InData Labs vs Persistent Systems: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.5/5) edges ahead of Persistent Systems (3.8/5) overall. InData Labs is the better choice for fintech, healthcare, and SaaS companies wanting a specialist data-science boutique rather than a generalist software vendor.. Persistent Systems is the stronger option for very large enterprises that want AI/ML delivered by the same vendor already running their broader IT estate.. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs Persistent Systems: head-to-head summary
| Criterion | InData Labs | Persistent Systems |
|---|---|---|
| Founded | 2014 | 1990 |
| HQ | Nicosia, Cyprus | Pune, India |
| Team size | 51–200 | 10,000+ |
| Rating | 4.5 / 5 | 3.8 / 5 |
| Best for | Fintech, healthcare, and SaaS companies wanting a specialist data-science boutique rather than a generalist software vendor. | Very large enterprises that want AI/ML delivered by the same vendor already running their broader IT estate. |
| Pricing model | Fixed project and Time & Material | Managed services and fixed project |
| Min. engagement | $20K | Not published |
| Primary tech stack | Python, Scikit-learn, TensorFlow | Python, Azure OpenAI, AWS |
| Industries served | FinTech, Healthcare, Technology/SaaS, Retail, Logistics | Financial Services, Healthcare, Technology/SaaS, Government |
InData Labs vs Persistent Systems: overview
InData Labs
InData Labs is a data science and AI consultancy founded in 2014 by Marat Karpeko, headquartered in Nicosia, Cyprus, with additional offices in Lithuania and the US. The 80+ person firm (per company website) runs its own R&D center and focuses on production AI systems for fintech, healthcare, SaaS, retail, and logistics clients.
Persistent Systems
Persistent Systems is an Indian multinational technology company founded in 1990 by Anand Deshpande, headquartered in Pune, with roughly 24,600 employees as of March 2025. Its AI/ML offerings, including the Persistent GenAI Hub, sit within a much larger portfolio spanning enterprise software, cloud, and digital engineering services rather than being the company's core specialization.
Services and capabilities: InData Labs vs Persistent Systems
| Capability | InData Labs | Persistent Systems |
|---|---|---|
| Custom ML model development | ✓ | ✓ |
| Deep learning & computer vision | ✗ | ✗ |
| NLP & LLM / Generative AI | ✗ | ✗ |
| MLOps & production deployment | ✗ | ✗ |
| Data engineering | ✓ | ✓ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✗ | ✓ |
Tech stack comparison: InData Labs vs Persistent Systems
| Framework / platform | InData Labs | Persistent Systems |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | N/A |
| Kubernetes | N/A | N/A |
| Databricks | N/A | N/A |
| LangChain | N/A | N/A |
Pricing comparison: InData Labs vs Persistent Systems
| Criterion | InData Labs | Persistent Systems |
|---|---|---|
| Minimum engagement | $20K | Not published |
| Engagement models | Fixed project, Time & Material | Managed services, Fixed project, Staff augmentation |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Enterprise / not published |
Target audience comparison: InData Labs vs Persistent Systems
| Dimension | InData Labs | Persistent Systems |
|---|---|---|
| Best company size | Startup to mid-market | Enterprise |
| Best industries | FinTech, Healthcare, Technology/SaaS | Financial Services, Healthcare, Technology/SaaS |
| Best use cases | Building a fintech risk-scoring or fraud model with a specialist data-science team, Standing up a healthcare predictive-analytics pilot with a boutique partner | Enterprises already using Persistent for core IT services wanting to add AI/ML from the same vendor, Very large, multi-year digital transformation programs where AI is one workstream among many |
| Typical project type | Fixed project | Managed services |
InData Labs vs Persistent Systems: pros and cons
| InData Labs | |
|---|---|
| + | Founder brought data-analytics experience from the gaming industry, an unusually data-intensive prior domain |
| + | Multi-country footprint (Cyprus, Lithuania, US) without the very large headcount of enterprise IT firms |
| + | 10+ years of focused data science practice rather than a recent AI pivot from generalist dev work |
| + | Named vertical focus (FinTech, Healthcare, Logistics) supports domain-specific model design |
| - | 80-person team limits capacity for very large multi-year enterprise programs |
| - | Less brand recognition in North America than US-headquartered competitors |
| - | Public case studies rarely disclose named enterprise clients |
| Persistent Systems | |
|---|---|
| + | 35 years of operating history and one of the largest headcounts on this list (24,000+) |
| + | AI capability delivered alongside a company's existing broader IT services relationship, reducing vendor sprawl |
| + | 16,000+ AI-trained staff cited internally, suggesting significant AI upskilling investment (per company website) |
| + | Public-company scale supports very large, multi-year enterprise transformation programs |
| - | AI/ML is one offering within a much larger, more generalist IT services portfolio rather than the firm's core focus |
| - | Buyers seeking cutting-edge ML specialization may find deeper expertise at AI-first boutiques on this list |
| - | Very large organization can mean slower response times and more layered account management than smaller firms |
Who should choose InData Labs?
InData Labs is the right choice for fintech, healthcare, and SaaS companies wanting a specialist data-science boutique rather than a generalist software vendor..
Dedicated in-house R&D center focused specifically on data science and AI rather than broad software outsourcing.. Minimum engagement starts at $20K. Works best with clients in FinTech, Healthcare, Technology/SaaS, Retail, Logistics.
Who should choose Persistent Systems?
Persistent Systems is the right choice for very large enterprises that want AI/ML delivered by the same vendor already running their broader IT estate..
Enterprise-wide scale (24,000+ employees) supporting AI/ML as part of a full IT services portfolio, not a standalone specialty.. Minimum engagement starts at Not published. Works best with clients in Financial Services, Healthcare, Technology/SaaS, Government.
Decision matrix: InData Labs vs Persistent Systems
| 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 | Check each company's engagement model |
| Your budget is at the lower end | Compare: InData Labs ($20K) vs Persistent Systems (Not published) |
| You need specialist depth in a specific vertical | InData Labs |
| You need staff augmentation or team extension | Persistent Systems |
| You need consulting before committing to a build | InData Labs |
Use case fit: InData Labs vs Persistent Systems
| Use case | InData Labs fit | Persistent Systems fit | Winner |
|---|---|---|---|
| Building a fintech risk-scoring or fraud model with a specialist data-science team | Strong | Limited | InData Labs |
| Standing up a healthcare predictive-analytics pilot with a boutique partner | Strong | Limited | InData Labs |
| Enterprises already using Persistent for core IT services wanting to add AI/ML from the same vendor | Limited | Strong | Persistent Systems |
| Very large, multi-year digital transformation programs where AI is one workstream among many | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: InData Labs vs Persistent Systems
InData Labs (4.5/5) is the stronger overall choice for most Machine Learning Development projects. Dedicated in-house R&D center focused specifically on data science and AI rather than broad software outsourcing.. It is best for fintech, healthcare, and SaaS companies wanting a specialist data-science boutique rather than a generalist software vendor..
Persistent Systems (3.8/5) is the better choice when very large enterprises that want AI/ML delivered by the same vendor already running their broader IT estate.. If your situation matches those criteria, Persistent Systems is a competitive option.
Related comparisons
InData Labs vs Persistent Systems FAQ
Is InData Labs better than Persistent Systems?
InData Labs (4.5/5) scores higher overall, but "better" depends on your use case. InData Labs is better for fintech, healthcare, and SaaS companies wanting a specialist data-science boutique rather than a generalist software vendor.. Persistent Systems is better for very large enterprises that want AI/ML delivered by the same vendor already running their broader IT estate..
How do InData Labs and Persistent Systems differ in pricing?
InData Labs uses fixed project and time & material pricing with a minimum engagement of $20K. Persistent Systems uses managed services and fixed project 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 Persistent Systems?
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 agency before shortlisting.
What are the main differences between InData Labs and Persistent Systems?
InData Labs's primary differentiator is: dedicated in-house r&d center focused specifically on data science and ai rather than broad software outsourcing.. Persistent Systems's primary differentiator is: enterprise-wide scale (24,000+ employees) supporting ai/ml as part of a full it services portfolio, not a standalone specialty.. They also differ in team size (51–200 vs 10,000+), minimum engagement ($20K vs Not published), and primary industries served (FinTech, Healthcare vs Financial Services, Healthcare).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.