InData Labs vs Keyrus: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.6/5) edges ahead of Keyrus (3.8/5) overall. InData Labs is the better choice for fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems. Keyrus is the stronger option for international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs Keyrus: head-to-head summary
| Criterion | InData Labs | Keyrus |
|---|---|---|
| Founded | 2014 | 2000 |
| HQ | Nicosia, Cyprus | Paris, France |
| Team size | 80+ | 3,500+ |
| Rating | 4.6 / 5 | 3.8 / 5 |
| Best for | Fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems | International enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience |
| Pricing model | Fixed project, T&M | T&M, retainer |
| Min. engagement | $15K | $50K+ |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, Tableau, Power BI |
| Industries served | fintech, healthcare, saas, retail, logistics | financial, retail, healthcare, manufacturing, media |
InData Labs vs Keyrus: overview
InData Labs
InData Labs is a data science and AI consultancy founded in 2014, with headquarters in Nicosia, Cyprus and offices in Lithuania and the US. The firm covers the full ML stack: generative AI (LLMs, RAG systems, AI agents), predictive ML (recommendation engines, churn models, computer vision), data engineering, and DevOps for AI infrastructure. With 80+ data science professionals, it focuses on mid-market clients in fintech, healthcare, SaaS, retail, and logistics. (Team size per company LinkedIn; independently verified.)
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.)
Services and capabilities: InData Labs vs Keyrus
| Capability | InData Labs | Keyrus |
|---|---|---|
| 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: InData Labs vs Keyrus
| Framework / platform | InData Labs | Keyrus |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | N/A |
Pricing comparison: InData Labs vs Keyrus
| Criterion | InData Labs | Keyrus |
|---|---|---|
| Minimum engagement | $15K | $50K+ |
| Engagement models | Fixed project, T&M | T&M, Retainer, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: InData Labs vs Keyrus
| Dimension | InData Labs | Keyrus |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | fintech, healthcare, saas | financial, retail, healthcare |
| Best use cases | GenAI and RAG-based knowledge management system, Churn prediction model for SaaS | Industrial AI deployment at enterprise scale, Analytics and ML platform for financial services |
| Typical project type | Fixed project | T&M |
InData Labs vs Keyrus: pros and cons
| InData Labs | |
|---|---|
| + | 10+ years of pure ML/AI focus — not a repositioned generalist practice |
| + | Production-grade GenAI including RAG and AI agent systems |
| + | Covers the full stack: ML engineering, data engineering, and MLOps |
| + | Strong track record in regulated industries (fintech, healthcare) |
| + | Verified Clutch and DesignRush ratings across multiple client reviews |
| - | Smaller team (80+) limits capacity for very large concurrent programmes |
| - | Not a staffing platform — less suited to pure team augmentation needs |
| 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 |
Who should choose InData Labs?
InData Labs is the right choice for fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems.
Deep ML and GenAI specialist with 10+ years of production deployments across regulated industries. Minimum engagement starts at $15K. Works best with clients in fintech, healthcare, saas, retail, logistics.
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.
Decision matrix: InData Labs vs Keyrus
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | InData Labs |
| You need a large dedicated team for an ongoing programme | Keyrus |
| Your budget is at the lower end | InData Labs |
| You need specialist depth in a specific vertical | InData Labs |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | InData Labs |
Use case fit: InData Labs vs Keyrus
| Use case | InData Labs fit | Keyrus fit | Winner |
|---|---|---|---|
| GenAI and RAG-based knowledge management system | Strong | Limited | InData Labs |
| Churn prediction model for SaaS | Strong | Limited | InData Labs |
| Industrial AI deployment at enterprise scale | Limited | Strong | Keyrus |
| Analytics and ML platform for financial services | Limited | Strong | Keyrus |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: InData Labs vs Keyrus
InData Labs (4.6/5) is the stronger overall choice for most Machine Learning projects. Deep ML and GenAI specialist with 10+ years of production deployments across regulated industries. It is best for fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems.
Keyrus (3.8/5) is the better choice when international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience. If your situation matches those criteria, Keyrus is a competitive option.
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InData Labs vs Keyrus FAQ
Is InData Labs better than Keyrus?
InData Labs (4.6/5) scores higher overall, but "better" depends on your use case. InData Labs is better for fintech, healthcare, and SaaS companies needing production-grade ML including GenAI and RAG systems. Keyrus is better for international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience.
How do InData Labs and Keyrus differ in pricing?
InData Labs uses fixed project, t&m pricing with a minimum engagement of $15K. Keyrus 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: InData Labs or Keyrus?
Keyrus 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 InData Labs and Keyrus?
InData Labs's primary differentiator is: deep ml and genai specialist with 10+ years of production deployments across regulated industries. Keyrus's primary differentiator is: from experimental ai to industrial ai — consulting group specialising in productionising ml for large organisations. They also differ in team size (80+ vs 3,500+), minimum engagement ($15K vs $50K+), and primary industries served (fintech, healthcare vs financial, retail).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.