XenonStack vs Yalantis: full comparison for 2026
Last updated: July 2026
Quick verdict
XenonStack (4.4/5) edges ahead of Yalantis (4.0/5) overall. XenonStack is the better choice for companies building agentic AI or real-time data platforms that want a specialist rather than a general IT outsourcer.. 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.
XenonStack vs Yalantis: head-to-head summary
| Criterion | XenonStack | Yalantis |
|---|---|---|
| Founded | 2016 | 2008 |
| HQ | Mohali, India | Larnaca, Cyprus |
| Team size | 50–100 | 500+ |
| Rating | 4.4 / 5 | 4.0 / 5 |
| Best for | Companies building agentic AI or real-time data platforms that want a specialist rather than a general IT outsourcer. | Compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms. |
| Pricing model | Project-based, retainer | Fixed project, dedicated team |
| Min. engagement | Not published | $10,000 |
| Primary tech stack | Kubernetes, Apache Kafka, AWS | AWS SageMaker, Azure ML, Google Cloud Vertex AI |
| Industries served | FinTech, Manufacturing, Telecom, Retail & E-commerce | Healthcare, IoT & Embedded Systems, FinTech, Logistics & Supply Chain |
XenonStack vs Yalantis: overview
XenonStack
XenonStack was founded in 2016 by Navdeep Singh Gill and is based in Mohali, India, operating as a technology consulting company centered on real-time data, generative AI, and agentic AI platform engineering. The company has grown from roughly 63 employees in 2023 to about 97 in 2026 and holds AWS, Azure, and Google Cloud partner status, alongside membership in the Cloud Native Computing Foundation and LF AI & Data. Its bootstrapped, revenue-funded growth (reported ~$3.8M ARR) suggests a stable but still relatively small operation for enterprise-scale programs.
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: XenonStack vs Yalantis
| Capability | XenonStack | Yalantis |
|---|---|---|
| Custom ML Models | ✗ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✓ | ✓ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✗ | ✗ |
Tech stack comparison: XenonStack vs Yalantis
| Framework / platform | XenonStack | Yalantis |
|---|---|---|
| TensorFlow | N/A | N/A |
| PyTorch | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | ✓ |
| LangChain | ✓ | ✓ |
| Hugging Face | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: XenonStack vs Yalantis
| Criterion | XenonStack | Yalantis |
|---|---|---|
| Minimum engagement | Not published | $10,000 |
| Engagement models | Project-based, Retainer, Dedicated team | Fixed project, Dedicated team, Staff augmentation |
| Rate transparency | Not public | Minimum disclosed |
| Price tier | Mid-market | Accessible |
Target audience comparison: XenonStack vs Yalantis
| Dimension | XenonStack | Yalantis |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | FinTech, Manufacturing, Telecom | Healthcare, IoT & Embedded Systems, FinTech |
| Best use cases | Enterprise needs a real-time data platform feeding downstream ML models., Company is building agentic AI workflows and needs specialist platform engineering, not just model development. | 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 |
XenonStack vs Yalantis: pros and cons
| XenonStack | |
|---|---|
| + | Multi-cloud partner status across AWS, Azure, and Google Cloud gives flexibility on platform choice rather than pushing a single vendor stack. |
| + | Bootstrapped and profitable growth trajectory (reported ~$3.8M ARR) signals operational stability without dependence on external funding rounds. |
| + | Cloud Native Computing Foundation and LF AI & Data membership reflects genuine open-source platform engineering involvement, not just marketing claims. |
| + | Specialization in agentic and real-time AI platform engineering is a differentiated niche versus generalist ML shops. |
| - | Team size of roughly 97 (2026) is small relative to the scale of enterprise real-time data platform programs it targets. |
| - | Conflicting HQ reports (Mohali, India vs. Dubai, UAE across sources) make it worth confirming the primary legal entity before contracting. |
| 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 XenonStack?
XenonStack is the right choice for companies building agentic AI or real-time data platforms that want a specialist rather than a general IT outsourcer..
Multi-cloud certified (AWS, Azure, GCP) platform-engineering specialist for real-time and agentic AI.. Minimum engagement starts at Not published. Works best with clients in FinTech, Manufacturing, Telecom, Retail & E-commerce.
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: XenonStack 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 | XenonStack |
| Your budget is at the lower end | Compare: XenonStack (Not published) vs Yalantis ($10,000) |
| You need specialist depth in a specific vertical | XenonStack |
| You need production MLOps support after model launch | Both offer MLOps support |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: XenonStack vs Yalantis
| Use case | XenonStack fit | Yalantis fit | Winner |
|---|---|---|---|
| Enterprise needs a real-time data platform feeding downstream ML models. | Strong | Limited | XenonStack |
| Company is building agentic AI workflows and needs specialist platform engineering, not just model development. | Strong | Strong | Both equally |
| Healthcare or IoT company needs ML development from a compliance-first engineering partner. | Limited | Strong | Yalantis |
| 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: XenonStack vs Yalantis
XenonStack (4.4/5) is the stronger overall choice for most Machine Learning Development projects. Multi-cloud certified (AWS, Azure, GCP) platform-engineering specialist for real-time and agentic AI.. It is best for companies building agentic AI or real-time data platforms that want a specialist rather than a general IT outsourcer..
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
XenonStack vs Yalantis FAQ
Is XenonStack better than Yalantis?
XenonStack (4.4/5) scores higher overall, but "better" depends on your use case. XenonStack is better for companies building agentic AI or real-time data platforms that want a specialist rather than a general IT outsourcer.. Yalantis is better for compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms..
How do XenonStack and Yalantis differ in pricing?
XenonStack uses project-based, retainer 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: XenonStack or Yalantis?
XenonStack 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 XenonStack and Yalantis?
XenonStack's primary differentiator is: multi-cloud certified (aws, azure, gcp) platform-engineering specialist for real-time and agentic ai.. 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, Manufacturing vs Healthcare, IoT & Embedded Systems).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.