Provectus vs Yalantis: full comparison for 2026
Last updated: July 2026
Quick verdict
Provectus (4.8/5) edges ahead of Yalantis (4.0/5) overall. Provectus is the better choice for mid-market and enterprise companies that need production-grade MLOps, not just a proof of concept.. 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.
Provectus vs Yalantis: head-to-head summary
| Criterion | Provectus | Yalantis |
|---|---|---|
| Founded | 2010 | 2008 |
| HQ | Palo Alto, California, United States | Larnaca, Cyprus |
| Team size | 500–1,000 | 500+ |
| Rating | 4.8 / 5 | 4.0 / 5 |
| Best for | Mid-market and enterprise companies that need production-grade MLOps, not just a proof of concept. | Compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms. |
| Pricing model | Time & materials, fixed project | Fixed project, dedicated team |
| Min. engagement | Not published | $10,000 |
| Primary tech stack | AWS SageMaker, Kubernetes, MLflow | AWS SageMaker, Azure ML, Google Cloud Vertex AI |
| Industries served | Retail & E-commerce, Healthcare, Manufacturing, Media & Entertainment, FinTech | Healthcare, IoT & Embedded Systems, FinTech, Logistics & Supply Chain |
Provectus vs Yalantis: overview
Provectus
Provectus was founded in 2010 in Palo Alto, California by Stepan Pushkarev and operates as an AI-first systems integrator, combining cloud engineering, big data engineering, and applied ML/AI. The company has grown to an estimated 500–1,000 employees across nine locations and positions itself around running the AI systems its clients run their business on, rather than one-off model delivery. Clutch lists Provectus at a $50–$99/hr rate band, consistent with a mid-market enterprise consultancy rather than a boutique.
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: Provectus vs Yalantis
| Capability | Provectus | Yalantis |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✓ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✓ | ✓ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✗ | ✗ |
Tech stack comparison: Provectus vs Yalantis
| Framework / platform | Provectus | Yalantis |
|---|---|---|
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | N/A | ✓ |
| LangChain | N/A | ✓ |
| Hugging Face | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: Provectus vs Yalantis
| Criterion | Provectus | Yalantis |
|---|---|---|
| Minimum engagement | Not published | $10,000 |
| Engagement models | Dedicated team, Fixed project, Managed MLOps | Fixed project, Dedicated team, Staff augmentation |
| Rate transparency | Not public | Minimum disclosed |
| Price tier | Mid-market | Accessible |
Target audience comparison: Provectus vs Yalantis
| Dimension | Provectus | Yalantis |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Retail & E-commerce, Healthcare, Manufacturing | Healthcare, IoT & Embedded Systems, FinTech |
| Best use cases | Company has a working ML prototype and needs it hardened into a production MLOps pipeline., Enterprise needs a single vendor for both cloud infrastructure and ML delivery. | 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 | Dedicated team | Fixed project |
Provectus vs Yalantis: pros and cons
| Provectus | |
|---|---|
| + | 500–1,000 person bench supports enterprise-scale engagements without subcontracting. |
| + | Combines cloud infrastructure engineering with ML delivery, reducing hand-off friction to a separate DevOps vendor. |
| + | 15+ years of delivery history since 2010 gives the firm depth in productionizing (not just prototyping) ML systems. |
| + | Broad industry coverage from retail to healthcare reduces vertical-specific onboarding risk. |
| - | Mid-market hourly rate ($50–$99/hr per Clutch) sits below boutique AI specialists, which can mean less senior researcher involvement per project. |
| - | Company size means engagement structure is closer to a managed vendor relationship than a tight advisory partnership. |
| 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 Provectus?
Provectus is the right choice for mid-market and enterprise companies that need production-grade MLOps, not just a proof of concept..
AI-first systems integrator built around running production ML/AI infrastructure long-term.. Minimum engagement starts at Not published. Works best with clients in Retail & E-commerce, Healthcare, Manufacturing, Media & Entertainment, FinTech.
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: Provectus vs Yalantis
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Provectus |
| You need a large dedicated team for an ongoing programme | Provectus |
| Your budget is at the lower end | Compare: Provectus (Not published) vs Yalantis ($10,000) |
| You need specialist depth in a specific vertical | Provectus |
| 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: Provectus vs Yalantis
| Use case | Provectus fit | Yalantis fit | Winner |
|---|---|---|---|
| Company has a working ML prototype and needs it hardened into a production MLOps pipeline. | Strong | Strong | Both equally |
| Enterprise needs a single vendor for both cloud infrastructure and ML delivery. | Strong | Limited | Provectus |
| 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: Provectus vs Yalantis
Provectus (4.8/5) is the stronger overall choice for most Machine Learning Development projects. AI-first systems integrator built around running production ML/AI infrastructure long-term.. It is best for mid-market and enterprise companies that need production-grade MLOps, not just a proof of concept..
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
Provectus vs Yalantis FAQ
Is Provectus better than Yalantis?
Provectus (4.8/5) scores higher overall, but "better" depends on your use case. Provectus is better for mid-market and enterprise companies that need production-grade MLOps, not just a proof of concept.. Yalantis is better for compliance-sensitive industries (IoT, healthcare, embedded systems) that need ML delivery across all three major cloud platforms..
How do Provectus and Yalantis differ in pricing?
Provectus uses time & materials, fixed project 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: Provectus or Yalantis?
Provectus 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 Provectus and Yalantis?
Provectus's primary differentiator is: ai-first systems integrator built around running production ml/ai infrastructure long-term.. 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 (500–1,000 vs 500+), minimum engagement (Not published vs $10,000), and primary industries served (Retail & E-commerce, Healthcare vs Healthcare, IoT & Embedded Systems).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.