Sigmoid vs Keyrus: full comparison for 2026
Last updated: July 2026
Quick verdict
Sigmoid (4.3/5) edges ahead of Keyrus (3.8/5) overall. Sigmoid is the better choice for fortune 500 retail, CPG, and financial services firms building AI-first data and ML platforms. 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.
Sigmoid vs Keyrus: head-to-head summary
| Criterion | Sigmoid | Keyrus |
|---|---|---|
| Founded | 2013 | 2000 |
| HQ | San Jose, CA | Paris, France |
| Team size | 500+ | 3,500+ |
| Rating | 4.3 / 5 | 3.8 / 5 |
| Best for | Fortune 500 retail, CPG, and financial services firms building AI-first data and ML platforms | International enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience |
| Pricing model | T&M, retainer | T&M, retainer |
| Min. engagement | $50K+ | $50K+ |
| Primary tech stack | Python, Databricks, Snowflake | Python, Tableau, Power BI |
| Industries served | retail, fintech, financial, CPG, manufacturing | financial, retail, healthcare, manufacturing, media |
Sigmoid vs Keyrus: overview
Sigmoid
Sigmoid was founded in 2013 and is headquartered in San Jose, California. The company focuses on AI-first data engineering, analytics, GenAI, and ML for Fortune 500 clients across retail, CPG, and financial services. Sigmoid was named to the Inc. 5000 in 2024 and raised a Series B from Sequoia Capital India in 2022. Core capabilities include Agentic AI, ML model deployment, data infrastructure modernisation, and BI platforms. (Employee count ~500+ per Sigmoid LinkedIn; funding per TechCrunch and Crunchbase.)
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: Sigmoid vs Keyrus
| Capability | Sigmoid | 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: Sigmoid vs Keyrus
| Framework / platform | Sigmoid | Keyrus |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | N/A | N/A |
| PyTorch | ✓ | N/A |
| AWS SageMaker | N/A | N/A |
| Azure ML | N/A | N/A |
Pricing comparison: Sigmoid vs Keyrus
| Criterion | Sigmoid | Keyrus |
|---|---|---|
| Minimum engagement | $50K+ | $50K+ |
| Engagement models | T&M, Retainer, Dedicated team | T&M, Retainer, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Sigmoid vs Keyrus
| Dimension | Sigmoid | Keyrus |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | retail, fintech, financial | financial, retail, healthcare |
| Best use cases | ML-powered demand forecasting for CPG, Agentic AI for financial services analytics | Industrial AI deployment at enterprise scale, Analytics and ML platform for financial services |
| Typical project type | T&M | T&M |
Sigmoid vs Keyrus: pros and cons
| Sigmoid | |
|---|---|
| + | Sequoia-backed with proven Fortune 500 execution in retail and CPG |
| + | Deep on data infrastructure: Databricks, Snowflake, Spark, dbt |
| + | Agentic AI and GenAI integrated into analytics programmes |
| + | Inc. 5000 recognition in 2024 signals verified revenue growth |
| + | Strong post-deployment ownership model |
| - | Minimum engagement oriented toward large programmes — not small pilots |
| - | Industry concentration in retail, CPG, and financial services — less suited to healthcare or government |
| 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 Sigmoid?
Sigmoid is the right choice for fortune 500 retail, CPG, and financial services firms building AI-first data and ML platforms.
Sequoia-backed AI and data engineering specialist with a Fortune 500 client portfolio in retail and CPG. Minimum engagement starts at $50K+. Works best with clients in retail, fintech, financial, CPG, manufacturing.
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: Sigmoid vs Keyrus
| 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 | Sigmoid |
| Your budget is at the lower end | Sigmoid |
| You need specialist depth in a specific vertical | Sigmoid |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Sigmoid |
Use case fit: Sigmoid vs Keyrus
| Use case | Sigmoid fit | Keyrus fit | Winner |
|---|---|---|---|
| ML-powered demand forecasting for CPG | Strong | Limited | Sigmoid |
| Agentic AI for financial services analytics | Strong | Limited | Sigmoid |
| Industrial AI deployment at enterprise scale | Limited | Strong | Keyrus |
| Analytics and ML platform for financial services | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Sigmoid vs Keyrus
Sigmoid (4.3/5) is the stronger overall choice for most Machine Learning projects. Sequoia-backed AI and data engineering specialist with a Fortune 500 client portfolio in retail and CPG. It is best for fortune 500 retail, CPG, and financial services firms building AI-first data and ML platforms.
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.
Related comparisons
Sigmoid vs Keyrus FAQ
Is Sigmoid better than Keyrus?
Sigmoid (4.3/5) scores higher overall, but "better" depends on your use case. Sigmoid is better for fortune 500 retail, CPG, and financial services firms building AI-first data and ML platforms. Keyrus is better for international enterprises seeking a global data and AI consulting partner with industrial-AI implementation experience.
How do Sigmoid and Keyrus differ in pricing?
Sigmoid uses t&m, retainer pricing with a minimum engagement of $50K+. 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: Sigmoid 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 Sigmoid and Keyrus?
Sigmoid's primary differentiator is: sequoia-backed ai and data engineering specialist with a fortune 500 client portfolio in retail and cpg. 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 (500+ vs 3,500+), minimum engagement ($50K+ vs $50K+), and primary industries served (retail, fintech vs financial, retail).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.