Addepto vs Iguazio: full comparison for 2026
Quick verdict
Addepto (3.9/5) edges ahead of Iguazio (3.5/5) overall. Addepto is the better choice for Manufacturing, logistics, retail SMEs — focused boutique, senior access. Iguazio is the stronger option for enterprises needing production-grade MLOps, real-time serving. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Iguazio: head-to-head summary
| Criterion | Addepto | Iguazio |
|---|---|---|
| Founded | 2017 | 2014 |
| HQ | Warsaw, Poland | Herzliya, Israel |
| Team size | 50–100 | 70+ |
| Rating | 3.9 / 5 | 3.5 / 5 |
| Primary differentiator | Focused vertical expertise in manufacturing predictive maintenance and retail AI at boutique scale — avoids the generalist overhead of larger firms for targeted use cases | MLOps platform specialist with real-time AI serving and multi-cloud/edge deployment — best for operationalising models rather than building them |
| Pricing model | Fixed project, T&M | Fixed project, Retainer |
| Min. engagement | $15K | $100K |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, MLflow, Kubernetes |
| Industries served | Manufacturing, Retail / E-commerce, Financial Services, Logistics | Financial Services, Healthcare, Technology / SaaS, Retail / E-commerce |
Addepto vs Iguazio: overview
Addepto
Addepto is a machine learning and AI consultancy established in 2017 and headquartered in Warsaw, Poland, with approximately 52 employees. Despite its small size, Addepto has built a focused portfolio in manufacturing predictive maintenance, logistics AI, and retail recommendation engines, delivering scalable ML solutions that align with the specific data patterns and operational constraints of each vertical. The firm's notable projects include predictive maintenance implementations for manufacturing clients, logistics optimisation using AI-driven analysis, and recommendation engines for retail. Addepto is one of the more accessible boutiques by team size and minimum engagement, suitable for companies requiring a specialised ML partner without enterprise-level overhead.
Iguazio
Iguazio was founded in 2014 and is headquartered in Herzliya, Israel, with a team of 70+ professionals. In January 2023, Iguazio was acquired by McKinsey & Company, marking a significant ownership change that buyers should factor into vendor selection. The company's Data Science and MLOps Platform enables enterprises to develop, deploy, and manage AI applications at scale, in real time, across multi-cloud, on-premises, and edge environments. Iguazio's consulting and ML development services are platform-native — clients typically engage Iguazio to deploy and operationalise ML models on its infrastructure rather than to design novel model architectures from scratch. (Per company website; independently unverifiable post-acquisition service scope details.)
Services and capabilities: Addepto vs Iguazio
| Capability | Addepto | Iguazio |
|---|---|---|
| Custom ML development | ✓ | ✓ |
| Deep learning | ✗ | ✗ |
| NLP / Text analytics | ✓ | ✗ |
| Computer vision | ✓ | ✗ |
| MLOps & deployment | ✓ | ✓ |
| Generative AI | ✗ | ✗ |
| AI strategy | ✗ | ✓ |
| Staff augmentation | ✗ | ✗ |
| Fixed-price projects | ✓ | ✓ |
| Dedicated team model | ✗ | ✗ |
Tech stack comparison: Addepto vs Iguazio
| Framework / platform | Addepto | Iguazio |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Kubernetes | ✓ | ✓ |
| Databricks | N/A | N/A |
| MLflow | ✓ | ✓ |
Pricing comparison: Addepto vs Iguazio
| Criterion | Addepto | Iguazio |
|---|---|---|
| Minimum engagement | $15K | $100K |
| Engagement models | Fixed project, Time & materials | Fixed project, Retainer |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Addepto vs Iguazio
| Dimension | Addepto | Iguazio |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Retail / E-commerce, Financial Services | Financial Services, Healthcare, Technology / SaaS |
| Best use cases | Predictive maintenance ML for manufacturing equipment with IoT sensor data integration, Recommendation engine development for e-commerce and retail personalisation platforms | Production ML model deployment and real-time serving infrastructure for financial services AI applications, MLOps platform implementation for enterprises moving multiple models from experimentation to production simultaneously |
| Typical project type | Fixed project | Fixed project |
Addepto vs Iguazio: pros and cons
| Addepto | |
|---|---|
| + | Focused manufacturing and retail portfolio reduces onboarding time on predictive maintenance and recommendation system projects |
| + | Small team ensures senior practitioner involvement throughout the engagement rather than junior staffing after kickoff |
| + | Competitive Warsaw-based rates are well below US boutiques of equivalent vertical ML depth |
| + | Accessible $15K minimum allows SMEs to engage professional ML delivery without enterprise investment levels |
| - | Team of ~52 strictly limits concurrent capacity — unsuitable for clients needing multiple simultaneous ML tracks |
| - | Founded 2017 — shorter track record than established competitors for high-stakes procurement decisions |
| - | Narrow vertical focus means less applicable experience for clients in healthcare, financial services, or media |
| - | Less infrastructure in generative AI, agentic systems, or large-scale MLOps compared to larger firms |
| Iguazio | |
|---|---|
| + | Purpose-built MLOps platform handles real-time AI serving at scale — stronger than generalist cloud MLOps for low-latency use cases |
| + | Multi-environment deployment (multi-cloud, on-prem, edge) in a single platform reduces MLOps infrastructure complexity |
| + | McKinsey acquisition provides access to broader strategic consulting resources alongside platform delivery |
| - | Acquired by McKinsey in January 2023 — consulting independence and platform road map priorities may shift toward McKinsey client interests; disclose in procurement evaluation |
| - | Small 70+ team creates capacity limits for large simultaneous ML development engagements beyond platform deployment |
| - | Platform-native delivery model is less suited to bespoke custom ML development than to MLOps operationalisation of existing models |
| - | Vendor lock-in risk is heightened given McKinsey acquisition — exit strategy from Iguazio platform should be documented before committing |
Who should choose Addepto?
A typical fit: predictive maintenance ML for manufacturing equipment with IoT sensor data integration.
Focused vertical expertise in manufacturing predictive maintenance and retail AI at boutique scale — avoids the generalist overhead of larger firms for targeted use cases. Minimum engagement starts at $15K. Works best with clients in Manufacturing, Retail / E-commerce, Financial Services, Logistics.
Who should choose Iguazio?
A typical fit: production ML model deployment and real-time serving infrastructure for financial services AI applications.
MLOps platform specialist with real-time AI serving and multi-cloud/edge deployment — best for operationalising models rather than building them. Minimum engagement starts at $100K. Works best with clients in Financial Services, Healthcare, Technology / SaaS, Retail / E-commerce.
Decision matrix: Addepto vs Iguazio
| 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 | Addepto |
| You need specialist depth in a specific vertical | Addepto |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: Addepto vs Iguazio
| Use case | Addepto fit | Iguazio fit | Winner |
|---|---|---|---|
| Predictive maintenance ML for manufacturing equipment with IoT sensor data integration | Strong | Limited | Addepto |
| Recommendation engine development for e-commerce and retail personalisation platforms | Strong | Limited | Addepto |
| Production ML model deployment and real-time serving infrastructure for financial services AI applications | Strong | Strong | Both equally |
| MLOps platform implementation for enterprises moving multiple models from experimentation to production simultaneously | Limited | Strong | Iguazio |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Addepto vs Iguazio
Addepto (3.9/5) is the stronger overall choice for most Machine Learning projects. Focused vertical expertise in manufacturing predictive maintenance and retail AI at boutique scale — avoids the generalist overhead of larger firms for targeted use cases.
Iguazio (3.5/5) is worth a look if you need MLOps platform implementation for enterprises moving multiple models from experimentation to production simultaneously. If your situation matches that, Iguazio is a competitive option.
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Addepto vs Iguazio FAQ
Is Addepto better than Iguazio?
Addepto (3.9/5) scores higher overall, but "better" depends on your use case. Addepto's strongest advantage: focused manufacturing and retail portfolio reduces onboarding time on predictive maintenance and recommendation system projects. Iguazio's strongest advantage: purpose-built MLOps platform handles real-time AI serving at scale — stronger than generalist cloud MLOps for low-latency use cases.
How do Addepto and Iguazio differ in pricing?
Addepto uses fixed project, t&m pricing with a minimum engagement of $15K. Iguazio uses fixed project, retainer pricing with a minimum engagement of $100K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Addepto or Iguazio?
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 agency before shortlisting.
What are the main differences between Addepto and Iguazio?
Addepto's primary differentiator is: focused vertical expertise in manufacturing predictive maintenance and retail AI at boutique scale — avoids the generalist overhead of larger firms for targeted use cases. Iguazio's primary differentiator is: MLOps platform specialist with real-time AI serving and multi-cloud/edge deployment — best for operationalising models rather than building them. They also differ in team size (50–100 vs 70+), minimum engagement ($15K vs $100K), and primary industries served (Manufacturing, Retail / E-commerce vs Financial Services, Healthcare).