | EPAM Systems | InData Labs |
|---|---|---|
| LLM gateway & model routing | ✗ | ✗ |
| Regression tests on prompt and model changes | ✗ | ✗ |
| Canary or staged rollout | ✗ | ✗ |
| Hallucination and drift monitoring | ✗ | ✗ |
| MLOps & model serving | ✓ | ✓ |
| Fixed-scope pilot | ✗ | ✓ |
| Managed run after launch | ✓ | ✗ |
Tech stack comparison: EPAM Systems vs InData Labs
| Framework / platform | EPAM Systems | InData Labs |
|---|---|---|
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | N/A |
| Kubernetes | ✓ | N/A |
| MLflow | N/A | N/A |
| Databricks | ✓ | N/A |
| Amazon Bedrock | N/A | N/A |
| Vertex AI | N/A | N/A |
| LangChain | N/A | N/A |
| Snowflake | ✓ | N/A |
Pricing comparison: EPAM Systems vs InData Labs
| Criterion | EPAM Systems | InData Labs |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Time & materials, Dedicated team, Managed services | Fixed-scope pilot, Fixed-scope project, Dedicated team |
| 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 | Enterprise | Startup to mid-market |
| Best industries | Financial services, Healthcare, Retail | Retail, Healthcare, Fintech |
| Best use cases | AI across a portfolio of enterprise products., Production AI in banking platforms. | A single computer vision system taken to production., Proof of concept followed by a production build. |
| Typical project type | Time & materials | Fixed-scope pilot |
EPAM Systems vs InData Labs: pros and cons
| EPAM Systems | |
|---|---|
| + | Very large engineering bench |
| + | Strong software delivery practice to build on |
| + | Financial-services depth added through First Derivative |
| + | Can run systems after launch |
| - | Recent acquisitions (NEORIS, First Derivative) are still being absorbed |
| - | Too large and slow for one small pilot |
| - | Rates not published |
| InData Labs | |
|---|---|
| + | Clear path from proof of concept to production |
| + | Senior data scientists on small projects |
| + | Computer vision and language model experience |
| - | Small team limits large or parallel programs |
| - | No named LLMOps tooling |
| - | No managed-run service listed |
Who should choose EPAM Systems?
A typical fit: AI across a portfolio of enterprise products.
Engineering capacity to put AI into dozens of products under one program. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail, Media, Manufacturing.
Who should choose InData Labs?
A typical fit: a single computer vision system taken to production.
Discovery, proof of concept, and production build from a small data science team. Minimum engagement is not publicly disclosed. Works best with clients in Retail, Healthcare, Fintech, Logistics, Media.
Decision matrix: EPAM Systems vs InData Labs
| Your situation | Recommended choice |
|---|---|
| Your pilot works but nobody trusts it in production yet | Ask each for its release and rollback process |
| Model API spend is growing faster than usage | Neither lists a gateway; ask how cost per feature is tracked |
| You need someone to watch quality after launch | EPAM Systems |
| You run classic ML models that need serving and retraining | Both cover MLOps |
| You want a priced pilot before a full rollout | InData Labs |
| Your budget is at the lower end | Compare: EPAM Systems (Not disclosed) vs InData Labs (Not disclosed) |
| You need a large team for many products at once | EPAM Systems |
Use case fit: EPAM Systems vs InData Labs
| Use case | EPAM Systems fit | InData Labs fit | Winner |
|---|---|---|---|
| AI across a portfolio of enterprise products. | Strong | Limited | EPAM Systems |
| Production AI in banking platforms. | Strong | Limited | EPAM Systems |
| A single computer vision system taken to production. | Limited | Strong | InData Labs |
| Proof of concept followed by a production build. | Limited | Strong | InData Labs |
Verdict: EPAM Systems vs InData Labs
EPAM Systems (4.1/5) is the stronger overall choice for most AI Implementation projects. Engineering capacity to put AI into dozens of products under one program.
InData Labs (3.9/5) is worth a look if you need proof of concept followed by a production build. 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 (4.1/5) scores higher overall, but "better" depends on your use case. EPAM Systems's strongest advantage: very large engineering bench. InData Labs's strongest advantage: clear path from proof of concept to production.
How do EPAM Systems and InData Labs differ in pricing?
EPAM Systems's pricing: time & materials and dedicated teams; rates not published. InData Labs's pricing: project-based; rates on request. Any hourly bands shown come from Clutch, not a published rate card, so a scoping call is still needed for a project quote.
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 provider before shortlisting.
What are the main differences between EPAM Systems and InData Labs?
EPAM Systems's primary differentiator is: engineering capacity to put AI into dozens of products under one program. InData Labs's primary differentiator is: discovery, proof of concept, and production build from a small data science team. They also differ in team size (61,000+ vs 80+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Financial services, Healthcare vs Retail, Healthcare).
Verify all details directly with each provider before making a decision.