InData Labs vs Cleveroad: full comparison for 2026
Quick verdict
InData Labs (4.1/5) edges ahead of Cleveroad (4.0/5) overall. InData Labs is the better choice for teams needing data science depth before an AI product build. Cleveroad is the stronger option for startups needing AI features inside a mobile or web product. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs Cleveroad: head-to-head summary
| Criterion | InData Labs | Cleveroad |
|---|---|---|
| Founded | 2014 | 2011 |
| HQ | Limassol, Cyprus | Krakow, Poland |
| Team size | 51-200 | 113-200 |
| Rating | 4.1 / 5 | 4.0 / 5 |
| Primary differentiator | Data-science-first practice rather than a generative-AI-branded service line | Production-deployment discipline carried over from a decade of mobile and web delivery |
| Pricing model | Fixed project or dedicated team | Fixed project or dedicated team |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, scikit-learn, TensorFlow | Python, React Native, AWS |
| Industries served | Retail & e-commerce, Gaming, Fintech, Healthcare | Retail & e-commerce, Healthcare, Logistics |
InData Labs vs Cleveroad: overview
InData Labs
InData Labs was founded in 2014 by gaming-industry veteran Marat Karpeko and is headquartered in Cyprus, with additional offices reported in Lithuania and the US. Employee figures vary from roughly 65 to 200 across different trackers, which is common for firms that mix core staff with project-based contractors. The company's practice centers on data science consulting: predictive analytics, natural language processing, computer vision, and big data analytics, positioned as a data-first alternative to firms that lead with generative AI branding.
Cleveroad
Cleveroad was founded in 2011, and public sources disagree on headquarters, with LinkedIn listing Claymont, Delaware and other trackers pointing to Krakow, Poland as the operational base. Employee estimates likewise vary, from roughly 113 up to a LinkedIn-reported 201-500 range. The company's roots are in mobile and web development for startups and enterprise clients, with safe, production-grade AI deployment positioned as a newer strength built on top of that existing delivery discipline.
Services and capabilities: InData Labs vs Cleveroad
| Capability | InData Labs | Cleveroad |
|---|---|---|
| Generative AI | ✗ | ✓ |
| Machine learning | ✓ | ✓ |
| AI agents | ✗ | ✗ |
| MLOps | ✗ | ✗ |
| AI consulting | ✗ | ✗ |
| Fixed-price projects | ✓ | ✓ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: InData Labs vs Cleveroad
| Framework / platform | InData Labs | Cleveroad |
|---|---|---|
| Python | ✓ | ✓ |
| PyTorch | N/A | N/A |
| TensorFlow | ✓ | ✓ |
| LangChain | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: InData Labs vs Cleveroad
| Criterion | InData Labs | Cleveroad |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Fixed project, Dedicated team | Fixed project, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: InData Labs vs Cleveroad
| Dimension | InData Labs | Cleveroad |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Retail & e-commerce, Gaming, Fintech | Retail & e-commerce, Healthcare, Logistics |
| Best use cases | Building predictive models from an existing data warehouse or event stream., Adding computer vision to a product that already generates image or video data. | Adding AI features to a mobile app already in production., Getting a startup MVP built with AI as one feature among several, not the entire product. |
| Typical project type | Fixed project | Fixed project |
InData Labs vs Cleveroad: pros and cons
| InData Labs | |
|---|---|
| + | Founder's gaming-industry background brings real-time data experience to computer vision work. |
| + | EU-based headquarters (Cyprus) can simplify GDPR-aligned data handling for European clients. |
| + | Predictive analytics and NLP depth predate the generative AI hype cycle. |
| + | Decade-plus track record in a narrower, more defensible specialty than broad AI consulting. |
| - | Reported team size varies close to 3x across public sources |
| - | Less public-facing generative AI and LLM case work than firms built around that specifically |
| Cleveroad | |
|---|---|
| + | Mobile and web development roots translate into disciplined production deployment practices. |
| + | Over a decade of delivery history across startup and enterprise clients. |
| + | Operates across four continents, giving flexible timezone coverage. |
| + | AI positioned as an addition to, not a replacement for, established product delivery skills. |
| - | Headquarters and employee count are reported inconsistently across public sources |
| - | AI-specific case studies are less prominent than the firm's mobile and web development portfolio |
Who should choose InData Labs?
A typical fit: building predictive models from an existing data warehouse or event stream.
Data-science-first practice rather than a generative-AI-branded service line. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, Gaming, Fintech, Healthcare.
Who should choose Cleveroad?
A typical fit: adding AI features to a mobile app already in production.
Production-deployment discipline carried over from a decade of mobile and web delivery. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, Healthcare, Logistics.
Decision matrix: InData Labs vs Cleveroad
| 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 | InData Labs |
| Your budget is at the lower end | Compare: InData Labs (Not disclosed) vs Cleveroad (Not disclosed) |
| You need specialist depth in a specific vertical | InData Labs |
| 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: InData Labs vs Cleveroad
| Use case | InData Labs fit | Cleveroad fit | Winner |
|---|---|---|---|
| Building predictive models from an existing data warehouse or event stream. | Strong | Limited | InData Labs |
| Adding computer vision to a product that already generates image or video data. | Strong | Strong | Both equally |
| Adding AI features to a mobile app already in production. | Strong | Strong | Both equally |
| Getting a startup MVP built with AI as one feature among several, not the entire product. | Limited | Strong | Cleveroad |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: InData Labs vs Cleveroad
InData Labs (4.1/5) is the stronger overall choice for most AI Development projects. Data-science-first practice rather than a generative-AI-branded service line.
Cleveroad (4.0/5) is worth a look if you need getting a startup MVP built with AI as one feature among several, not the entire product. If your situation matches that, Cleveroad is a competitive option.
Related comparisons
InData Labs vs Cleveroad FAQ
Is InData Labs better than Cleveroad?
InData Labs (4.1/5) scores higher overall, but "better" depends on your use case. InData Labs's strongest advantage: Founder's gaming-industry background brings real-time data experience to computer vision work. Cleveroad's strongest advantage: mobile and web development roots translate into disciplined production deployment practices.
How do InData Labs and Cleveroad differ in pricing?
InData Labs uses fixed project or dedicated team pricing. Cleveroad uses fixed project or dedicated team pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: InData Labs or Cleveroad?
Cleveroad 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 Cleveroad?
InData Labs's primary differentiator is: data-science-first practice rather than a generative-AI-branded service line. Cleveroad's primary differentiator is: production-deployment discipline carried over from a decade of mobile and web delivery. They also differ in team size (51-200 vs 113-200), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Retail & e-commerce, Gaming vs Retail & e-commerce, Healthcare).
Verify all details directly with each company before making a decision.