Space-O Technologies vs DataArt: full comparison for 2026
Last updated: July 2026
Quick verdict
Space-O Technologies (4.0/5) edges ahead of DataArt (3.9/5) overall. Space-O Technologies is the better choice for companies that need machine learning embedded into a mobile or web application, not a standalone ML research engagement.. DataArt is the stronger option for regulated-industry enterprises (finance, healthcare) that need AI delivery with built-in governance frameworks.. The right choice depends on your project size, budget, and required tech stack.
Space-O Technologies vs DataArt: head-to-head summary
| Criterion | Space-O Technologies | DataArt |
|---|---|---|
| Founded | 2010 | 1997 |
| HQ | Ahmedabad, India | New York, New York, United States |
| Team size | 140+ | 6,000+ |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Best for | Companies that need machine learning embedded into a mobile or web application, not a standalone ML research engagement. | Regulated-industry enterprises (finance, healthcare) that need AI delivery with built-in governance frameworks. |
| Pricing model | Project-based, dedicated team | Time & materials, managed engagement |
| Min. engagement | Not published | Not published |
| Primary tech stack | TensorFlow, Keras, OpenAI API | Python, AWS, Azure |
| Industries served | Healthcare, EdTech, Retail & E-commerce, Travel & Hospitality | FinTech, Media & Entertainment, Healthcare, Retail & E-commerce, Travel & Hospitality |
Space-O Technologies vs DataArt: overview
Space-O Technologies
Space-O Technologies was founded in 2010 by Rakeshkumar Patel and Atit Tusharbhai Purani, growing to roughly 140 full-stack engineers and AI specialists with offices in the US, Canada, and India. The company built its reputation on mobile app development (including early on-demand apps and EdTech products) before extending into machine learning on both neural and non-neural networks, working with frameworks including Keras, Caffe, and TensorFlow, plus more recent integration of OpenAI's GPT, Whisper, and LangChain. Its origin as a mobile-app shop means ML is a newer, added capability rather than the company's founding focus.
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.
Services and capabilities: Space-O Technologies vs DataArt
| Capability | Space-O Technologies | DataArt |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✗ | ✗ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✗ | ✓ |
Tech stack comparison: Space-O Technologies vs DataArt
| Framework / platform | Space-O Technologies | DataArt |
|---|---|---|
| TensorFlow | ✓ | N/A |
| PyTorch | N/A | N/A |
| AWS | N/A | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | N/A | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| Kubernetes | N/A | ✓ |
Pricing comparison: Space-O Technologies vs DataArt
| Criterion | Space-O Technologies | DataArt |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Project-based, Dedicated team, Fixed project | Managed engagement, Time & materials, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Space-O Technologies vs DataArt
| Dimension | Space-O Technologies | DataArt |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, EdTech, Retail & E-commerce | FinTech, Media & Entertainment, Healthcare |
| Best use cases | Company needs an ML feature (recommendation, prediction, chatbot) built directly into a new or existing mobile app., EdTech or travel company wants a single vendor for both application development and embedded AI features. | 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. |
| Typical project type | Project-based | Managed engagement |
Space-O Technologies vs DataArt: pros and cons
| Space-O Technologies | |
|---|---|
| + | 15 years of product-delivery history (since 2010), with a track record that includes early on-demand and EdTech app development. |
| + | 300+ delivered software solutions and 1,200+ clients gives it a broad delivery pattern library. |
| + | Integrates modern generative AI tooling (GPT, Whisper, LangChain) alongside classical ML frameworks (Keras, Caffe, TensorFlow). |
| + | Offices across US, Canada, and India provide time-zone coverage for North American clients. |
| - | Company's core identity and longest track record is in mobile app development, not ML — AI/ML is a newer, extended service line. |
| - | 140-person team spread across app development, AI development, and other services means ML-specific bench depth is smaller than the total headcount suggests. |
| 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. |
Who should choose Space-O Technologies?
Space-O Technologies is the right choice for companies that need machine learning embedded into a mobile or web application, not a standalone ML research engagement..
15 years of mobile/software product delivery experience (since 2010) with ML added as a production-application capability.. Minimum engagement starts at Not published. Works best with clients in Healthcare, EdTech, Retail & E-commerce, Travel & Hospitality.
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.
Decision matrix: Space-O Technologies vs DataArt
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Space-O Technologies |
| You need a large dedicated team for an ongoing programme | Space-O Technologies |
| Your budget is at the lower end | Compare: Space-O Technologies (Not published) vs DataArt (Not published) |
| You need specialist depth in a specific vertical | DataArt |
| You need production MLOps support after model launch | Both offer MLOps support |
| You need consulting before committing to a build | DataArt |
Use case fit: Space-O Technologies vs DataArt
| Use case | Space-O Technologies fit | DataArt fit | Winner |
|---|---|---|---|
| Company needs an ML feature (recommendation, prediction, chatbot) built directly into a new or existing mobile app. | Strong | Strong | Both equally |
| EdTech or travel company wants a single vendor for both application development and embedded AI features. | Strong | Limited | Space-O Technologies |
| Regulated financial services or healthcare company needs AI delivery with a built-in governance framework. | Limited | Strong | DataArt |
| Enterprise wants a vendor with named, publicly referenceable clients like Priceline and Legal & General. | Limited | Strong | DataArt |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | Limited | Limited | Both equally |
Verdict: Space-O Technologies vs DataArt
Space-O Technologies (4.0/5) is the stronger overall choice for most Machine Learning Development projects. 15 years of mobile/software product delivery experience (since 2010) with ML added as a production-application capability.. It is best for companies that need machine learning embedded into a mobile or web application, not a standalone ML research engagement..
DataArt (3.9/5) is the better choice when regulated-industry enterprises (finance, healthcare) that need AI delivery with built-in governance frameworks.. If your situation matches those criteria, DataArt is a competitive option.
Related comparisons
Space-O Technologies vs DataArt FAQ
Is Space-O Technologies better than DataArt?
Space-O Technologies (4.0/5) scores higher overall, but "better" depends on your use case. Space-O Technologies is better for companies that need machine learning embedded into a mobile or web application, not a standalone ML research engagement.. DataArt is better for regulated-industry enterprises (finance, healthcare) that need AI delivery with built-in governance frameworks..
How do Space-O Technologies and DataArt differ in pricing?
Space-O Technologies uses project-based, dedicated team pricing with a minimum engagement of Not published. DataArt 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: Space-O Technologies or DataArt?
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 Space-O Technologies and DataArt?
Space-O Technologies's primary differentiator is: 15 years of mobile/software product delivery experience (since 2010) with ml added as a production-application capability.. DataArt's primary differentiator is: artisyn, a proprietary ai-enabled operating model embedding governance and ai agents across the delivery lifecycle.. They also differ in team size (140+ vs 6,000+), minimum engagement (Not published vs Not published), and primary industries served (Healthcare, EdTech vs FinTech, Media & Entertainment).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.