Addepto vs Grape Up: full comparison for 2026
Last updated: July 2026
Quick verdict
Addepto (4.4/5) edges ahead of Grape Up (4.0/5) overall. Addepto is the better choice for mid-market to enterprise buyers in aviation, logistics, or finance that already have a proof-of-concept and need it hardened into a production MLOps pipeline.. Grape Up is the stronger option for automotive and finance enterprises wanting agentic AI and generative-AI-powered legacy modernization delivered by an experienced cloud-native engineering firm.. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Grape Up: head-to-head summary
| Criterion | Addepto | Grape Up |
|---|---|---|
| Founded | 2017 | 2006 |
| HQ | Warsaw, Poland | Kraków, Poland |
| Team size | 50–249 | Not disclosed |
| Rating | 4.4 / 5 | 4.0 / 5 |
| Best for | Mid-market to enterprise buyers in aviation, logistics, or finance that already have a proof-of-concept and need it hardened into a production MLOps pipeline. | Automotive and finance enterprises wanting agentic AI and generative-AI-powered legacy modernization delivered by an experienced cloud-native engineering firm. |
| Pricing model | Fixed project, Time & Materials | Fixed project, dedicated team |
| Min. engagement | $10,000+ | Not published |
| Primary tech stack | Python, MLOps pipelines, AWS | Python, Kubernetes, Cloud-native platforms |
| Industries served | Aviation, Manufacturing, Automotive, Financial Services, Retail/E-commerce, Logistics | Automotive, Financial Services, Manufacturing, Aviation |
Addepto vs Grape Up: overview
Addepto
Addepto is a Warsaw, Poland AI consultancy founded in 2017 that explicitly positions its value around production-grade delivery — moving clients from proof-of-concept to production — rather than research exploration. It covers AI consulting, generative AI development, data engineering, MLOps, document processing, and computer vision, serving aviation, manufacturing, automotive, finance, retail, healthcare, and logistics clients. Addepto is a GoodFirms top-rated firm for Big Data and Business Intelligence services, with a 50–249 employee band per Clutch.
Grape Up
Grape Up is a Kraków, Poland AI and cloud-native engineering firm founded in 2006, delivering agentic AI, generative-AI-powered legacy modernization, and advanced analytics alongside its own productized platforms: Databoostr for data sharing and monetization, and Cloudboostr, a Kubernetes stack for cloud deployment. Named clients include Porsche, Nissan, Mazda, Ducati, BNP, and Allstate (per company website), concentrated in automotive, finance, manufacturing, and aviation.
Services and capabilities: Addepto vs Grape Up
| Capability | Addepto | Grape Up |
|---|---|---|
| ML Development | ✓ | ✗ |
| AI Consulting | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI | ✗ | ✓ |
| MLOps | ✓ | ✓ |
| Data Engineering | ✓ | ✗ |
| Staff Augmentation | ✗ | ✗ |
Tech stack comparison: Addepto vs Grape Up
| Framework / platform | Addepto | Grape Up |
|---|---|---|
| Python | ✓ | ✓ |
| AWS | ✓ | N/A |
| Microsoft Azure | N/A | N/A |
| Google Cloud | ✓ | N/A |
| Kubernetes | N/A | ✓ |
| PyTorch | N/A | N/A |
| LangChain | N/A | N/A |
| Databricks | N/A | N/A |
Pricing comparison: Addepto vs Grape Up
| Criterion | Addepto | Grape Up |
|---|---|---|
| Minimum engagement | $10,000+ | Not published |
| Engagement models | Fixed project, Time & Materials, Dedicated team | Fixed project, Dedicated team |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Enterprise / mid-market |
Target audience comparison: Addepto vs Grape Up
| Dimension | Addepto | Grape Up |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Aviation, Manufacturing, Automotive | Automotive, Financial Services, Manufacturing |
| Best use cases | Computer vision for document processing, MLOps pipeline hardening for existing proof-of-concepts | Agentic AI workflow automation for enterprises, Generative-AI-powered legacy system modernization |
| Typical project type | Fixed project | Fixed project |
Addepto vs Grape Up: pros and cons
| Addepto | |
|---|---|
| + | Broad industry coverage from aviation to legal shows delivery flexibility beyond a single vertical |
| + | Explicit MLOps and production focus addresses the common 'stuck in proof-of-concept' failure mode |
| + | $10K entry point is accessible for a mid-market pilot engagement |
| + | GoodFirms top-rated recognition for Big Data and Business Intelligence services |
| - | Broad industry spread can mean less depth in any single regulated vertical than a specialist boutique |
| - | Exact team size within the 50–249 Clutch band is not broken out by function |
| - | Public case studies are largely testimonial-based rather than published with hard metrics |
| Grape Up | |
|---|---|
| + | Notable automotive and finance client roster (Porsche, Nissan, Mazda, Ducati, BNP, Allstate) per company website |
| + | Own productized platforms (Databoostr, Cloudboostr) show deeper platform-engineering capability than pure staffing vendors |
| + | Founded 2006 — nearly two decades of continuous Kraków-based delivery |
| + | Agentic AI and GenAI-powered legacy modernization address a current enterprise pain point directly |
| - | Team size and detailed employee count are not publicly disclosed |
| - | Cloud-native and Kubernetes engineering roots mean AI/ML depth may be shallower than pure-play ML boutiques |
| - | Public case studies emphasize client logos over specific project outcomes and metrics |
Who should choose Addepto?
Addepto is the right choice for mid-market to enterprise buyers in aviation, logistics, or finance that already have a proof-of-concept and need it hardened into a production MLOps pipeline..
Explicit 'proof-of-concept to production' positioning addresses the common failure mode where enterprise ML pilots never reach deployment.. Minimum engagement starts at $10,000+. Works best with clients in Aviation, Manufacturing, Automotive, Financial Services, Retail/E-commerce, Logistics.
Who should choose Grape Up?
Grape Up is the right choice for automotive and finance enterprises wanting agentic AI and generative-AI-powered legacy modernization delivered by an experienced cloud-native engineering firm..
Built its own productized platforms (Databoostr, Cloudboostr) alongside custom delivery — a hybrid product-plus-services model less common among pure consultancies on this list.. Minimum engagement starts at Not published. Works best with clients in Automotive, Financial Services, Manufacturing, Aviation.
Decision matrix: Addepto vs Grape Up
| 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 | Addepto |
| Your budget is at the lower end | Compare: Addepto ($10,000+) vs Grape Up (Not published) |
| 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 | Addepto |
Use case fit: Addepto vs Grape Up
| Use case | Addepto fit | Grape Up fit | Winner |
|---|---|---|---|
| Computer vision for document processing | Strong | Limited | Addepto |
| MLOps pipeline hardening for existing proof-of-concepts | Strong | Limited | Addepto |
| Agentic AI workflow automation for enterprises | Limited | Strong | Grape Up |
| Generative-AI-powered legacy system modernization | Limited | Strong | Grape Up |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Addepto vs Grape Up
Addepto (4.4/5) is the stronger overall choice for most Machine Learning Development projects. Explicit 'proof-of-concept to production' positioning addresses the common failure mode where enterprise ML pilots never reach deployment.. It is best for mid-market to enterprise buyers in aviation, logistics, or finance that already have a proof-of-concept and need it hardened into a production MLOps pipeline..
Grape Up (4.0/5) is the better choice when automotive and finance enterprises wanting agentic AI and generative-AI-powered legacy modernization delivered by an experienced cloud-native engineering firm.. If your situation matches those criteria, Grape Up is a competitive option.
Related comparisons
Addepto vs Grape Up FAQ
Is Addepto better than Grape Up?
Addepto (4.4/5) scores higher overall, but "better" depends on your use case. Addepto is better for mid-market to enterprise buyers in aviation, logistics, or finance that already have a proof-of-concept and need it hardened into a production MLOps pipeline.. Grape Up is better for automotive and finance enterprises wanting agentic AI and generative-AI-powered legacy modernization delivered by an experienced cloud-native engineering firm..
How do Addepto and Grape Up differ in pricing?
Addepto uses fixed project, time & materials pricing with a minimum engagement of $10,000+. Grape Up uses fixed project, dedicated team 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 Grape Up?
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 Grape Up?
Addepto's primary differentiator is: explicit 'proof-of-concept to production' positioning addresses the common failure mode where enterprise ml pilots never reach deployment.. Grape Up's primary differentiator is: built its own productized platforms (databoostr, cloudboostr) alongside custom delivery — a hybrid product-plus-services model less common among pure consultancies on this list.. They also differ in team size (50–249 vs Not disclosed), minimum engagement ($10,000+ vs Not published), and primary industries served (Aviation, Manufacturing vs Automotive, Financial Services).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.