DataArt vs ScienceSoft: full comparison for 2026
Last updated: July 2026
Quick verdict
DataArt (3.9/5) edges ahead of ScienceSoft (3.8/5) overall. DataArt is the better choice for regulated-industry enterprises (finance, healthcare) that need AI delivery with built-in governance frameworks.. 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.
DataArt vs ScienceSoft: head-to-head summary
| Criterion | DataArt | ScienceSoft |
|---|---|---|
| Founded | 1997 | 1989 |
| HQ | New York, New York, United States | McKinney, Texas, United States |
| Team size | 6,000+ | 750+ |
| Rating | 3.9 / 5 | 3.8 / 5 |
| Best for | Regulated-industry enterprises (finance, healthcare) that need AI delivery with built-in governance frameworks. | Enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record. |
| Pricing model | Time & materials, managed engagement | Time & materials, managed engagement |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, AWS, Azure | AWS, Azure ML, Google Cloud |
| Industries served | FinTech, Media & Entertainment, Healthcare, Retail & E-commerce, Travel & Hospitality | Healthcare, Insurance, Manufacturing, Retail & E-commerce, Telecom |
DataArt vs ScienceSoft: overview
DataArt
DataArt was founded in 1997 in New York City by Eugene Goland and has grown to more than 6,000 engineers across 40+ locations in the US, UK, Europe, Latin America, India, and the Middle East. The firm delivers data, analytics, and AI platforms for finance, media, healthcare, retail, and travel clients, built around Artisyn, its AI-enabled operating model that embeds AI agents and governance frameworks across the software development lifecycle, including regulated industries. Clients cited on its Clutch profile include Priceline, Ocado Technology, Legal & General, and Flutter Entertainment.
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: DataArt vs ScienceSoft
| Capability | DataArt | ScienceSoft |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✓ | ✓ |
Tech stack comparison: DataArt vs ScienceSoft
| Framework / platform | DataArt | ScienceSoft |
|---|---|---|
| TensorFlow | N/A | N/A |
| PyTorch | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: DataArt vs ScienceSoft
| Criterion | DataArt | ScienceSoft |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Managed engagement, Time & materials, Dedicated team | Managed engagement, Time & materials, Staff augmentation |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: DataArt vs ScienceSoft
| Dimension | DataArt | ScienceSoft |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | FinTech, Media & Entertainment, Healthcare | Healthcare, Insurance, Manufacturing |
| Best use cases | Regulated financial services or healthcare company needs AI delivery with a built-in governance framework., Enterprise wants a vendor with named, publicly referenceable clients like Priceline and Legal & General. | 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 | Managed engagement | Managed engagement |
DataArt vs ScienceSoft: pros and cons
| DataArt | |
|---|---|
| + | Named enterprise clients (Priceline, Ocado Technology, Legal & General, Flutter Entertainment) are independently verifiable via public case studies. |
| + | 27+ years of operating history (since 1997) gives it one of the longer track records in this list. |
| + | Artisyn operating model specifically addresses AI governance for regulated industries like financial services and healthcare, a genuine differentiator. |
| + | 6,000+ engineers across 40+ global locations provide substantial delivery capacity and geographic flexibility. |
| - | At 6,000+ employees, engagements are structured around managed delivery rather than close founder-level involvement. |
| - | AI/ML is one of several core service lines (alongside broader data/analytics platform work), not the firm's exclusive focus. |
| 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 DataArt?
DataArt is the right choice for regulated-industry enterprises (finance, healthcare) that need AI delivery with built-in governance frameworks..
Artisyn, a proprietary AI-enabled operating model embedding governance and AI agents across the delivery lifecycle.. Minimum engagement starts at Not published. Works best with clients in FinTech, Media & Entertainment, Healthcare, Retail & E-commerce, Travel & Hospitality.
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: DataArt 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 | DataArt |
| Your budget is at the lower end | Compare: DataArt (Not published) vs ScienceSoft (Not published) |
| You need specialist depth in a specific vertical | DataArt |
| You need production MLOps support after model launch | ScienceSoft |
| You need consulting before committing to a build | DataArt |
Use case fit: DataArt vs ScienceSoft
| Use case | DataArt fit | ScienceSoft fit | Winner |
|---|---|---|---|
| Regulated financial services or healthcare company needs AI delivery with a built-in governance framework. | Strong | Limited | DataArt |
| Enterprise wants a vendor with named, publicly referenceable clients like Priceline and Legal & General. | Strong | Strong | Both equally |
| Enterprise wants AI/MLOps delivery from a vendor with 35+ years of institutional stability. | Strong | Strong | Both equally |
| 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: DataArt vs ScienceSoft
DataArt (3.9/5) is the stronger overall choice for most Machine Learning Development projects. Artisyn, a proprietary AI-enabled operating model embedding governance and AI agents across the delivery lifecycle.. It is best for regulated-industry enterprises (finance, healthcare) that need AI delivery with built-in governance frameworks..
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
DataArt vs ScienceSoft FAQ
Is DataArt better than ScienceSoft?
DataArt (3.9/5) scores higher overall, but "better" depends on your use case. DataArt is better for regulated-industry enterprises (finance, healthcare) that need AI delivery with built-in governance frameworks.. 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 DataArt and ScienceSoft differ in pricing?
DataArt uses time & materials, managed engagement 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: DataArt or ScienceSoft?
DataArt 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 DataArt and ScienceSoft?
DataArt's primary differentiator is: artisyn, a proprietary ai-enabled operating model embedding governance and ai agents across the delivery lifecycle.. 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 (6,000+ vs 750+), minimum engagement (Not published vs Not published), and primary industries served (FinTech, Media & Entertainment vs Healthcare, Insurance).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.