Tensorway vs Keyrus: full comparison for 2026
Quick verdict
Tensorway (4.8/5) edges ahead of Keyrus (3.8/5) overall. Tensorway is the better choice for mid-market teams, full-lifecycle ML ownership. Keyrus is the stronger option for international enterprises, industrial-AI implementation. The right choice depends on your project size, budget, and required tech stack.
Tensorway vs Keyrus: head-to-head summary
| Criterion | Tensorway | Keyrus |
|---|---|---|
| Founded | 2019 | 2000 |
| HQ | Alicante, Spain | Paris, France |
| Team size | 50+ | 3,500+ |
| Rating | 4.8 / 5 | 3.8 / 5 |
| Primary differentiator | Full-lifecycle ML ownership — model design, training infrastructure, and drift monitoring in one team | From experimental AI to industrial AI — consulting group specialising in productionising ML for large organisations |
| Pricing model | Fixed project, T&M, retainer, dedicated team | T&M, retainer |
| Min. engagement | $10K | $50K+ |
| Primary tech stack | Python, scikit-learn, XGBoost | Python, Tableau, Power BI |
| Industries served | e-commerce, logistics, fintech, healthcare, travel | financial, retail, healthcare, manufacturing, media |
Tensorway vs Keyrus: overview
Tensorway
Tensorway is a machine learning engineering firm operating as a dedicated ML-focused unit of its parent company, a software development firm established in 2001. It specialises in custom ML product builds that require sustained ownership — covering model design, training infrastructure, MLOps pipelines, and ongoing drift monitoring under one team. Its core stack includes Python (scikit-learn, XGBoost, LightGBM), Prophet for time-series, and cloud platforms such as AWS SageMaker and Azure ML. Industries served include e-commerce, logistics, fintech, healthcare, and online travel.
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: Tensorway vs Keyrus
| Capability | Tensorway | 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: Tensorway vs Keyrus
| Framework / platform | Tensorway | Keyrus |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | N/A | N/A |
| PyTorch | N/A | N/A |
| AWS SageMaker | ✓ | N/A |
| Azure ML | ✓ | N/A |
Pricing comparison: Tensorway vs Keyrus
| Criterion | Tensorway | Keyrus |
|---|---|---|
| Minimum engagement | $10K | $50K+ |
| Engagement models | Fixed project, T&M, Retainer, Dedicated team | T&M, Retainer, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Tensorway vs Keyrus
| Dimension | Tensorway | Keyrus |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | e-commerce, logistics, fintech | financial, retail, healthcare |
| Best use cases | Computer vision model for medical imaging diagnostics, NLP-based guest experience assistant for hospitality brands | Industrial AI deployment at enterprise scale, Analytics and ML platform for financial services |
| Typical project type | Fixed project | T&M |
Tensorway vs Keyrus: pros and cons
| Tensorway | |
|---|---|
| + | Boutique team structure — clients work directly with senior deep learning engineers, not account managers |
| + | Hands-on production ML delivery on AWS across computer vision and NLP workloads |
| + | Deep specialisation in deep learning, NLP, computer vision, and agentic AI rather than broad ML generalism |
| + | Established project-management and QA processes for predictable, well-documented delivery |
| + | Strong delivery track record in deep learning and NLP, with client references available under NDA |
| - | Smaller specialist team (50+) — less suited to very large enterprise programmes needing broad staffing |
| - | AWS-centric delivery — teams standardized on other clouds may need added integration effort |
| 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 Tensorway?
A typical fit: computer vision model for medical imaging diagnostics.
Full-lifecycle ML ownership — model design, training infrastructure, and drift monitoring in one team. Minimum engagement starts at $10K. Works best with clients in e-commerce, logistics, fintech, healthcare, travel.
Who should choose Keyrus?
A typical fit: industrial AI deployment at enterprise scale.
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: Tensorway vs Keyrus
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Tensorway |
| You need a large dedicated team for an ongoing programme | Tensorway |
| Your budget is at the lower end | Tensorway |
| You need specialist depth in a specific vertical | Tensorway |
| You need staff augmentation or team extension | Tensorway |
| You need consulting before committing to a build | Tensorway |
Use case fit: Tensorway vs Keyrus
| Use case | Tensorway fit | Keyrus fit | Winner |
|---|---|---|---|
| Computer vision model for medical imaging diagnostics | Strong | Limited | Tensorway |
| NLP-based guest experience assistant for hospitality brands | Strong | Limited | Tensorway |
| 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: Tensorway vs Keyrus
Tensorway (4.8/5) is the stronger overall choice for most Machine Learning projects. Full-lifecycle ML ownership — model design, training infrastructure, and drift monitoring in one team.
Keyrus (3.8/5) is worth a look if you need analytics and ML platform for financial services. If your situation matches that, Keyrus is a competitive option.
Related comparisons
Tensorway vs Keyrus FAQ
Is Tensorway better than Keyrus?
Tensorway (4.8/5) scores higher overall, but "better" depends on your use case. Tensorway's strongest advantage: boutique team structure — clients work directly with senior deep learning engineers, not account managers. Keyrus's strongest advantage: global footprint: 20+ countries, 3,500+ professionals.
How do Tensorway and Keyrus differ in pricing?
Tensorway uses fixed project, t&m, retainer, dedicated team pricing with a minimum engagement of $10K. 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: Tensorway 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 Tensorway and Keyrus?
Tensorway's primary differentiator is: full-lifecycle ML ownership — model design, training infrastructure, and drift monitoring in one team. 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 (50+ vs 3,500+), minimum engagement ($10K vs $50K+), and primary industries served (e-commerce, logistics vs financial, retail).