Addepto vs Quantiphi: full comparison for 2026
Last updated: July 2026
Quick verdict
Addepto (4.4/5) edges ahead of Quantiphi (4.2/5) overall. Addepto is the better choice for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build.. Quantiphi is the stronger option for enterprises standardized on AWS wanting a partner with the deepest documented AWS AI/ML partnership credentials in this comparison.. 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, USA |
| Team size | 51–200 | 1,001–5,000 |
| Rating | 4.4 / 5 | 4.2 / 5 |
| Best for | Cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build. | Enterprises standardized on AWS wanting a partner with the deepest documented AWS AI/ML partnership credentials in this comparison. |
| Pricing model | Project-based | Not published; enterprise project engagements |
| Min. engagement | $10,000 | Not published |
| Primary tech stack | Python, MLOps tooling, Cloud ML platforms (AWS/GCP/Azure) | AWS SageMaker, Amazon Bedrock, AWS |
| Industries served | Finance, Healthcare, Retail | Public sector, Healthcare, Financial services, Media |
Addepto vs Quantiphi: overview
Addepto
Addepto is a Poland-based AI consulting firm founded in 2018 by Artur Haponik and Edwin Lisowski that focuses specifically on machine learning consulting, MLOps consulting, and data/analytics advisory work rather than broader software development. The company has around 52 employees and holds a 4.7 Clutch rating, with Clutch-reported project costs typically in the $10,000–$49,000 range, making it one of the more budget-accessible options among firms in this category. Addepto has been recognized among Forbes' top AI consulting companies and appeared on the Deloitte Technology Fast 500 EMEA list, citing 1,193 percent revenue growth over the qualifying period. In December 2025, Addepto was acquired by KMS Technology, a US-based digital engineering, data, and AI company backed by growth private equity firm Sunstone Partners; Addepto now operates as an integrated division rather than as a fully independent company.
Quantiphi
Quantiphi is a digital engineering company founded in 2013 by Vivek Khemani, Asif Hasan, Ritesh Patel, and Reghu Hariharan, focused on applied artificial intelligence, machine learning, and data science for complex business problems. Headquartered in Marlborough, Massachusetts, the company operates across six global locations and reports between 1,000 and 5,000 employees. Quantiphi holds AWS Premier Global Consulting Partner status and was named the first Preferred Amazon Quick Global SI Partner by the AWS Generative AI Innovation Center, alongside being recognized as 2025 AWS Public Sector Global GenAI Consulting Partner of the Year.
Services and capabilities: Addepto vs Quantiphi
| Capability | Addepto | Quantiphi |
|---|---|---|
| Custom model training | ✓ | ✓ |
| Fine-tuning & adaptation | ✗ | ✗ |
| MLOps pipeline | ✓ | ✓ |
| Model deployment & serving | ✗ | ✓ |
| Data engineering for ML | ✓ | ✗ |
| ML infrastructure management | ✗ | ✓ |
| Computer vision | ✗ | ✗ |
| NLP & LLM development | ✗ | ✗ |
| Forecasting & time-series modeling | ✗ | ✗ |
| ML strategy consulting | ✓ | ✗ |
Tech stack comparison: Addepto vs Quantiphi
| Framework / platform | Addepto | Quantiphi |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| MLflow | N/A | N/A |
| AWS SageMaker | N/A | ✓ |
| Amazon Bedrock | N/A | ✓ |
| Google Cloud | N/A | N/A |
| Microsoft Azure | N/A | N/A |
| Kubernetes | N/A | ✓ |
| Snowflake | N/A | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: Addepto vs Quantiphi
| Criterion | Addepto | Quantiphi |
|---|---|---|
| Minimum engagement | $10,000 | Not published |
| Engagement models | Fixed project, Advisory/consulting retainer | Enterprise project engagement, Managed AI services |
| 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 | Finance, Healthcare, Retail | Public sector, Healthcare, Financial services |
| Best use cases | Auditing an existing ML pipeline and recommending MLOps improvements, Running a well-scoped, budget-constrained machine learning pilot | Building and deploying ML models on AWS SageMaker at enterprise scale, Running a generative AI initiative using Amazon Bedrock with AWS-certified delivery support |
| Typical project type | Fixed project | Enterprise project engagement |
Addepto vs Quantiphi: pros and cons
| Addepto | |
|---|---|
| + | 4.7 Clutch rating with lower typical project cost ($10K–$49K) than most peers in this comparison. |
| + | Named a top 10 AI consulting company by Forbes. |
| + | Deloitte Technology Fast 500 EMEA recognition (#143) signals strong recent revenue growth. |
| + | Focused specifically on ML/MLOps consulting rather than diluting attention across general software development. |
| - | Small team (~52 employees) caps capacity for large or multiple concurrent enterprise engagements. |
| - | Lower typical project size may signal a fit for smaller-scope work rather than large production ML platforms. |
| - | Public case studies with named enterprise clients are limited in available sources. |
| - | Now part of KMS Technology following the December 2025 acquisition, introducing near-term integration and roadmap uncertainty for prospective clients. |
| Quantiphi | |
|---|---|
| + | Strongest documented AWS partnership tier (Premier Global Consulting Partner) among companies in this comparison. |
| + | 2025 AWS Public Sector Global GenAI Consulting Partner of the Year recognition. |
| + | Reported $630.2M in revenue signals substantial scale and financial stability. |
| + | Multi-location global presence supports enterprise clients needing regional delivery. |
| - | Heavy AWS specialization may be less useful for clients standardized on Azure or GCP. |
| - | No clearly located aggregate Clutch/G2 star rating in available public sources. |
| - | Employee count range (1,000–5,000) is wide, making exact delivery capacity hard to pin down. |
| - | Pricing model and minimum engagement are not published. |
Who should choose Addepto?
Addepto is the right choice for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build..
Dedicated MLOps-consulting service line and Clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option.. Minimum engagement starts at $10,000. Works best with clients in Finance, Healthcare, Retail.
Who should choose Quantiphi?
Quantiphi is the right choice for enterprises standardized on AWS wanting a partner with the deepest documented AWS AI/ML partnership credentials in this comparison..
Deepest AWS-specific partnership credentials among firms researched, including AWS GenAI Innovation Center preferred-partner status.. Minimum engagement starts at Not published. Works best with clients in Public sector, Healthcare, Financial services, Media.
Decision matrix: Addepto vs Quantiphi
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Addepto |
| You need a large dedicated team for an ongoing programme | Check each company's engagement model |
| Your budget is at the lower end | Compare: Addepto ($10,000) vs Quantiphi (Not published) |
| 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 | Addepto |
Use case fit: Addepto vs Quantiphi
| Use case | Addepto fit | Quantiphi fit | Winner |
|---|---|---|---|
| Auditing an existing ML pipeline and recommending MLOps improvements | Strong | Limited | Addepto |
| Running a well-scoped, budget-constrained machine learning pilot | Strong | Strong | Both equally |
| Building and deploying ML models on AWS SageMaker at enterprise scale | Limited | Strong | Quantiphi |
| Running a generative AI initiative using Amazon Bedrock with AWS-certified delivery support | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Strong | Strong | Both equally |
Verdict: Addepto vs Quantiphi
Addepto (4.4/5) is the stronger overall choice for most ML Model Development projects. Dedicated MLOps-consulting service line and Clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option.. It is best for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build..
Quantiphi (4.2/5) is the better choice when enterprises standardized on AWS wanting a partner with the deepest documented AWS AI/ML partnership credentials in this comparison.. If your situation matches those criteria, Quantiphi is a competitive option.
Related comparisons
Addepto vs Quantiphi FAQ
Is Addepto better than Quantiphi?
Addepto (4.4/5) scores higher overall, but "better" depends on your use case. Addepto is better for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build.. Quantiphi is better for enterprises standardized on AWS wanting a partner with the deepest documented AWS AI/ML partnership credentials in this comparison..
How do Addepto and Quantiphi differ in pricing?
Addepto uses project-based pricing with a minimum engagement of $10,000. Quantiphi uses not published; enterprise project engagements pricing with a minimum engagement of Not published. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Addepto or Quantiphi?
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 company before shortlisting.
What are the main differences between Addepto and Quantiphi?
Addepto's primary differentiator is: dedicated mlops-consulting service line and clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option.. Quantiphi's primary differentiator is: deepest aws-specific partnership credentials among firms researched, including aws genai innovation center preferred-partner status.. They also differ in team size (51–200 vs 1,001–5,000), minimum engagement ($10,000 vs Not published), and primary industries served (Finance, Healthcare vs Public sector, Healthcare).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.