Yalantis vs Space-O Technologies: full comparison for 2026
Last updated: July 2026
Quick verdict
Yalantis (4.0/5) edges ahead of Space-O Technologies (4.0/5) overall. Yalantis is the better choice for compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms.. Space-O Technologies is the stronger option for companies that need machine learning embedded into a mobile or web application, not a standalone ML research engagement.. The right choice depends on your project size, budget, and required tech stack.
Yalantis vs Space-O Technologies: head-to-head summary
| Criterion | Yalantis | Space-O Technologies |
|---|---|---|
| Founded | 2008 | 2010 |
| HQ | Larnaca, Cyprus | Ahmedabad, India |
| Team size | 500+ | 140+ |
| Rating | 4.0 / 5 | 4.0 / 5 |
| Best for | Compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms. | Companies that need machine learning embedded into a mobile or web application, not a standalone ML research engagement. |
| Pricing model | Fixed project, dedicated team | Project-based, dedicated team |
| Min. engagement | $10,000 | Not published |
| Primary tech stack | AWS SageMaker, Azure ML, Google Cloud Vertex AI | TensorFlow, Keras, OpenAI API |
| Industries served | Healthcare, IoT & Embedded Systems, FinTech, Logistics & Supply Chain | Healthcare, EdTech, Retail & E-commerce, Travel & Hospitality |
Yalantis vs Space-O Technologies: overview
Yalantis
Yalantis was founded in 2008 with headquarters in Larnaca, Cyprus and development hubs in Dnipro, Kyiv, and Lviv, Ukraine, growing to roughly 500 specialists. The firm positions itself as a 'compliance-first engineering partner,' building high-performance ML models across Amazon, Microsoft Azure, and Google Cloud ML platforms, including data preparation, model selection, training, deployment, and multimodal LLM processing for visual and text data. Project costs are reported to range from $10,000 to over $800,000, indicating the firm handles both small scoped projects and large enterprise programs.
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.
Services and capabilities: Yalantis vs Space-O Technologies
| Capability | Yalantis | Space-O Technologies |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✓ | ✗ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✗ | ✗ |
Tech stack comparison: Yalantis vs Space-O Technologies
| Framework / platform | Yalantis | Space-O Technologies |
|---|---|---|
| TensorFlow | N/A | ✓ |
| PyTorch | N/A | N/A |
| AWS | ✓ | N/A |
| Azure | ✓ | N/A |
| Google Cloud | ✓ | N/A |
| LangChain | ✓ | ✓ |
| Hugging Face | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Yalantis vs Space-O Technologies
| Criterion | Yalantis | Space-O Technologies |
|---|---|---|
| Minimum engagement | $10,000 | Not published |
| Engagement models | Fixed project, Dedicated team, Staff augmentation | Project-based, Dedicated team, Fixed project |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Mid-market |
Target audience comparison: Yalantis vs Space-O Technologies
| Dimension | Yalantis | Space-O Technologies |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, IoT & Embedded Systems, FinTech | Healthcare, EdTech, Retail & E-commerce |
| Best use cases | Healthcare or IoT company needs ML development from a compliance-first engineering partner., Company wants flexibility to deploy models across AWS, Azure, or GCP without being locked into one platform. | 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. |
| Typical project type | Fixed project | Project-based |
Yalantis vs Space-O Technologies: pros and cons
| Yalantis | |
|---|---|
| + | Compliance-first positioning is a genuine differentiator for regulated industries like healthcare and embedded/IoT systems. |
| + | Multi-cloud ML delivery capability (AWS, Azure, GCP) avoids vendor lock-in to a single hyperscaler. |
| + | Wide project-cost range ($10,000–$800,000+) means the firm can serve both small scoped projects and large programs without switching vendors. |
| + | 500+ specialists across three Ukrainian development hubs provides meaningful delivery redundancy. |
| - | IoT and hardware engineering heritage means ML is one of several engineering disciplines rather than the firm's sole focus. |
| - | Larnaca, Cyprus legal HQ with all technical delivery in Ukraine is standard for the region but worth confirming for contract jurisdiction purposes. |
| 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. |
Who should choose Yalantis?
Yalantis is the right choice for compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms..
Compliance-first positioning combined with multi-cloud ML delivery (AWS, Azure, GCP) under one roof.. Minimum engagement starts at $10,000. Works best with clients in Healthcare, IoT & Embedded Systems, FinTech, Logistics & Supply Chain.
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.
Decision matrix: Yalantis vs Space-O Technologies
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Yalantis |
| You need a large dedicated team for an ongoing programme | Yalantis |
| Your budget is at the lower end | Compare: Yalantis ($10,000) vs Space-O Technologies (Not published) |
| You need specialist depth in a specific vertical | Yalantis |
| You need production MLOps support after model launch | Yalantis |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: Yalantis vs Space-O Technologies
| Use case | Yalantis fit | Space-O Technologies fit | Winner |
|---|---|---|---|
| Healthcare or IoT company needs ML development from a compliance-first engineering partner. | Strong | Limited | Yalantis |
| Company wants flexibility to deploy models across AWS, Azure, or GCP without being locked into one platform. | Strong | Strong | Both equally |
| 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. | Limited | Strong | Space-O Technologies |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | Limited | Limited | Both equally |
Verdict: Yalantis vs Space-O Technologies
Yalantis (4.0/5) is the stronger overall choice for most Machine Learning Development projects. Compliance-first positioning combined with multi-cloud ML delivery (AWS, Azure, GCP) under one roof.. It is best for compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms..
Space-O Technologies (4.0/5) is the better choice when companies that need machine learning embedded into a mobile or web application, not a standalone ML research engagement.. If your situation matches those criteria, Space-O Technologies is a competitive option.
Related comparisons
Yalantis vs Space-O Technologies FAQ
Is Yalantis better than Space-O Technologies?
Yalantis (4.0/5) scores higher overall, but "better" depends on your use case. Yalantis is better for compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms.. Space-O Technologies is better for companies that need machine learning embedded into a mobile or web application, not a standalone ML research engagement..
How do Yalantis and Space-O Technologies differ in pricing?
Yalantis uses fixed project, dedicated team pricing with a minimum engagement of $10,000. Space-O Technologies uses project-based, dedicated team 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: Yalantis or Space-O Technologies?
Yalantis 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 Yalantis and Space-O Technologies?
Yalantis's primary differentiator is: compliance-first positioning combined with multi-cloud ml delivery (aws, azure, gcp) under one roof.. 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.. They also differ in team size (500+ vs 140+), minimum engagement ($10,000 vs Not published), and primary industries served (Healthcare, IoT & Embedded Systems vs Healthcare, EdTech).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.