InData Labs vs Yalantis: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.5/5) edges ahead of Yalantis (4.0/5) overall. InData Labs is the better choice for finTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead.. Yalantis is the stronger option for compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms.. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs Yalantis: head-to-head summary
| Criterion | InData Labs | Yalantis |
|---|---|---|
| Founded | 2014 | 2008 |
| HQ | Limassol, Cyprus | Larnaca, Cyprus |
| Team size | 50–100 | 500+ |
| Rating | 4.5 / 5 | 4.0 / 5 |
| Best for | FinTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead. | Compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms. |
| Pricing model | Project-based, dedicated team | Fixed project, dedicated team |
| Min. engagement | Not published | $10,000 |
| Primary tech stack | Python, TensorFlow, PyTorch | AWS SageMaker, Azure ML, Google Cloud Vertex AI |
| Industries served | FinTech, Healthcare, Retail & E-commerce, Logistics & Supply Chain | Healthcare, IoT & Embedded Systems, FinTech, Logistics & Supply Chain |
InData Labs vs Yalantis: overview
InData Labs
InData Labs was founded in 2014 by Marat Karpeko and is headquartered in Limassol, Cyprus, with additional offices in Lithuania and the United States. The company has stayed a pure-play AI/data-science consultancy for over a decade, building production ML systems for fintech, healthcare, SaaS, retail, and logistics clients, and is listed in Clutch's Top 10 AI Software Companies leaders matrix. At roughly 80 professionals, it is one of the smaller specialist firms in this list, trading scale for narrower focus.
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.
Services and capabilities: InData Labs vs Yalantis
| Capability | InData Labs | Yalantis |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✓ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✗ | ✗ |
Tech stack comparison: InData Labs vs Yalantis
| Framework / platform | InData Labs | Yalantis |
|---|---|---|
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | N/A | ✓ |
| LangChain | N/A | ✓ |
| Hugging Face | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: InData Labs vs Yalantis
| Criterion | InData Labs | Yalantis |
|---|---|---|
| Minimum engagement | Not published | $10,000 |
| Engagement models | Project-based, Dedicated team | Fixed project, Dedicated team, Staff augmentation |
| Rate transparency | Not public | Minimum disclosed |
| Price tier | Mid-market | Accessible |
Target audience comparison: InData Labs vs Yalantis
| Dimension | InData Labs | Yalantis |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | FinTech, Healthcare, Retail & E-commerce | Healthcare, IoT & Embedded Systems, FinTech |
| Best use cases | FinTech company needs predictive analytics built by a team that has done nothing but AI/data science since 2014., Healthcare startup needs a computer vision model with a small, senior delivery team. | 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. |
| Typical project type | Project-based | Fixed project |
InData Labs vs Yalantis: pros and cons
| InData Labs | |
|---|---|
| + | Has operated as a dedicated AI/data science firm since 2014 with no pivot to general software outsourcing. |
| + | Ranked in Clutch's Top 10 AI Software Companies leaders matrix. |
| + | Covers the full pipeline from data engineering through generative AI and computer vision, avoiding narrow single-service lock-in. |
| + | Smaller team size (~80) generally means less account-management overhead between client and engineers. |
| - | At roughly 80 people, InData Labs cannot staff large multi-workstream enterprise programs the way a 2,000+ person firm can. |
| - | Limassol, Cyprus HQ has a thinner regional case-study base in North America compared to US-headquartered peers. |
| 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. |
Who should choose InData Labs?
InData Labs is the right choice for finTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead..
Ten-plus years as a pure-play AI/data-science firm with no general software-development sideline.. Minimum engagement starts at Not published. Works best with clients in FinTech, Healthcare, Retail & E-commerce, Logistics & Supply Chain.
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.
Decision matrix: InData Labs vs Yalantis
| 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 | InData Labs |
| Your budget is at the lower end | Compare: InData Labs (Not published) vs Yalantis ($10,000) |
| You need specialist depth in a specific vertical | InData Labs |
| 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: InData Labs vs Yalantis
| Use case | InData Labs fit | Yalantis fit | Winner |
|---|---|---|---|
| FinTech company needs predictive analytics built by a team that has done nothing but AI/data science since 2014. | Strong | Limited | InData Labs |
| Healthcare startup needs a computer vision model with a small, senior delivery team. | Strong | Strong | Both equally |
| 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 |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | Limited | Limited | Both equally |
Verdict: InData Labs vs Yalantis
InData Labs (4.5/5) is the stronger overall choice for most Machine Learning Development projects. Ten-plus years as a pure-play AI/data-science firm with no general software-development sideline.. It is best for finTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead..
Yalantis (4.0/5) is the better choice when compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms.. If your situation matches those criteria, Yalantis is a competitive option.
Related comparisons
InData Labs vs Yalantis FAQ
Is InData Labs better than Yalantis?
InData Labs (4.5/5) scores higher overall, but "better" depends on your use case. InData Labs is better for finTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead.. Yalantis is better for compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms..
How do InData Labs and Yalantis differ in pricing?
InData Labs uses project-based, dedicated team pricing with a minimum engagement of Not published. Yalantis uses fixed project, dedicated team pricing with a minimum engagement of $10,000. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: InData Labs or Yalantis?
InData Labs 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 InData Labs and Yalantis?
InData Labs's primary differentiator is: ten-plus years as a pure-play ai/data-science firm with no general software-development sideline.. Yalantis's primary differentiator is: compliance-first positioning combined with multi-cloud ml delivery (aws, azure, gcp) under one roof.. They also differ in team size (50–100 vs 500+), minimum engagement (Not published vs $10,000), and primary industries served (FinTech, Healthcare vs Healthcare, IoT & Embedded Systems).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.