InData Labs vs Persistent Systems: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.3/5) edges ahead of Persistent Systems (3.9/5) overall. InData Labs is the better choice for companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks.. Persistent Systems is the stronger option for mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and explainable-AI accelerators.. 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 (delivery center: Minsk, Belarus) | Pune, India |
| Team size | 51–200 | 10,000+ |
| Rating | 4.3 / 5 | 3.9 / 5 |
| Best for | Companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks. | Mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and explainable-AI accelerators. |
| Pricing model | Project-based | Not published; enterprise project engagements |
| Min. engagement | $25,000 | Not published |
| Primary tech stack | Python, Computer vision frameworks, NLP toolkits | AWS, Microsoft Azure, Google Cloud |
| Industries served | Transportation/logistics, Retail, Finance | Healthcare, Financial services, Technology/software, Life sciences |
InData Labs vs Persistent Systems: overview
InData Labs
InData Labs is a data science consultancy founded in 2014 by Marat Karpeko, with a registered headquarters in Nicosia, Cyprus, and its primary research and development center in Minsk, Belarus. The company focuses on predictive analytics, natural language processing, and computer vision, delivering custom AI model development for clients ranging from logistics to retail. Published case studies include a freight-rate prediction model for a transportation company and a dog-face-identification model reporting 91.96 percent accuracy, giving it more quantified, checkable outcome data than many peers of similar size.
Persistent Systems
Persistent Systems Limited was founded in 1990 in Pune, India, by Dr. Anand Deshpande, and has grown into a publicly traded (NSE/BSE: PERSISTENT) multinational technology services company with more than 24,000 employees. Its Data Science and Machine Learning practice spans data engineering through enterprise ML deployment across AWS, Azure, and Google Cloud, supported by its Data Experience Hub (DxH), a set of accelerators aimed at operationalizing ML and detecting bias in models through explainable AI. Persistent was named a Leader in the Everest Group Data & AI PEAK Matrix 2025 for the mid-market segment, and holds AWS Premier Tier Partner and Google Cloud Data & Analytics plus Machine Learning Specializations.
Services and capabilities: InData Labs vs Persistent Systems
| Capability | InData Labs | Persistent Systems |
|---|---|---|
| Custom model training | ✓ | ✓ |
| Fine-tuning & adaptation | ✗ | ✗ |
| MLOps pipeline | ✗ | ✓ |
| Model deployment & serving | ✗ | ✗ |
| Data engineering for ML | ✓ | ✓ |
| ML infrastructure management | ✗ | ✓ |
| Computer vision | ✓ | ✗ |
| NLP & LLM development | ✓ | ✗ |
| Forecasting & time-series modeling | ✗ | ✗ |
| ML strategy consulting | ✗ | ✗ |
Tech stack comparison: InData Labs vs Persistent Systems
| Framework / platform | InData Labs | Persistent Systems |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| MLflow | N/A | N/A |
| AWS SageMaker | N/A | N/A |
| Amazon Bedrock | N/A | N/A |
| Google Cloud | N/A | ✓ |
| Microsoft Azure | N/A | ✓ |
| Kubernetes | N/A | N/A |
| Snowflake | N/A | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: InData Labs vs Persistent Systems
| Criterion | InData Labs | Persistent Systems |
|---|---|---|
| Minimum engagement | $25,000 | Not published |
| Engagement models | Fixed project, Time & Material | Enterprise project engagement, Managed AI services |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: InData Labs vs Persistent Systems
| Dimension | InData Labs | Persistent Systems |
|---|---|---|
| Best company size | Startup to mid-market | Enterprise |
| Best industries | Transportation/logistics, Retail, Finance | Healthcare, Financial services, Technology/software |
| Best use cases | Building a predictive pricing or demand-forecasting model for logistics or transportation, Developing a computer-vision classification model with a documented accuracy target | Operationalizing ML models at enterprise scale using pre-built MLOps accelerators, Running bias detection and explainable AI reviews on existing production models |
| Typical project type | Fixed project | Enterprise project engagement |
InData Labs vs Persistent Systems: pros and cons
| InData Labs | |
|---|---|
| + | Case studies include specific, quantified model accuracy figures rather than vague outcome claims. |
| + | Featured among Clutch's broader provider directory with a positive review sentiment on delivery timeliness. |
| + | Focused specialization in predictive analytics and computer vision avoids service-line dilution. |
| + | Recognized in a 2016 "Top 100 Big Data" listing, indicating an established track record. |
| - | Team size figures are inconsistent across sources (roughly 50–80 depending on source), so exact headcount is uncertain. |
| - | Registered HQ (Cyprus) differs from the primary delivery center (Belarus), which some buyers may want clarified given regional considerations. |
| - | Public tech-stack disclosure is limited beyond high-level specialization areas. |
| - | Fewer large, brand-name enterprise clients named publicly compared to bigger peers. |
| Persistent Systems | |
|---|---|
| + | Everest Group Leader ranking in the Data & AI PEAK Matrix 2025 (mid-market segment) is an independently sourced third-party validation. |
| + | Purpose-built DxH accelerators for MLOps and bias detection add concrete, named tooling beyond generic claims. |
| + | Publicly traded with 35-year operating history, providing financial transparency. |
| + | Named healthcare client work (e.g., cancer-detection collaboration) with a specific, checkable use case. |
| - | Very large scale (24,000+ employees) means ML/AI is one of several major practice areas competing for delivery focus. |
| - | No clearly located aggregate Clutch/G2 star rating specific to its AI practice in available public sources. |
| - | Pricing model and minimum engagement are not published. |
| - | India-centric delivery model may require additional coordination for clients preferring more localized teams. |
Who should choose InData Labs?
InData Labs is the right choice for companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks..
Publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims.. Minimum engagement starts at $25,000. Works best with clients in Transportation/logistics, Retail, Finance.
Who should choose Persistent Systems?
Persistent Systems is the right choice for mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and explainable-AI accelerators..
Purpose-built DxH accelerator suite for MLOps and bias detection, plus a specific Everest Group Leader ranking in the mid-market Data & AI segment rather than only the largest enterprise tier.. Minimum engagement starts at Not published. Works best with clients in Healthcare, Financial services, Technology/software, Life sciences.
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 ($25,000) vs Persistent Systems (Not published) |
| You need specialist depth in a specific vertical | Persistent Systems |
| 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 Persistent Systems
| Use case | InData Labs fit | Persistent Systems fit | Winner |
|---|---|---|---|
| Building a predictive pricing or demand-forecasting model for logistics or transportation | Strong | Limited | InData Labs |
| Developing a computer-vision classification model with a documented accuracy target | Strong | Limited | InData Labs |
| Operationalizing ML models at enterprise scale using pre-built MLOps accelerators | Limited | Strong | Persistent Systems |
| Running bias detection and explainable AI reviews on existing production models | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Limited | Strong | Persistent Systems |
Verdict: InData Labs vs Persistent Systems
InData Labs (4.3/5) is the stronger overall choice for most ML Model Development projects. Publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims.. It is best for companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks..
Persistent Systems (3.9/5) is the better choice when mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and explainable-AI accelerators.. 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 (4.3/5) scores higher overall, but "better" depends on your use case. InData Labs is better for companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks.. Persistent Systems is better for mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and explainable-AI accelerators..
How do InData Labs and Persistent Systems differ in pricing?
InData Labs uses project-based pricing with a minimum engagement of $25,000. Persistent Systems uses not published; enterprise project engagements 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 company before shortlisting.
What are the main differences between InData Labs and Persistent Systems?
InData Labs's primary differentiator is: publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims.. Persistent Systems's primary differentiator is: purpose-built dxh accelerator suite for mlops and bias detection, plus a specific everest group leader ranking in the mid-market data & ai segment rather than only the largest enterprise tier.. They also differ in team size (51–200 vs 10,000+), minimum engagement ($25,000 vs Not published), and primary industries served (Transportation/logistics, Retail vs Healthcare, Financial services).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.