InData Labs vs Grape Up: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.4/5) edges ahead of Grape Up (4.0/5) overall. InData Labs is the better choice for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop.. 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.
InData Labs vs Grape Up: head-to-head summary
| Criterion | InData Labs | Grape Up |
|---|---|---|
| Founded | 2014 | 2006 |
| HQ | Nicosia, Cyprus (R&D and delivery centers in Lithuania and the US) | Kraków, Poland |
| Team size | 80+ | Not disclosed |
| Rating | 4.4 / 5 | 4.0 / 5 |
| Best for | Companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop. | 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 | Not published | Not published |
| Primary tech stack | Python, Generative AI/GPT tooling, Computer vision frameworks | Python, Kubernetes, Cloud-native platforms |
| Industries served | Cross-industry, Predictive Analytics | Automotive, Financial Services, Manufacturing, Aviation |
InData Labs vs Grape Up: overview
InData Labs
InData Labs is a data science and AI consultancy legally headquartered in Nicosia, Cyprus, founded in 2014 by video-gaming industry veteran Marat Karpeko, with R&D and delivery centers in Lithuania and the US. The 80+ person firm runs its own R&D center and covers a wide technical band from generative AI and GPT integration through predictive analytics, forecasting, and computer vision. Its Cyprus legal HQ gives clients an EU-entity contracting structure alongside nearshore delivery capacity.
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: InData Labs vs Grape Up
| Capability | InData Labs | Grape Up |
|---|---|---|
| ML Development | ✓ | ✗ |
| AI Consulting | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI | ✓ | ✓ |
| MLOps | ✗ | ✓ |
| Data Engineering | ✓ | ✗ |
| Staff Augmentation | ✗ | ✗ |
Tech stack comparison: InData Labs vs Grape Up
| Framework / platform | InData Labs | Grape Up |
|---|---|---|
| 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: InData Labs vs Grape Up
| Criterion | InData Labs | Grape Up |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Fixed project, Time & Materials | Fixed project, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Enterprise / mid-market | Enterprise / mid-market |
Target audience comparison: InData Labs vs Grape Up
| Dimension | InData Labs | Grape Up |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Cross-industry, Predictive Analytics | Automotive, Financial Services, Manufacturing |
| Best use cases | Generative AI and GPT integration projects, Predictive analytics and forecasting | Agentic AI workflow automation for enterprises, Generative-AI-powered legacy system modernization |
| Typical project type | Fixed project | Fixed project |
InData Labs vs Grape Up: pros and cons
| InData Labs | |
|---|---|
| + | Founded 2014 — one of the longer-running boutique data science firms in this list |
| + | In-house R&D center is a differentiator versus pure staff-augmentation vendors |
| + | Cyprus legal HQ with Lithuania/US delivery centers gives EU-entity contracting plus nearshore delivery |
| + | Broad technical range from generative AI to classic forecasting and computer vision |
| - | 80+ employee band is imprecise — exact current headcount is not independently published |
| - | Legal HQ (Cyprus) is a smaller AI hub than its Lithuania delivery center, which may matter to buyers wanting an on-the-ground presence |
| - | Pricing model and minimum engagement are not published |
| 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 InData Labs?
InData Labs is the right choice for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop..
Runs its own R&D center rather than purely project-based delivery, spanning generative AI/GPT integration through classic predictive analytics and computer vision.. Minimum engagement starts at Not published. Works best with clients in Cross-industry, Predictive Analytics.
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: InData Labs vs Grape Up
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | InData Labs |
| You need a large dedicated team for an ongoing programme | Grape Up |
| Your budget is at the lower end | Compare: InData Labs (Not published) vs Grape Up (Not published) |
| You need specialist depth in a specific vertical | Grape Up |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | InData Labs |
Use case fit: InData Labs vs Grape Up
| Use case | InData Labs fit | Grape Up fit | Winner |
|---|---|---|---|
| Generative AI and GPT integration projects | Strong | Strong | Both equally |
| Predictive analytics and forecasting | Strong | Limited | InData Labs |
| 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: InData Labs vs Grape Up
InData Labs (4.4/5) is the stronger overall choice for most Machine Learning Development projects. Runs its own R&D center rather than purely project-based delivery, spanning generative AI/GPT integration through classic predictive analytics and computer vision.. It is best for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop..
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
InData Labs vs Grape Up FAQ
Is InData Labs better than Grape Up?
InData Labs (4.4/5) scores higher overall, but "better" depends on your use case. InData Labs is better for companies wanting a decade-plus data science track record with in-house R&D rather than a pure project-delivery shop.. 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 InData Labs and Grape Up differ in pricing?
InData Labs uses fixed project, time & materials pricing with a minimum engagement of Not published. 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: InData Labs or Grape Up?
InData Labs 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 InData Labs and Grape Up?
InData Labs's primary differentiator is: runs its own r&d center rather than purely project-based delivery, spanning generative ai/gpt integration through classic predictive analytics and computer vision.. 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 (80+ vs Not disclosed), minimum engagement (Not published vs Not published), and primary industries served (Cross-industry, Predictive Analytics vs Automotive, Financial Services).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.