Space-O Technologies vs SoftServe: full comparison for 2026
Last updated: July 2026
Quick verdict
Space-O Technologies (4.0/5) edges ahead of SoftServe (4.0/5) 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.. SoftServe is the stronger option for enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices.. The right choice depends on your project size, budget, and required tech stack.
Space-O Technologies vs SoftServe: head-to-head summary
| Criterion | Space-O Technologies | SoftServe |
|---|---|---|
| Founded | 2010 | 1993 |
| HQ | Ahmedabad, India | Austin, Texas, United States / Lviv, Ukraine |
| Team size | 140+ | 12,000+ |
| Rating | 4.0 / 5 | 4.0 / 5 |
| Best for | Companies that need machine learning embedded into a mobile or web application, not a standalone ML research engagement. | Enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices. |
| Pricing model | Project-based, dedicated team | Time & materials, managed engagement |
| Min. engagement | Not published | Not published |
| Primary tech stack | TensorFlow, Keras, OpenAI API | AWS, Azure, Google Cloud |
| Industries served | Healthcare, EdTech, Retail & E-commerce, Travel & Hospitality | Healthcare, FinTech, Retail & E-commerce, Manufacturing, Energy |
Space-O Technologies vs SoftServe: 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.
SoftServe
SoftServe was founded in 1993 in Lviv, Ukraine and now operates with a US headquarters in Austin, Texas and a European headquarters in Lviv, employing more than 12,000 people across 58 offices in 14 countries (with one source citing roughly 10,336 as of a recent count). The company's offerings span digital engineering, data analytics, cloud services, AI, machine learning, and IoT, and it ranked seventh among more than 130 Western European companies in Clutch's 2019 software development category. Its scale and 30+ year history make it a large, generalist engineering firm with AI as one of several core practices.
Services and capabilities: Space-O Technologies vs SoftServe
| Capability | Space-O Technologies | SoftServe |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| Generative AI | ✓ | ✗ |
| AI Consulting | ✗ | ✓ |
Tech stack comparison: Space-O Technologies vs SoftServe
| Framework / platform | Space-O Technologies | SoftServe |
|---|---|---|
| TensorFlow | ✓ | ✓ |
| PyTorch | N/A | N/A |
| AWS | N/A | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | N/A | ✓ |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| Kubernetes | N/A | ✓ |
Pricing comparison: Space-O Technologies vs SoftServe
| Criterion | Space-O Technologies | SoftServe |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Project-based, Dedicated team, Fixed project | Managed engagement, Time & materials, Staff augmentation |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Space-O Technologies vs SoftServe
| Dimension | Space-O Technologies | SoftServe |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, EdTech, Retail & E-commerce | Healthcare, FinTech, Retail & E-commerce |
| 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. | Large enterprise wants a single vendor covering AI/ML alongside cloud, data analytics, and IoT services., Company needs a choice between US and EU contracting jurisdictions from the same firm. |
| Typical project type | Project-based | Managed engagement |
Space-O Technologies vs SoftServe: 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. |
| SoftServe | |
|---|---|
| + | 12,000+ employees across 58 offices in 14 countries gives it enterprise-scale delivery capacity and geographic redundancy. |
| + | 31 years of continuous operation (since 1993) through multiple technology cycles, including the post-2022 relocation pressures on Ukraine-founded firms. |
| + | Ranked 7th among 130+ Western European companies in Clutch's 2019 software development category, an independently sourced recognition. |
| + | Dual US/Ukraine headquarters structure gives clients a choice of contracting jurisdiction. |
| - | 12,000+ person scale means AI/ML is one of several mature practices (alongside cloud, data analytics, IoT) rather than the firm's core identity. |
| - | Reported employee counts vary by thousands across sources (10,336 vs. 12,000+), reflecting the difficulty of pinning down exact current headcount at this scale. |
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 SoftServe?
SoftServe is the right choice for enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices..
31 years of engineering history (since 1993) with dual US and Ukraine headquarters and 12,000+ employees.. Minimum engagement starts at Not published. Works best with clients in Healthcare, FinTech, Retail & E-commerce, Manufacturing, Energy.
Decision matrix: Space-O Technologies vs SoftServe
| 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 SoftServe (Not published) |
| You need specialist depth in a specific vertical | SoftServe |
| You need production MLOps support after model launch | SoftServe |
| You need consulting before committing to a build | SoftServe |
Use case fit: Space-O Technologies vs SoftServe
| Use case | Space-O Technologies fit | SoftServe 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 |
| Large enterprise wants a single vendor covering AI/ML alongside cloud, data analytics, and IoT services. | Limited | Strong | SoftServe |
| Company needs a choice between US and EU contracting jurisdictions from the same firm. | Strong | Strong | Both equally |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | Limited | Limited | Both equally |
Verdict: Space-O Technologies vs SoftServe
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..
SoftServe (4.0/5) is the better choice when enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices.. If your situation matches those criteria, SoftServe is a competitive option.
Related comparisons
Space-O Technologies vs SoftServe FAQ
Is Space-O Technologies better than SoftServe?
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.. SoftServe is better for enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices..
How do Space-O Technologies and SoftServe differ in pricing?
Space-O Technologies uses project-based, dedicated team pricing with a minimum engagement of Not published. SoftServe 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 SoftServe?
SoftServe 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 SoftServe?
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.. SoftServe's primary differentiator is: 31 years of engineering history (since 1993) with dual us and ukraine headquarters and 12,000+ employees.. They also differ in team size (140+ vs 12,000+), minimum engagement (Not published vs Not published), and primary industries served (Healthcare, EdTech vs Healthcare, FinTech).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.