Provectus vs Addepto: full comparison for 2026
Last updated: July 2026
Quick verdict
Provectus (4.5/5) edges ahead of Addepto (4.4/5) overall. Provectus is the better choice for mid-market companies that need cloud data infrastructure and ML model development handled by the same integrator.. Addepto is the stronger option for cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build.. The right choice depends on your project size, budget, and required tech stack.
Provectus vs Addepto: head-to-head summary
| Criterion | Provectus | Addepto |
|---|---|---|
| Founded | 2010 | 2018 |
| HQ | Palo Alto, USA | Warsaw, Poland |
| Team size | 501–1,000 | 51–200 |
| Rating | 4.5 / 5 | 4.4 / 5 |
| Best for | Mid-market companies that need cloud data infrastructure and ML model development handled by the same integrator. | Cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build. |
| Pricing model | Not published; project and dedicated team | Project-based |
| Min. engagement | Not published | $10,000 |
| Primary tech stack | Python, AWS, GCP | Python, MLOps tooling, Cloud ML platforms (AWS/GCP/Azure) |
| Industries served | Cross-industry mid-market, Healthcare, Retail, Media | Finance, Healthcare, Retail |
Provectus vs Addepto: overview
Provectus
Provectus is an AI-first systems integrator and solutions provider founded in 2010 and headquartered in Palo Alto, California, with an international delivery team of more than 600 people spread across Ukraine, the US, Canada, and several other countries. The company's practice spans cloud engineering, big data engineering, and applied AI/ML, reflecting its origin as a broader cloud and data engineering consultancy that layered in machine learning capability. It positions itself specifically toward the mid-market rather than either small startups or the largest global enterprises. Founder and CEO Stepan Pushkarev continues to lead the company.
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.
Services and capabilities: Provectus vs Addepto
| Capability | Provectus | Addepto |
|---|---|---|
| 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: Provectus vs Addepto
| Framework / platform | Provectus | Addepto |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| MLflow | N/A | N/A |
| AWS SageMaker | N/A | N/A |
| Amazon Bedrock | N/A | 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: Provectus vs Addepto
| Criterion | Provectus | Addepto |
|---|---|---|
| Minimum engagement | Not published | $10,000 |
| Engagement models | Project-based, Dedicated team, Cloud/data engineering retainer | Fixed project, Advisory/consulting retainer |
| Rate transparency | Not public | Minimum disclosed |
| Price tier | Mid-market | Accessible |
Target audience comparison: Provectus vs Addepto
| Dimension | Provectus | Addepto |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Cross-industry mid-market, Healthcare, Retail | Finance, Healthcare, Retail |
| Best use cases | Building the data pipeline and feature store underneath a new ML model program, Migrating legacy big-data infrastructure to a cloud-native stack in preparation for ML workloads | Auditing an existing ML pipeline and recommending MLOps improvements, Running a well-scoped, budget-constrained machine learning pilot |
| Typical project type | Project-based | Fixed project |
Provectus vs Addepto: pros and cons
| Provectus | |
|---|---|
| + | Fifteen-year operating history with a clear mid-market positioning. |
| + | Strong big-data/cloud engineering foundation underpins its ML delivery, useful when data infrastructure is the bottleneck. |
| + | 600+ person distributed team offers meaningful delivery capacity without full enterprise-scale overhead. |
| + | Explicit mid-market focus avoids the "too small" or "too generic-enterprise" mismatch some buyers hit elsewhere. |
| - | Team-size reporting varies by source (500–1,000+), indicating some uncertainty in exact headcount. |
| - | Named, public case studies with concrete client outcomes are limited in available search results. |
| - | Pricing model and minimums are not published. |
| - | Positioning as a broad AI/cloud integrator means ML model development competes for attention with other service lines. |
| 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. |
Who should choose Provectus?
Provectus is the right choice for mid-market companies that need cloud data infrastructure and ML model development handled by the same integrator..
Grew out of cloud and big-data engineering roots, giving it particular strength in the data infrastructure layer underneath ML models, not just the models themselves.. Minimum engagement starts at Not published. Works best with clients in Cross-industry mid-market, Healthcare, Retail, Media.
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.
Decision matrix: Provectus vs Addepto
| 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 | Provectus |
| Your budget is at the lower end | Compare: Provectus (Not published) vs Addepto ($10,000) |
| You need specialist depth in a specific vertical | Provectus |
| 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: Provectus vs Addepto
| Use case | Provectus fit | Addepto fit | Winner |
|---|---|---|---|
| Building the data pipeline and feature store underneath a new ML model program | Strong | Limited | Provectus |
| Migrating legacy big-data infrastructure to a cloud-native stack in preparation for ML workloads | Strong | Limited | Provectus |
| Auditing an existing ML pipeline and recommending MLOps improvements | Limited | Strong | Addepto |
| Running a well-scoped, budget-constrained machine learning pilot | Limited | Strong | Addepto |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Strong | Strong | Both equally |
Verdict: Provectus vs Addepto
Provectus (4.5/5) is the stronger overall choice for most ML Model Development projects. Grew out of cloud and big-data engineering roots, giving it particular strength in the data infrastructure layer underneath ML models, not just the models themselves.. It is best for mid-market companies that need cloud data infrastructure and ML model development handled by the same integrator..
Addepto (4.4/5) is the better choice when cost-conscious teams that specifically need MLOps consulting or a well-scoped machine learning advisory engagement rather than a full custom software build.. If your situation matches those criteria, Addepto is a competitive option.
Related comparisons
Provectus vs Addepto FAQ
Is Provectus better than Addepto?
Provectus (4.5/5) scores higher overall, but "better" depends on your use case. Provectus is better for mid-market companies that need cloud data infrastructure and ML model development handled by the same integrator.. 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..
How do Provectus and Addepto differ in pricing?
Provectus uses not published; project and dedicated team pricing with a minimum engagement of Not published. Addepto uses project-based pricing with a minimum engagement of $10,000. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Provectus or Addepto?
Provectus 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 Provectus and Addepto?
Provectus's primary differentiator is: grew out of cloud and big-data engineering roots, giving it particular strength in the data infrastructure layer underneath ml models, not just the models themselves.. 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.. They also differ in team size (501–1,000 vs 51–200), minimum engagement (Not published vs $10,000), and primary industries served (Cross-industry mid-market, Healthcare vs Finance, Healthcare).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.