InData Labs vs SoftServe: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.5/5) edges ahead of SoftServe (4.0/5) overall. InData Labs is the better choice for fintech, healthcare, and SaaS companies wanting a specialist data-science boutique rather than a generalist software vendor.. SoftServe is the stronger option for enterprises wanting a large, established engineering partner with a long-running AI/ML and data practice alongside cloud and IoT work.. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs SoftServe: head-to-head summary
| Criterion | InData Labs | SoftServe |
|---|---|---|
| Founded | 2014 | 1993 |
| HQ | Nicosia, Cyprus | Austin, Texas, USA / Lviv, Ukraine |
| Team size | 51–200 | 10,000+ |
| Rating | 4.5 / 5 | 4.0 / 5 |
| Best for | Fintech, healthcare, and SaaS companies wanting a specialist data-science boutique rather than a generalist software vendor. | Enterprises wanting a large, established engineering partner with a long-running AI/ML and data practice alongside cloud and IoT work. |
| Pricing model | Fixed project and Time & Material | Fixed project, dedicated team, staff augmentation |
| Min. engagement | $20K | Not published |
| Primary tech stack | Python, Scikit-learn, TensorFlow | Python, TensorFlow, Azure |
| Industries served | FinTech, Healthcare, Technology/SaaS, Retail, Logistics | Healthcare, Retail, Financial Services, Technology/SaaS |
InData Labs vs SoftServe: overview
InData Labs
InData Labs is a data science and AI consultancy founded in 2014 by Marat Karpeko, headquartered in Nicosia, Cyprus, with additional offices in Lithuania and the US. The 80+ person firm (per company website) runs its own R&D center and focuses on production AI systems for fintech, healthcare, SaaS, retail, and logistics clients.
SoftServe
SoftServe is a digital engineering and consulting company founded in 1993 in Lviv, Ukraine, with US headquarters in Austin, Texas and European headquarters remaining in Lviv. Reported headcount ranges from roughly 10,000 to 12,000 employees across 58 offices in 14 countries, with AI/ML, data and analytics, and cloud among its core practice areas.
Services and capabilities: InData Labs vs SoftServe
| Capability | InData Labs | SoftServe |
|---|---|---|
| Custom ML model development | ✓ | ✓ |
| Deep learning & computer vision | ✗ | ✗ |
| NLP & LLM / Generative AI | ✗ | ✗ |
| MLOps & production deployment | ✗ | ✓ |
| Data engineering | ✓ | ✓ |
| AI strategy consulting | ✓ | ✗ |
| Staff augmentation | ✗ | ✓ |
Tech stack comparison: InData Labs vs SoftServe
| Framework / platform | InData Labs | SoftServe |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | N/A |
| Kubernetes | N/A | ✓ |
| Databricks | N/A | N/A |
| LangChain | N/A | N/A |
Pricing comparison: InData Labs vs SoftServe
| Criterion | InData Labs | SoftServe |
|---|---|---|
| Minimum engagement | $20K | Not published |
| Engagement models | Fixed project, Time & Material | Fixed project, Dedicated team, Staff augmentation |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Enterprise / not published |
Target audience comparison: InData Labs vs SoftServe
| Dimension | InData Labs | SoftServe |
|---|---|---|
| Best company size | Startup to mid-market | Enterprise |
| Best industries | FinTech, Healthcare, Technology/SaaS | Healthcare, Retail, Financial Services |
| Best use cases | Building a fintech risk-scoring or fraud model with a specialist data-science team, Standing up a healthcare predictive-analytics pilot with a boutique partner | Enterprise clients needing AI/ML delivered as part of a broader digital engineering program, Healthcare or retail programs combining cloud migration with applied ML |
| Typical project type | Fixed project | Fixed project |
InData Labs vs SoftServe: pros and cons
| InData Labs | |
|---|---|
| + | Founder brought data-analytics experience from the gaming industry, an unusually data-intensive prior domain |
| + | Multi-country footprint (Cyprus, Lithuania, US) without the very large headcount of enterprise IT firms |
| + | 10+ years of focused data science practice rather than a recent AI pivot from generalist dev work |
| + | Named vertical focus (FinTech, Healthcare, Logistics) supports domain-specific model design |
| - | 80-person team limits capacity for very large multi-year enterprise programs |
| - | Less brand recognition in North America than US-headquartered competitors |
| - | Public case studies rarely disclose named enterprise clients |
| SoftServe | |
|---|---|
| + | 32 years of operating history, among the longest on this list |
| + | 10,000+ employees across 58 offices supports very large, globally distributed programs |
| + | AI/ML practice sits alongside mature cloud, data, and IoT capabilities from the same firm |
| + | Dual US/Ukraine headquarters structure has proven resilient through a long operating history |
| - | AI/ML is one of several major practice areas rather than the company's sole focus |
| - | Very large scale may mean less senior-level access on smaller engagements than boutique specialists |
| - | Minimum engagement size and standard pricing not publicly disclosed |
Who should choose InData Labs?
InData Labs is the right choice for fintech, healthcare, and SaaS companies wanting a specialist data-science boutique rather than a generalist software vendor..
Dedicated in-house R&D center focused specifically on data science and AI rather than broad software outsourcing.. Minimum engagement starts at $20K. Works best with clients in FinTech, Healthcare, Technology/SaaS, Retail, Logistics.
Who should choose SoftServe?
SoftServe is the right choice for enterprises wanting a large, established engineering partner with a long-running AI/ML and data practice alongside cloud and IoT work..
32 years of continuous operation spanning both a US public-market presence and deep Ukrainian engineering roots.. Minimum engagement starts at Not published. Works best with clients in Healthcare, Retail, Financial Services, Technology/SaaS.
Decision matrix: InData Labs vs SoftServe
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | InData Labs |
| You need a large dedicated team for an ongoing programme | SoftServe |
| Your budget is at the lower end | Compare: InData Labs ($20K) vs SoftServe (Not published) |
| You need specialist depth in a specific vertical | InData Labs |
| You need staff augmentation or team extension | SoftServe |
| You need consulting before committing to a build | InData Labs |
Use case fit: InData Labs vs SoftServe
| Use case | InData Labs fit | SoftServe fit | Winner |
|---|---|---|---|
| Building a fintech risk-scoring or fraud model with a specialist data-science team | Strong | Limited | InData Labs |
| Standing up a healthcare predictive-analytics pilot with a boutique partner | Strong | Limited | InData Labs |
| Enterprise clients needing AI/ML delivered as part of a broader digital engineering program | Limited | Strong | SoftServe |
| Healthcare or retail programs combining cloud migration with applied ML | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Strong | SoftServe |
Verdict: InData Labs vs SoftServe
InData Labs (4.5/5) is the stronger overall choice for most Machine Learning Development projects. Dedicated in-house R&D center focused specifically on data science and AI rather than broad software outsourcing.. It is best for fintech, healthcare, and SaaS companies wanting a specialist data-science boutique rather than a generalist software vendor..
SoftServe (4.0/5) is the better choice when enterprises wanting a large, established engineering partner with a long-running AI/ML and data practice alongside cloud and IoT work.. If your situation matches those criteria, SoftServe is a competitive option.
Related comparisons
InData Labs vs SoftServe FAQ
Is InData Labs better than SoftServe?
InData Labs (4.5/5) scores higher overall, but "better" depends on your use case. InData Labs is better for fintech, healthcare, and SaaS companies wanting a specialist data-science boutique rather than a generalist software vendor.. SoftServe is better for enterprises wanting a large, established engineering partner with a long-running AI/ML and data practice alongside cloud and IoT work..
How do InData Labs and SoftServe differ in pricing?
InData Labs uses fixed project and time & material pricing with a minimum engagement of $20K. SoftServe uses fixed project, dedicated team, staff augmentation pricing with a minimum engagement of Not published. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: InData Labs or SoftServe?
InData Labs is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each agency before shortlisting.
What are the main differences between InData Labs and SoftServe?
InData Labs's primary differentiator is: dedicated in-house r&d center focused specifically on data science and ai rather than broad software outsourcing.. SoftServe's primary differentiator is: 32 years of continuous operation spanning both a us public-market presence and deep ukrainian engineering roots.. They also differ in team size (51–200 vs 10,000+), minimum engagement ($20K vs Not published), and primary industries served (FinTech, Healthcare vs Healthcare, Retail).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.