Keyrus vs Modak: full comparison for 2026
Last updated: July 2026
Quick verdict
Keyrus (3.8/5) edges ahead of Modak (3.7/5) overall. Keyrus is the better choice for international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience. 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.
Keyrus vs Modak: head-to-head summary
| Criterion | Keyrus | Modak |
|---|---|---|
| Founded | 2000 | 2016 |
| HQ | Paris, France | San Jose, CA |
| Team size | 3,500+ | 100–200 |
| Rating | 3.8 / 5 | 3.7 / 5 |
| Best for | International enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience | Large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption |
| Pricing model | T&M, retainer | T&M, retainer |
| Min. engagement | $50K+ | $50K+ |
| Primary tech stack | Python, Tableau, Power BI | Python, Apache Spark, Databricks |
| Industries served | financial, retail, healthcare, manufacturing, media | financial, healthcare, manufacturing, logistics, saas |
Keyrus vs Modak: overview
Keyrus
Keyrus is an international consulting group founded in 2000, headquartered in Paris, France, and operating in over 20 countries with 3,500+ professionals. The company positions itself at the intersection of business, data, and AI — helping clients move from experimental AI to industrial-grade ML systems in production. Services span data strategy, BI, analytics, AI testing, and ML deployment. (Employee count and global footprint per Keyrus 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: Keyrus vs Modak
| Capability | Keyrus | 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: Keyrus vs Modak
| Framework / platform | Keyrus | Modak |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | N/A | N/A |
| PyTorch | N/A | N/A |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | N/A |
Pricing comparison: Keyrus vs Modak
| Criterion | Keyrus | Modak |
|---|---|---|
| Minimum engagement | $50K+ | $50K+ |
| Engagement models | T&M, Retainer, Dedicated team | T&M, Retainer |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Keyrus vs Modak
| Dimension | Keyrus | Modak |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | financial, retail, healthcare | financial, healthcare, manufacturing |
| Best use cases | Industrial AI deployment at enterprise scale, Analytics and ML platform for financial services | Enterprise data modernisation for AI readiness, ML-powered ETL and data prep pipeline |
| Typical project type | T&M | T&M |
Keyrus vs Modak: pros and cons
| Keyrus | |
|---|---|
| + | Global footprint: 20+ countries, 3,500+ professionals |
| + | Industrial-AI focus — moves clients from PoC to production scale |
| + | Strong analytics and BI alongside ML for full data stack coverage |
| + | AI testing and validation capability |
| - | Large-firm pricing not suited to startup or SMB budgets |
| - | AI is one offering within broader data consulting — not ML-first |
| 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 Keyrus?
Keyrus is the right choice for international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience.
From experimental AI to industrial AI — consulting group specialising in productionising ML for large organisations. Minimum engagement starts at $50K+. Works best with clients in financial, retail, healthcare, manufacturing, media.
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: Keyrus vs Modak
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| You need a large dedicated team for an ongoing programme | Keyrus |
| Your budget is at the lower end | Keyrus |
| You need specialist depth in a specific vertical | Keyrus |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Keyrus |
Use case fit: Keyrus vs Modak
| Use case | Keyrus fit | Modak fit | Winner |
|---|---|---|---|
| Industrial AI deployment at enterprise scale | Strong | Limited | Keyrus |
| Analytics and ML platform for financial services | Strong | Limited | Keyrus |
| 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: Keyrus vs Modak
Keyrus (3.8/5) is the stronger overall choice for most Machine Learning projects. From experimental AI to industrial AI — consulting group specialising in productionising ML for large organisations. It is best for international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience.
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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Keyrus vs Modak FAQ
Is Keyrus better than Modak?
Keyrus (3.8/5) scores higher overall, but "better" depends on your use case. Keyrus is better for international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience. Modak is better for large enterprises needing AI-driven data modernisation to prepare unstructured data for ML consumption.
How do Keyrus and Modak differ in pricing?
Keyrus uses t&m, retainer pricing with a minimum engagement of $50K+. 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: Keyrus or Modak?
Modak 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 Keyrus and Modak?
Keyrus's primary differentiator is: from experimental ai to industrial ai — consulting group specialising in productionising ml for large organisations. 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 (3,500+ vs 100–200), minimum engagement ($50K+ vs $50K+), and primary industries served (financial, retail vs financial, healthcare).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.