Addepto vs Quantiphi: full comparison for 2026
Quick verdict
Addepto (4.3/5) edges ahead of Quantiphi (4.3/5) overall. Addepto is the better choice for data teams needing warehouse work before an LLM project. Quantiphi is the stronger option for google Cloud estates, high-volume document AI. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Quantiphi: head-to-head summary
| Criterion | Addepto | Quantiphi |
|---|---|---|
| Founded | 2018 | 2013 |
| HQ | Warsaw, Poland | Marlborough, MA, USA |
| Team size | 50–249 | 3,500+ |
| Rating | 4.3 / 5 | 4.3 / 5 |
| Primary differentiator | Data engineering and MLOps come first, so AI features sit on warehouses that are already clean and monitored | Premier partner on both Google Cloud and AWS, with a long run of Document AI and contact-centre deployments |
| Pricing model | $50–$99/hr (Clutch band); discovery workshops, then T&M | Fixed-price and T&M with offshore-weighted rates; rates on request |
| Min. engagement | $10,000+ (Clutch) | Not disclosed |
| Primary tech stack | Databricks, Snowflake, Azure OpenAI | Google Vertex AI, Google Document AI, AWS Bedrock |
| Industries served | Manufacturing, Retail & e-commerce, Aviation, Financial services | Healthcare, Insurance, Financial services, Public sector, Media |
Addepto vs Quantiphi: overview
Addepto
Addepto is a Warsaw-based AI and data consultancy that started trading in April 2018 (some directories list 2017). Clutch shows 50–249 employees, a $50–$99 hourly band and a $10,000 minimum project. CB Insights reports that KMS Technology acquired the company in December 2025, after an earlier tie-up with Grape Up. Its work typically begins with data engineering and moves on to generative AI, MLOps and AI discovery workshops.
Quantiphi
Quantiphi is an AI-first digital engineering firm founded in 2013, with U.S. headquarters in Marlborough, Massachusetts and most of its delivery staff in India. It employs about 3,500–4,000 people and holds premier-level partnerships with Google Cloud and AWS, plus many partner-of-the-year awards (exact counts differ across its own pages). Document AI, contact-centre AI and data modernization make up much of its published work.
Services and capabilities: Addepto vs Quantiphi
| Capability | Addepto | Quantiphi |
|---|---|---|
| CRM / ERP integration | ✗ | ✗ |
| LLM API gateway & cost control | ✓ | ✗ |
| Document processing | ✗ | ✓ |
| Agentic workflows | ✗ | ✗ |
| Fixed-price pilot | ✗ | ✗ |
| Managed services after launch | ✗ | ✗ |
| PII masking & access control | ✗ | ✗ |
Tech stack comparison: Addepto vs Quantiphi
| Framework / platform | Addepto | Quantiphi |
|---|---|---|
| Salesforce | N/A | N/A |
| SAP | N/A | N/A |
| Microsoft Dynamics 365 | N/A | N/A |
| HubSpot | N/A | N/A |
| Snowflake | ✓ | ✓ |
| Databricks | ✓ | ✓ |
| Azure OpenAI | ✓ | N/A |
| AWS Bedrock | N/A | ✓ |
| LangChain | ✓ | N/A |
| ServiceNow | N/A | N/A |
Pricing comparison: Addepto vs Quantiphi
| Criterion | Addepto | Quantiphi |
|---|---|---|
| Minimum engagement | $10,000+ (Clutch) | Not disclosed |
| Engagement models | Fixed project, Time & materials | Fixed project, Time & materials, Dedicated team |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Mid-market |
Target audience comparison: Addepto vs Quantiphi
| Dimension | Addepto | Quantiphi |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Retail & e-commerce, Aviation | Healthcare, Insurance, Financial services |
| Best use cases | Building a Databricks lakehouse that later feeds demand forecasts and an internal assistant., Productionizing ML models with MLflow and monitoring. | Insurance claims intake with Document AI extraction and human review., Contact-centre AI on Google Cloud for a high-volume support operation. |
| Typical project type | Fixed project | Fixed project |
Addepto vs Quantiphi: pros and cons
| Addepto | |
|---|---|
| + | Publicly listed Clutch rate band and $10,000 minimum make budgeting straightforward |
| + | Databricks and Spark experience helps when the data estate is the real blocker |
| + | Discovery workshops scope use cases before larger spend |
| + | Predictive analytics and LLM work available from the same team |
| - | Acquired by KMS Technology in December 2025 (per CB Insights), so ownership and leadership may change |
| - | Corporate history includes several restructurings, which complicates long-term vendor planning |
| - | Limited published work inside CRM platforms |
| Quantiphi | |
|---|---|
| + | Rare dual premier status with Google Cloud and AWS |
| + | Mature document AI practice for claims, forms and medical records |
| + | India-weighted delivery keeps blended rates below U.S. consultancies |
| + | Can scale teams quickly for large backlogs |
| - | Award and partner counts vary between its own pages, so confirm current tiers in partner directories |
| - | Offshore-heavy delivery needs strong client-side product ownership |
| - | Less visible work inside Salesforce or SAP |
Who should choose Addepto?
A typical fit: building a Databricks lakehouse that later feeds demand forecasts and an internal assistant.
Data engineering and MLOps come first, so AI features sit on warehouses that are already clean and monitored. Minimum engagement starts at $10,000+ (Clutch). Works best with clients in Manufacturing, Retail & e-commerce, Aviation, Financial services.
Who should choose Quantiphi?
A typical fit: insurance claims intake with Document AI extraction and human review.
Premier partner on both Google Cloud and AWS, with a long run of Document AI and contact-centre deployments. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Insurance, Financial services, Public sector, Media.
Decision matrix: Addepto vs Quantiphi
| Your situation | Recommended choice |
|---|---|
| You want a fixed-price audit or pilot before committing | Neither advertises one; ask for a scoped pilot |
| AI has to work inside your existing CRM or ERP | Neither lists CRM/ERP integration work |
| Personal data must be masked and answers limited by user permissions | Ask both for their PII and access-control design |
| Your budget is at the lower end | Compare: Addepto ($10,000+ (Clutch)) vs Quantiphi (Not disclosed) |
| You want the vendor to run the AI service after launch | Neither offers managed services; plan in-house operations |
| You are building multi-step agents across systems | Neither lists agentic AI work |
Use case fit: Addepto vs Quantiphi
| Use case | Addepto fit | Quantiphi fit | Winner |
|---|---|---|---|
| Building a Databricks lakehouse that later feeds demand forecasts and an internal assistant. | Strong | Limited | Addepto |
| Productionizing ML models with MLflow and monitoring. | Strong | Limited | Addepto |
| Insurance claims intake with Document AI extraction and human review. | Limited | Strong | Quantiphi |
| Contact-centre AI on Google Cloud for a high-volume support operation. | Limited | Strong | Quantiphi |
Verdict: Addepto vs Quantiphi
Addepto (4.3/5) is the stronger overall choice for most AI Integration projects. Data engineering and MLOps come first, so AI features sit on warehouses that are already clean and monitored.
Quantiphi (4.3/5) is worth a look if you need contact-centre AI on Google Cloud for a high-volume support operation. If your situation matches that, Quantiphi is a competitive option.
Related comparisons
Addepto vs Quantiphi FAQ
Is Addepto better than Quantiphi?
Addepto (4.3/5) scores higher overall, but "better" depends on your use case. Addepto's strongest advantage: publicly listed Clutch rate band and $10,000 minimum make budgeting straightforward. Quantiphi's strongest advantage: rare dual premier status with Google Cloud and AWS.
How do Addepto and Quantiphi differ in pricing?
Addepto pricing: $50–$99/hr (Clutch band); discovery workshops, then T&M. Minimum engagement: $10,000+ (Clutch). Quantiphi pricing: Fixed-price and T&M with offshore-weighted rates; rates on request. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Addepto or Quantiphi?
Addepto is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.
What are the main differences between Addepto and Quantiphi?
Addepto's primary differentiator is: data engineering and MLOps come first, so AI features sit on warehouses that are already clean and monitored. Quantiphi's primary differentiator is: premier partner on both Google Cloud and AWS, with a long run of Document AI and contact-centre deployments. They also differ in team size (50–249 vs 3,500+), minimum engagement ($10,000+ (Clutch) vs Not disclosed), and primary industries served (Manufacturing, Retail & e-commerce vs Healthcare, Insurance).
Verify all details directly with each company before making a decision.