Quantiphi vs Addepto: full comparison for 2026
Last updated: July 2026
Quick verdict
Quantiphi (4.4/5) edges ahead of Addepto (4.1/5) overall. Quantiphi is the better choice for enterprises, especially in financial services, needing AI delivery at scale with strong cloud-native ML platform engineering.. Addepto is the stronger option for companies wanting boutique AI/BI consulting from a team now backed by KMS Technology's additional resources post-acquisition.. The right choice depends on your project size, budget, and required tech stack.
Quantiphi vs Addepto: head-to-head summary
| Criterion | Quantiphi | Addepto |
|---|---|---|
| Founded | 2013 | 2018 |
| HQ | Marlborough, Massachusetts, USA | Warsaw, Poland |
| Team size | 1,001–5,000 | 51–200 |
| Rating | 4.4 / 5 | 4.1 / 5 |
| Best for | Enterprises, especially in financial services, needing AI delivery at scale with strong cloud-native ML platform engineering. | Companies wanting boutique AI/BI consulting from a team now backed by KMS Technology's additional resources post-acquisition. |
| Pricing model | Fixed project and managed AI services | Fixed project and consulting retainer |
| Min. engagement | Not published | $20K |
| Primary tech stack | Python, TensorFlow, Google Cloud Vertex AI | Python, Scikit-learn, TensorFlow |
| Industries served | Financial Services, Healthcare, Media, Technology/SaaS | Financial Services, Retail, Manufacturing |
Quantiphi vs Addepto: overview
Quantiphi
Quantiphi is an AI-first digital engineering company founded in 2013 by Vivek Khemani, Asif Hasan, Ritesh Patel, and Reghu Hariharan, headquartered in Marlborough, Massachusetts. Reported headcount is roughly 2,670–3,927 employees depending on source, making it one of the larger, more established AI-native firms on this list, with strong focus on financial services and cloud-native ML platform engineering.
Addepto
Addepto is an AI and data consulting company founded in Warsaw, Poland in 2018 by Edwin Lisowski and Artur Haponik (one source lists 2017), specializing in machine learning, artificial intelligence, and business intelligence solutions. Reported headcount is roughly 55–58 employees across Europe, North America, and Asia. In December 2025, Addepto was acquired by KMS Technology; prospective clients should confirm how the acquisition affects team continuity, existing contracts, and service delivery going forward.
Services and capabilities: Quantiphi vs Addepto
| Capability | Quantiphi | Addepto |
|---|---|---|
| Custom ML model development | ✓ | ✓ |
| Deep learning & computer vision | ✗ | ✗ |
| NLP & LLM / Generative AI | ✗ | ✗ |
| MLOps & production deployment | ✓ | ✗ |
| Data engineering | ✓ | ✗ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: Quantiphi vs Addepto
| Framework / platform | Quantiphi | Addepto |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| PyTorch | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | ✓ | N/A |
| Kubernetes | ✓ | N/A |
| Databricks | N/A | N/A |
| LangChain | N/A | N/A |
Pricing comparison: Quantiphi vs Addepto
| Criterion | Quantiphi | Addepto |
|---|---|---|
| Minimum engagement | Not published | $20K |
| Engagement models | Fixed project, Managed services | Fixed project, Consulting retainer |
| Rate transparency | Not public | Minimum disclosed |
| Price tier | Enterprise / not published | Accessible |
Target audience comparison: Quantiphi vs Addepto
| Dimension | Quantiphi | Addepto |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial Services, Healthcare, Media | Financial Services, Retail, Manufacturing |
| Best use cases | Enterprise financial-services AI programs requiring both scale and deep ML expertise, Cloud-native ML platform builds on GCP, AWS, or Azure at production scale | Mid-market companies wanting boutique AI/BI consulting now paired with KMS Technology's broader resources, Business intelligence projects that also require a machine learning component |
| Typical project type | Fixed project | Fixed project |
Quantiphi vs Addepto: pros and cons
| Quantiphi | |
|---|---|
| + | Founded as an AI-first company rather than a generalist IT firm that later added an AI practice |
| + | Enterprise-scale headcount (2,600+) supports large, multi-region programs |
| + | Strong cloud-native ML platform engineering, reducing gaps between model development and production deployment |
| + | 13 years of continuous focus on applied AI and analytics |
| - | Scale and enterprise sales process may be slower and less accessible for small pilot projects than boutique competitors |
| - | Recent employee counts show a reported year-over-year headcount decline (~4% per one source), worth asking about directly |
| - | Minimum engagement size and standard pricing are not publicly disclosed |
| Addepto | |
|---|---|
| + | 7 years of focused AI/BI consulting experience prior to the KMS Technology acquisition |
| + | Small team size historically meant direct founder-level access on engagements |
| + | Multi-continent presence (Europe, North America, Asia) despite a compact headcount |
| + | Acquisition by KMS Technology (Dec 2025) may bring additional delivery resources and stability |
| - | Acquired by KMS Technology in December 2025 — buyers should confirm how this affects team continuity, pricing, and existing contracts before signing |
| - | Public sources disagree on exact founding year (2017 vs. 2018) and employee count (55 vs. 58) |
| - | Post-acquisition integration could change the service delivery model in ways not yet publicly documented |
Who should choose Quantiphi?
Quantiphi is the right choice for enterprises, especially in financial services, needing AI delivery at scale with strong cloud-native ML platform engineering..
AI-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist IT outsourcing.. Minimum engagement starts at Not published. Works best with clients in Financial Services, Healthcare, Media, Technology/SaaS.
Who should choose Addepto?
Addepto is the right choice for companies wanting boutique AI/BI consulting from a team now backed by KMS Technology's additional resources post-acquisition..
Boutique AI/BI consultancy that gained additional scale and resources through its December 2025 acquisition by KMS Technology.. Minimum engagement starts at $20K. Works best with clients in Financial Services, Retail, Manufacturing.
Decision matrix: Quantiphi vs Addepto
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Quantiphi |
| You need a large dedicated team for an ongoing programme | Check each company's engagement model |
| Your budget is at the lower end | Compare: Quantiphi (Not published) vs Addepto ($20K) |
| You need specialist depth in a specific vertical | Quantiphi |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Quantiphi |
Use case fit: Quantiphi vs Addepto
| Use case | Quantiphi fit | Addepto fit | Winner |
|---|---|---|---|
| Enterprise financial-services AI programs requiring both scale and deep ML expertise | Strong | Limited | Quantiphi |
| Cloud-native ML platform builds on GCP, AWS, or Azure at production scale | Strong | Limited | Quantiphi |
| Mid-market companies wanting boutique AI/BI consulting now paired with KMS Technology's broader resources | Limited | Strong | Addepto |
| Business intelligence projects that also require a machine learning component | Limited | Strong | Addepto |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Quantiphi vs Addepto
Quantiphi (4.4/5) is the stronger overall choice for most Machine Learning Development projects. AI-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist IT outsourcing.. It is best for enterprises, especially in financial services, needing AI delivery at scale with strong cloud-native ML platform engineering..
Addepto (4.1/5) is the better choice when companies wanting boutique AI/BI consulting from a team now backed by KMS Technology's additional resources post-acquisition.. If your situation matches those criteria, Addepto is a competitive option.
Related comparisons
Quantiphi vs Addepto FAQ
Is Quantiphi better than Addepto?
Quantiphi (4.4/5) scores higher overall, but "better" depends on your use case. Quantiphi is better for enterprises, especially in financial services, needing AI delivery at scale with strong cloud-native ML platform engineering.. Addepto is better for companies wanting boutique AI/BI consulting from a team now backed by KMS Technology's additional resources post-acquisition..
How do Quantiphi and Addepto differ in pricing?
Quantiphi uses fixed project and managed ai services pricing with a minimum engagement of Not published. Addepto uses fixed project and consulting retainer pricing with a minimum engagement of $20K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Quantiphi or Addepto?
Quantiphi 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 Quantiphi and Addepto?
Quantiphi's primary differentiator is: ai-native firm that reached enterprise scale (2,600+ employees) without pivoting from generalist it outsourcing.. Addepto's primary differentiator is: boutique ai/bi consultancy that gained additional scale and resources through its december 2025 acquisition by kms technology.. They also differ in team size (1,001–5,000 vs 51–200), minimum engagement (Not published vs $20K), and primary industries served (Financial Services, Healthcare vs Financial Services, Retail).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.