Yalantis vs Modak: full comparison for 2026
Last updated: July 2026
Quick verdict
Yalantis (3.9/5) edges ahead of Modak (3.7/5) overall. Yalantis is the better choice for healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering. Modak is the stronger option for large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption. The right choice depends on your project size, budget, and required tech stack.
Yalantis vs Modak: head-to-head summary
| Criterion | Yalantis | Modak |
|---|---|---|
| Founded | 2008 | 2016 |
| HQ | Kyiv, Ukraine | San Jose, CA |
| Team size | 200–400 | 100–200 |
| Rating | 3.9 / 5 | 3.7 / 5 |
| Best for | Healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering | Large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption |
| Pricing model | Fixed project, T&M | T&M, retainer |
| Min. engagement | $25K+ | $50K+ |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, Apache Spark, Databricks |
| Industries served | healthcare, fintech, saas, logistics, manufacturing | financial, healthcare, manufacturing, logistics, saas |
Yalantis vs Modak: overview
Yalantis
Yalantis was founded in 2008 and operates with a focus on compliance-first IoT and software engineering alongside machine learning consulting. The company's ML team provides domain-specific consulting, model deployment, and ongoing support, with depth in regulated industries including healthcare and fintech. ML consultants hold master's degrees in machine learning and have production data science experience. (Founded year per Tracxn; specialisation per Yalantis official website.)
Modak
Modak is an AI-native data engineering company headquartered in San Jose, California, founded in 2016. The company uses machine learning techniques to transform how structured and unstructured enterprise data is prepared, consumed, and shared — focusing on AI-driven data modernisation for large organisations. Global consulting services help enterprises modernise data infrastructure, accelerate AI readiness, and drive measurable business outcomes. (Founding year and approach per Modak official website and ZoomInfo.)
Services and capabilities: Yalantis vs Modak
| Capability | Yalantis | Modak |
|---|---|---|
| Custom ML build | ✓ | ✗ |
| ML consulting | ✓ | ✓ |
| Computer vision | ✓ | ✗ |
| NLP / LLM | ✓ | ✗ |
| Predictive analytics | ✓ | ✓ |
| MLOps | ✗ | ✓ |
| Data engineering | ✗ | ✓ |
| Generative AI | ✗ | ✗ |
| Staff augmentation | ✗ | ✗ |
| Fixed-price projects | ✓ | ✗ |
| Dedicated team model | ✗ | ✗ |
Tech stack comparison: Yalantis vs Modak
| Framework / platform | Yalantis | Modak |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | N/A |
Pricing comparison: Yalantis vs Modak
| Criterion | Yalantis | Modak |
|---|---|---|
| Minimum engagement | $25K+ | $50K+ |
| Engagement models | Fixed project, T&M, Retainer | T&M, Retainer |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Yalantis vs Modak
| Dimension | Yalantis | Modak |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | healthcare, fintech, saas | financial, healthcare, manufacturing |
| Best use cases | Compliance-aware ML model for healthcare data, Predictive analytics for fintech risk management | Enterprise data modernisation for AI readiness, ML-powered ETL and data prep pipeline |
| Typical project type | Fixed project | T&M |
Yalantis vs Modak: pros and cons
| Yalantis | |
|---|---|
| + | Compliance-first approach for regulated healthcare and fintech projects |
| + | Full-lifecycle ML: from consulting through deployment and support |
| + | Master's-qualified ML consultants — verifiable technical depth |
| + | IoT integration experience alongside ML — rare combination |
| - | Ukraine-based delivery carries geographic risk considerations for some clients |
| - | Less suited to pure data science research or exploratory projects |
| Modak | |
|---|---|
| + | ML applied to data engineering itself — accelerates data prep for ML programmes |
| + | AI-native from inception — not a repositioned data warehouse firm |
| + | Strong on unstructured data processing for AI readiness |
| + | San Jose HQ with enterprise client focus |
| - | Data engineering focus — not suited to custom ML model development or computer vision |
| - | Minimum engagement oriented toward large enterprise programmes |
| - | Less suited to companies without an existing large data estate |
Who should choose Yalantis?
Yalantis is the right choice for healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering.
Compliance-first ML delivery — particularly strong for healthcare and regulated fintech with IoT integration needs. Minimum engagement starts at $25K+. Works best with clients in healthcare, fintech, saas, logistics, manufacturing.
Who should choose Modak?
Modak is the right choice for large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption.
ML-powered data engineering — uses ML itself to accelerate data prep and modernisation at enterprise scale. Minimum engagement starts at $50K+. Works best with clients in financial, healthcare, manufacturing, logistics, saas.
Decision matrix: Yalantis vs Modak
| 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 | Check each company's engagement model |
| Your budget is at the lower end | Yalantis |
| You need specialist depth in a specific vertical | Yalantis |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Yalantis |
Use case fit: Yalantis vs Modak
| Use case | Yalantis fit | Modak fit | Winner |
|---|---|---|---|
| Compliance-aware ML model for healthcare data | Strong | Limited | Yalantis |
| Predictive analytics for fintech risk management | Strong | Limited | Yalantis |
| Enterprise data modernisation for AI readiness | Strong | Strong | Both equally |
| ML-powered ETL and data prep pipeline | Limited | Strong | Modak |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Yalantis vs Modak
Yalantis (3.9/5) is the stronger overall choice for most Machine Learning projects. Compliance-first ML delivery — particularly strong for healthcare and regulated fintech with IoT integration needs. It is best for healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering.
Modak (3.7/5) is the better choice when large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption. If your situation matches those criteria, Modak is a competitive option.
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Yalantis vs Modak FAQ
Is Yalantis better than Modak?
Yalantis (3.9/5) scores higher overall, but "better" depends on your use case. Yalantis is better for healthcare and fintech companies needing compliance-aware ML consulting paired with IoT or embedded engineering. Modak is better for large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption.
How do Yalantis and Modak differ in pricing?
Yalantis uses fixed project, t&m pricing with a minimum engagement of $25K+. Modak uses t&m, retainer pricing with a minimum engagement of $50K+. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Yalantis or Modak?
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 agency before shortlisting.
What are the main differences between Yalantis and Modak?
Yalantis's primary differentiator is: compliance-first ml delivery — particularly strong for healthcare and regulated fintech with iot integration needs. Modak's primary differentiator is: ml-powered data engineering — uses ml itself to accelerate data prep and modernisation at enterprise scale. They also differ in team size (200–400 vs 100–200), minimum engagement ($25K+ vs $50K+), and primary industries served (healthcare, fintech vs financial, healthcare).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.