InData Labs vs ScienceSoft: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.5/5) edges ahead of ScienceSoft (3.8/5) overall. InData Labs is the better choice for finTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead.. ScienceSoft is the stronger option for enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record.. 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 | Limassol, Cyprus | McKinney, Texas, United States |
| Team size | 50–100 | 750+ |
| Rating | 4.5 / 5 | 3.8 / 5 |
| Best for | FinTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead. | Enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record. |
| Pricing model | Project-based, dedicated team | Time & materials, managed engagement |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, TensorFlow, PyTorch | AWS, Azure ML, Google Cloud |
| Industries served | FinTech, Healthcare, Retail & E-commerce, Logistics & Supply Chain | Healthcare, Insurance, Manufacturing, Retail & E-commerce, Telecom |
InData Labs vs ScienceSoft: overview
InData Labs
InData Labs was founded in 2014 by Marat Karpeko and is headquartered in Limassol, Cyprus, with additional offices in Lithuania and the United States. The company has stayed a pure-play AI/data-science consultancy for over a decade, building production ML systems for fintech, healthcare, SaaS, retail, and logistics clients, and is listed in Clutch's Top 10 AI Software Companies leaders matrix. At roughly 80 professionals, it is one of the smaller specialist firms in this list, trading scale for narrower focus.
ScienceSoft
ScienceSoft was founded in 1989 and is headquartered in McKinney, Texas, bringing together more than 750 engineers and consultants with a track record of over 4,200 successful projects for 1,400+ clients across healthcare, insurance, investment, manufacturing, retail, and telecom. Its AI practice includes AI engineers, generative AI consultants, and MLOps experts working with both open-source frameworks and cloud-native AI services, and Clutch has named ScienceSoft a 2018 Global IT Leader among its Clutch 1000 companies. At 35+ years old, it is one of the longest-established firms in this list, with AI as a newer addition to a much older core business.
Services and capabilities: InData Labs vs ScienceSoft
| Capability | InData Labs | ScienceSoft |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✓ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✗ | ✓ |
Tech stack comparison: InData Labs vs ScienceSoft
| Framework / platform | InData Labs | ScienceSoft |
|---|---|---|
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | N/A | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: InData Labs vs ScienceSoft
| Criterion | InData Labs | ScienceSoft |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Project-based, Dedicated team | Managed engagement, Time & materials, Staff augmentation |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: InData Labs vs ScienceSoft
| Dimension | InData Labs | ScienceSoft |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | FinTech, Healthcare, Retail & E-commerce | Healthcare, Insurance, Manufacturing |
| Best use cases | FinTech company needs predictive analytics built by a team that has done nothing but AI/data science since 2014., Healthcare startup needs a computer vision model with a small, senior delivery team. | Enterprise wants AI/MLOps delivery from a vendor with 35+ years of institutional stability., Insurance or healthcare company needs a vendor with a very large (4,200+) project delivery track record. |
| Typical project type | Project-based | Managed engagement |
InData Labs vs ScienceSoft: pros and cons
| InData Labs | |
|---|---|
| + | Has operated as a dedicated AI/data science firm since 2014 with no pivot to general software outsourcing. |
| + | Ranked in Clutch's Top 10 AI Software Companies leaders matrix. |
| + | Covers the full pipeline from data engineering through generative AI and computer vision, avoiding narrow single-service lock-in. |
| + | Smaller team size (~80) generally means less account-management overhead between client and engineers. |
| - | At roughly 80 people, InData Labs cannot staff large multi-workstream enterprise programs the way a 2,000+ person firm can. |
| - | Limassol, Cyprus HQ has a thinner regional case-study base in North America compared to US-headquartered peers. |
| ScienceSoft | |
|---|---|
| + | 35+ years of operating history (since 1989) is among the longest track records of any firm in this list. |
| + | 4,200+ successful projects for 1,400+ clients provides an extensive delivery pattern library across industries. |
| + | 2018 Global IT Leader recognition from Clutch, part of the Clutch 1000, is an independently sourced distinction. |
| + | 750+ engineers and consultants with dedicated MLOps and generative AI consulting roles, not just generalist developers relabeled. |
| - | AI is a comparatively newer addition to a company whose core 35-year identity is broader IT consulting. |
| - | 750-person total headcount spans many practice areas, so AI-specific bench depth is smaller than the total suggests. |
Who should choose InData Labs?
InData Labs is the right choice for finTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead..
Ten-plus years as a pure-play AI/data-science firm with no general software-development sideline.. Minimum engagement starts at Not published. Works best with clients in FinTech, Healthcare, Retail & E-commerce, Logistics & Supply Chain.
Who should choose ScienceSoft?
ScienceSoft is the right choice for enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record..
35+ years of continuous operating history (since 1989), among the longest-established vendors in this list.. Minimum engagement starts at Not published. Works best with clients in Healthcare, Insurance, Manufacturing, Retail & E-commerce, Telecom.
Decision matrix: InData Labs vs ScienceSoft
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| You need a large dedicated team for an ongoing programme | InData Labs |
| Your budget is at the lower end | Compare: InData Labs (Not published) vs ScienceSoft (Not published) |
| You need specialist depth in a specific vertical | ScienceSoft |
| You need production MLOps support after model launch | ScienceSoft |
| You need consulting before committing to a build | ScienceSoft |
Use case fit: InData Labs vs ScienceSoft
| Use case | InData Labs fit | ScienceSoft fit | Winner |
|---|---|---|---|
| FinTech company needs predictive analytics built by a team that has done nothing but AI/data science since 2014. | Strong | Limited | InData Labs |
| Healthcare startup needs a computer vision model with a small, senior delivery team. | Strong | Strong | Both equally |
| Enterprise wants AI/MLOps delivery from a vendor with 35+ years of institutional stability. | Limited | Strong | ScienceSoft |
| Insurance or healthcare company needs a vendor with a very large (4,200+) project delivery track record. | Limited | Strong | ScienceSoft |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | 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. Ten-plus years as a pure-play AI/data-science firm with no general software-development sideline.. It is best for finTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead..
ScienceSoft (3.8/5) is the better choice when enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record.. 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 that want a decade-old AI specialist without enterprise-scale overhead.. ScienceSoft is better for enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record..
How do InData Labs and ScienceSoft differ in pricing?
InData Labs uses project-based, dedicated team pricing with a minimum engagement of Not published. ScienceSoft uses time & materials, managed engagement 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?
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 company before shortlisting.
What are the main differences between InData Labs and ScienceSoft?
InData Labs's primary differentiator is: ten-plus years as a pure-play ai/data-science firm with no general software-development sideline.. ScienceSoft's primary differentiator is: 35+ years of continuous operating history (since 1989), among the longest-established vendors in this list.. They also differ in team size (50–100 vs 750+), minimum engagement (Not published vs Not published), and primary industries served (FinTech, Healthcare vs Healthcare, Insurance).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.