Grape Up vs Deviniti: full comparison for 2026
Last updated: July 2026
Quick verdict
Grape Up (4.0/5) edges ahead of Deviniti (4.0/5) overall. Grape Up is the better choice for automotive and finance enterprises wanting agentic AI and generative-AI-powered legacy modernization delivered by an experienced cloud-native engineering firm.. Deviniti is the stronger option for enterprises in regulated or complex sectors wanting generative AI, RAG, and LLM work delivered by a vendor with deep enterprise-software (Atlassian ecosystem) roots.. The right choice depends on your project size, budget, and required tech stack.
Grape Up vs Deviniti: head-to-head summary
| Criterion | Grape Up | Deviniti |
|---|---|---|
| Founded | 2006 | 2004 |
| HQ | Kraków, Poland | Wrocław, Poland |
| Team size | Not disclosed | 300+ |
| Rating | 4.0 / 5 | 4.0 / 5 |
| Best for | Automotive and finance enterprises wanting agentic AI and generative-AI-powered legacy modernization delivered by an experienced cloud-native engineering firm. | Enterprises in regulated or complex sectors wanting generative AI, RAG, and LLM work delivered by a vendor with deep enterprise-software (Atlassian ecosystem) roots. |
| Pricing model | Fixed project, dedicated team | Fixed project, staff augmentation |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Kubernetes, Cloud-native platforms | Python, LLM fine-tuning tooling, RAG architectures |
| Industries served | Automotive, Financial Services, Manufacturing, Aviation | Financial Institutions, Regulated enterprise IT |
Grape Up vs Deviniti: overview
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.
Deviniti
Deviniti is a Wrocław, Poland software house founded in 2004, with 300+ specialists serving over 15,000 clients across 38 countries (per company website). It holds 50+ Atlassian-certified professionals and was a 2024–2025 Atlassian Partner of the Year finalist for Emerging Markets, and has more recently built out generative AI, custom AI agent, self-hosted LLM, LLM fine-tuning, and RAG architecture capabilities, including contributions to the open-source Bielik.AI project.
Services and capabilities: Grape Up vs Deviniti
| Capability | Grape Up | Deviniti |
|---|---|---|
| ML Development | ✗ | ✓ |
| AI Consulting | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI | ✓ | ✓ |
| MLOps | ✓ | ✗ |
| Data Engineering | ✗ | ✗ |
| Staff Augmentation | ✗ | ✓ |
Tech stack comparison: Grape Up vs Deviniti
| Framework / platform | Grape Up | Deviniti |
|---|---|---|
| Python | ✓ | ✓ |
| AWS | N/A | N/A |
| Microsoft Azure | N/A | N/A |
| Google Cloud | N/A | N/A |
| Kubernetes | ✓ | N/A |
| PyTorch | N/A | N/A |
| LangChain | N/A | N/A |
| Databricks | N/A | N/A |
Pricing comparison: Grape Up vs Deviniti
| Criterion | Grape Up | Deviniti |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Fixed project, Dedicated team | Fixed project, Staff augmentation, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Enterprise / mid-market | Enterprise / mid-market |
Target audience comparison: Grape Up vs Deviniti
| Dimension | Grape Up | Deviniti |
|---|---|---|
| Best company size | Startup to mid-market | Mid-market to enterprise |
| Best industries | Automotive, Financial Services, Manufacturing | Financial Institutions, Regulated enterprise IT |
| Best use cases | Agentic AI workflow automation for enterprises, Generative-AI-powered legacy system modernization | Self-hosted LLM and RAG system development, AI chatbot and knowledge-base solutions for enterprises |
| Typical project type | Fixed project | Fixed project |
Grape Up vs Deviniti: pros and cons
| 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 |
| Deviniti | |
|---|---|
| + | 300+ specialists and 15,000+ clients across 38 countries show significant delivery scale (per company website) |
| + | Contributions to the open-source Bielik.AI project demonstrate genuine LLM/NLP engineering, not just integration work |
| + | Deep Atlassian-ecosystem expertise is a strong complementary asset for enterprise clients running Jira/Confluence-based workflows |
| + | Founded 2004 — two decades of enterprise software delivery experience |
| - | Generative AI and RAG practice is newer than its core Atlassian and enterprise-software business, so ML-specific track record is shorter than the overall company history suggests |
| - | 300+ specialists are split across Atlassian consulting and AI/software delivery, so dedicated AI headcount is unclear |
| - | 15,000+ client claim is per company marketing and not independently broken down by service line |
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.
Who should choose Deviniti?
Deviniti is the right choice for enterprises in regulated or complex sectors wanting generative AI, RAG, and LLM work delivered by a vendor with deep enterprise-software (Atlassian ecosystem) roots..
50+ Atlassian-certified professionals and Atlassian Partner of the Year finalist status give it unusually strong enterprise-IT integration credibility alongside its generative AI practice and Bielik.AI open-source contributions.. Minimum engagement starts at Not published. Works best with clients in Financial Institutions, Regulated enterprise IT.
Decision matrix: Grape Up vs Deviniti
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Grape Up |
| You need a large dedicated team for an ongoing programme | Grape Up |
| Your budget is at the lower end | Compare: Grape Up (Not published) vs Deviniti (Not published) |
| You need specialist depth in a specific vertical | Grape Up |
| You need staff augmentation or team extension | Deviniti |
| You need consulting before committing to a build | Grape Up |
Use case fit: Grape Up vs Deviniti
| Use case | Grape Up fit | Deviniti fit | Winner |
|---|---|---|---|
| Agentic AI workflow automation for enterprises | Strong | Limited | Grape Up |
| Generative-AI-powered legacy system modernization | Strong | Limited | Grape Up |
| Self-hosted LLM and RAG system development | Limited | Strong | Deviniti |
| AI chatbot and knowledge-base solutions for enterprises | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Grape Up vs Deviniti
Grape Up (4.0/5) is the stronger overall choice for most Machine Learning Development projects. Built its own productized platforms (Databoostr, Cloudboostr) alongside custom delivery — a hybrid product-plus-services model less common among pure consultancies on this list.. It is best for automotive and finance enterprises wanting agentic AI and generative-AI-powered legacy modernization delivered by an experienced cloud-native engineering firm..
Deviniti (4.0/5) is the better choice when enterprises in regulated or complex sectors wanting generative AI, RAG, and LLM work delivered by a vendor with deep enterprise-software (Atlassian ecosystem) roots.. If your situation matches those criteria, Deviniti is a competitive option.
Related comparisons
Grape Up vs Deviniti FAQ
Is Grape Up better than Deviniti?
Grape Up (4.0/5) scores higher overall, but "better" depends on your use case. 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.. Deviniti is better for enterprises in regulated or complex sectors wanting generative AI, RAG, and LLM work delivered by a vendor with deep enterprise-software (Atlassian ecosystem) roots..
How do Grape Up and Deviniti differ in pricing?
Grape Up uses fixed project, dedicated team pricing with a minimum engagement of Not published. Deviniti uses fixed project, staff augmentation 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: Grape Up or Deviniti?
Deviniti 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 Grape Up and Deviniti?
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.. Deviniti's primary differentiator is: 50+ atlassian-certified professionals and atlassian partner of the year finalist status give it unusually strong enterprise-it integration credibility alongside its generative ai practice and bielik.ai open-source contributions.. They also differ in team size (Not disclosed vs 300+), minimum engagement (Not published vs Not published), and primary industries served (Automotive, Financial Services vs Financial Institutions, Regulated enterprise IT).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.