Yalantis vs ScienceSoft: full comparison for 2026
Last updated: July 2026
Quick verdict
Yalantis (4.0/5) edges ahead of ScienceSoft (3.8/5) overall. Yalantis is the better choice for compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms.. 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.
Yalantis vs ScienceSoft: head-to-head summary
| Criterion | Yalantis | ScienceSoft |
|---|---|---|
| Founded | 2008 | 1989 |
| HQ | Larnaca, Cyprus | McKinney, Texas, United States |
| Team size | 500+ | 750+ |
| Rating | 4.0 / 5 | 3.8 / 5 |
| Best for | Compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms. | Enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record. |
| Pricing model | Fixed project, dedicated team | Time & materials, managed engagement |
| Min. engagement | $10,000 | Not published |
| Primary tech stack | AWS SageMaker, Azure ML, Google Cloud Vertex AI | AWS, Azure ML, Google Cloud |
| Industries served | Healthcare, IoT & Embedded Systems, FinTech, Logistics & Supply Chain | Healthcare, Insurance, Manufacturing, Retail & E-commerce, Telecom |
Yalantis vs ScienceSoft: 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.
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: Yalantis vs ScienceSoft
| Capability | Yalantis | ScienceSoft |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✓ | ✓ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✗ | ✓ |
Tech stack comparison: Yalantis vs ScienceSoft
| Framework / platform | Yalantis | ScienceSoft |
|---|---|---|
| TensorFlow | N/A | N/A |
| PyTorch | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | ✓ |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Yalantis vs ScienceSoft
| Criterion | Yalantis | ScienceSoft |
|---|---|---|
| Minimum engagement | $10,000 | Not published |
| Engagement models | Fixed project, Dedicated team, Staff augmentation | Managed engagement, Time & materials, Staff augmentation |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Mid-market |
Target audience comparison: Yalantis vs ScienceSoft
| Dimension | Yalantis | ScienceSoft |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, IoT & Embedded Systems, FinTech | Healthcare, Insurance, Manufacturing |
| 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. | 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 | Fixed project | Managed engagement |
Yalantis vs ScienceSoft: 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. |
| 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 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 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: Yalantis vs ScienceSoft
| 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 ScienceSoft (Not published) |
| You need specialist depth in a specific vertical | ScienceSoft |
| You need production MLOps support after model launch | Both offer MLOps support |
| You need consulting before committing to a build | ScienceSoft |
Use case fit: Yalantis vs ScienceSoft
| Use case | Yalantis fit | ScienceSoft fit | Winner |
|---|---|---|---|
| Healthcare or IoT company needs ML development from a compliance-first engineering partner. | Strong | Strong | Both equally |
| Company wants flexibility to deploy models across AWS, Azure, or GCP without being locked into one platform. | 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: Yalantis vs ScienceSoft
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..
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
Yalantis vs ScienceSoft FAQ
Is Yalantis better than ScienceSoft?
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.. 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 Yalantis and ScienceSoft differ in pricing?
Yalantis uses fixed project, dedicated team pricing with a minimum engagement of $10,000. 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: Yalantis or ScienceSoft?
ScienceSoft 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 ScienceSoft?
Yalantis's primary differentiator is: compliance-first positioning combined with multi-cloud ml delivery (aws, azure, gcp) under one roof.. 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 (500+ vs 750+), minimum engagement ($10,000 vs Not published), and primary industries served (Healthcare, IoT & Embedded Systems vs Healthcare, Insurance).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.