InData Labs vs ScienceSoft: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.5/5) edges ahead of ScienceSoft (3.9/5) 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.. ScienceSoft is the stronger option for companies wanting AI/ML delivered by a long-established generalist IT consultancy already handling other IT needs.. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs ScienceSoft: head-to-head summary
| Criterion | InData Labs | ScienceSoft |
|---|---|---|
| Founded | 2014 | 1989 |
| HQ | Nicosia, Cyprus | McKinney, Texas, USA |
| Team size | 51–200 | 501–1,000 |
| Rating | 4.5 / 5 | 3.9 / 5 |
| Best for | Fintech, healthcare, and SaaS companies wanting a specialist data-science boutique rather than a generalist software vendor. | Companies wanting AI/ML delivered by a long-established generalist IT consultancy already handling other IT needs. |
| Pricing model | Fixed project and Time & Material | Fixed project and Time & Material |
| Min. engagement | $20K | Not published |
| Primary tech stack | Python, Scikit-learn, TensorFlow | Python, TensorFlow, AWS |
| Industries served | FinTech, Healthcare, Technology/SaaS, Retail, Logistics | Healthcare, Retail, Financial Services, Manufacturing |
InData Labs vs ScienceSoft: 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.
ScienceSoft
ScienceSoft is an IT consulting and software development company founded in 1989, headquartered in McKinney, Texas, with additional offices in Europe, the UAE, and Vietnam. The firm reports more than 750 IT professionals and over 3,600 delivered projects across its 36-year history, with AI/ML positioned as one core service area among IT strategy consulting, cloud, cybersecurity, and quality assurance.
Services and capabilities: InData Labs vs ScienceSoft
| Capability | InData Labs | ScienceSoft |
|---|---|---|
| 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 ScienceSoft
| Framework / platform | InData Labs | ScienceSoft |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | N/A |
| Kubernetes | N/A | N/A |
| Databricks | N/A | N/A |
| LangChain | N/A | N/A |
Pricing comparison: InData Labs vs ScienceSoft
| Criterion | InData Labs | ScienceSoft |
|---|---|---|
| Minimum engagement | $20K | Not published |
| Engagement models | Fixed project, Time & Material | Fixed project, Time & Material |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Enterprise / not published |
Target audience comparison: InData Labs vs ScienceSoft
| Dimension | InData Labs | ScienceSoft |
|---|---|---|
| Best company size | Startup to mid-market | Mid-market to 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 | Companies wanting AI/ML bundled with existing cloud, QA, or cybersecurity work from a single long-established vendor, Healthcare or manufacturing clients needing broad IT consulting plus a specific ML/AI component |
| Typical project type | Fixed project | Fixed project |
InData Labs vs ScienceSoft: 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 |
| ScienceSoft | |
|---|---|
| + | 36 years of continuous operation and 3,600+ delivered projects (per company website) among the longest track records reviewed here |
| + | Over half of staff cited as senior-level specialists (per company website) |
| + | Broad IT service catalog means AI/ML can be bundled with cloud, security, or QA from the same vendor |
| + | Multi-region office presence (Europe, UAE, Vietnam) beyond the US HQ |
| - | AI/ML is one of several core services (alongside cloud, cybersecurity, QA) rather than the firm's defining specialty |
| - | Less AI-first branding or ML-specific certification profile than boutique AI consultancies on this list |
| - | Minimum engagement size 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 ScienceSoft?
ScienceSoft is the right choice for companies wanting AI/ML delivered by a long-established generalist IT consultancy already handling other IT needs..
36 years of continuous IT consulting history, one of the longest track records among firms on this list.. Minimum engagement starts at Not published. Works best with clients in Healthcare, Retail, Financial Services, Manufacturing.
Decision matrix: InData Labs vs ScienceSoft
| 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 | Check each company's engagement model |
| Your budget is at the lower end | Compare: InData Labs ($20K) vs ScienceSoft (Not published) |
| You need specialist depth in a specific vertical | InData Labs |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | InData Labs |
Use case fit: InData Labs vs ScienceSoft
| Use case | InData Labs fit | ScienceSoft 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 |
| Companies wanting AI/ML bundled with existing cloud, QA, or cybersecurity work from a single long-established vendor | Limited | Strong | ScienceSoft |
| Healthcare or manufacturing clients needing broad IT consulting plus a specific ML/AI component | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: InData Labs vs ScienceSoft
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..
ScienceSoft (3.9/5) is the better choice when companies wanting AI/ML delivered by a long-established generalist IT consultancy already handling other IT needs.. If your situation matches those criteria, ScienceSoft is a competitive option.
Related comparisons
InData Labs vs ScienceSoft FAQ
Is InData Labs better than ScienceSoft?
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.. ScienceSoft is better for companies wanting AI/ML delivered by a long-established generalist IT consultancy already handling other IT needs..
How do InData Labs and ScienceSoft differ in pricing?
InData Labs uses fixed project and time & material pricing with a minimum engagement of $20K. ScienceSoft uses fixed project and time & material 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 ScienceSoft?
ScienceSoft 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 ScienceSoft?
InData Labs's primary differentiator is: dedicated in-house r&d center focused specifically on data science and ai rather than broad software outsourcing.. ScienceSoft's primary differentiator is: 36 years of continuous it consulting history, one of the longest track records among firms on this list.. They also differ in team size (51–200 vs 501–1,000), 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.