InData Labs vs Quantiphi: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.3/5) edges ahead of Quantiphi (4.2/5) overall. InData Labs is the better choice for companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks.. Quantiphi is the stronger option for enterprises standardized on AWS wanting a partner with the deepest documented AWS AI/ML partnership credentials in this comparison.. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs Quantiphi: head-to-head summary
| Criterion | InData Labs | Quantiphi |
|---|---|---|
| Founded | 2014 | 2013 |
| HQ | Nicosia, Cyprus (delivery center: Minsk, Belarus) | Marlborough, USA |
| Team size | 51–200 | 1,001–5,000 |
| Rating | 4.3 / 5 | 4.2 / 5 |
| Best for | Companies needing a focused predictive-analytics or computer-vision model with clearly documented accuracy benchmarks. | Enterprises standardized on AWS wanting a partner with the deepest documented AWS AI/ML partnership credentials in this comparison. |
| 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 SageMaker, Amazon Bedrock, AWS |
| Industries served | Transportation/logistics, Retail, Finance | Public sector, Healthcare, Financial services, Media |
InData Labs vs Quantiphi: 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.
Quantiphi
Quantiphi is a digital engineering company founded in 2013 by Vivek Khemani, Asif Hasan, Ritesh Patel, and Reghu Hariharan, focused on applied artificial intelligence, machine learning, and data science for complex business problems. Headquartered in Marlborough, Massachusetts, the company operates across six global locations and reports between 1,000 and 5,000 employees. Quantiphi holds AWS Premier Global Consulting Partner status and was named the first Preferred Amazon Quick Global SI Partner by the AWS Generative AI Innovation Center, alongside being recognized as 2025 AWS Public Sector Global GenAI Consulting Partner of the Year.
Services and capabilities: InData Labs vs Quantiphi
| Capability | InData Labs | Quantiphi |
|---|---|---|
| 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 Quantiphi
| Framework / platform | InData Labs | Quantiphi |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| MLflow | N/A | N/A |
| AWS SageMaker | N/A | ✓ |
| Amazon Bedrock | N/A | ✓ |
| Google Cloud | N/A | N/A |
| Microsoft Azure | N/A | N/A |
| Kubernetes | N/A | ✓ |
| Snowflake | N/A | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: InData Labs vs Quantiphi
| Criterion | InData Labs | Quantiphi |
|---|---|---|
| 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 Quantiphi
| Dimension | InData Labs | Quantiphi |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Transportation/logistics, Retail, Finance | Public sector, Healthcare, Financial services |
| 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 | Building and deploying ML models on AWS SageMaker at enterprise scale, Running a generative AI initiative using Amazon Bedrock with AWS-certified delivery support |
| Typical project type | Fixed project | Enterprise project engagement |
InData Labs vs Quantiphi: 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. |
| Quantiphi | |
|---|---|
| + | Strongest documented AWS partnership tier (Premier Global Consulting Partner) among companies in this comparison. |
| + | 2025 AWS Public Sector Global GenAI Consulting Partner of the Year recognition. |
| + | Reported $630.2M in revenue signals substantial scale and financial stability. |
| + | Multi-location global presence supports enterprise clients needing regional delivery. |
| - | Heavy AWS specialization may be less useful for clients standardized on Azure or GCP. |
| - | No clearly located aggregate Clutch/G2 star rating in available public sources. |
| - | Employee count range (1,000–5,000) is wide, making exact delivery capacity hard to pin down. |
| - | Pricing model and minimum engagement are not published. |
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 Quantiphi?
Quantiphi is the right choice for enterprises standardized on AWS wanting a partner with the deepest documented AWS AI/ML partnership credentials in this comparison..
Deepest AWS-specific partnership credentials among firms researched, including AWS GenAI Innovation Center preferred-partner status.. Minimum engagement starts at Not published. Works best with clients in Public sector, Healthcare, Financial services, Media.
Decision matrix: InData Labs vs Quantiphi
| 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 Quantiphi (Not published) |
| You need specialist depth in a specific vertical | Quantiphi |
| 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 Quantiphi
| Use case | InData Labs fit | Quantiphi fit | Winner |
|---|---|---|---|
| Building a predictive pricing or demand-forecasting model for logistics or transportation | Strong | Strong | Both equally |
| Developing a computer-vision classification model with a documented accuracy target | Strong | Limited | InData Labs |
| Building and deploying ML models on AWS SageMaker at enterprise scale | Strong | Strong | Both equally |
| Running a generative AI initiative using Amazon Bedrock with AWS-certified delivery support | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Limited | Strong | Quantiphi |
Verdict: InData Labs vs Quantiphi
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..
Quantiphi (4.2/5) is the better choice when enterprises standardized on AWS wanting a partner with the deepest documented AWS AI/ML partnership credentials in this comparison.. If your situation matches those criteria, Quantiphi is a competitive option.
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InData Labs vs Quantiphi FAQ
Is InData Labs better than Quantiphi?
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.. Quantiphi is better for enterprises standardized on AWS wanting a partner with the deepest documented AWS AI/ML partnership credentials in this comparison..
How do InData Labs and Quantiphi differ in pricing?
InData Labs uses project-based pricing with a minimum engagement of $25,000. Quantiphi 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 Quantiphi?
Quantiphi 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 Quantiphi?
InData Labs's primary differentiator is: publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims.. Quantiphi's primary differentiator is: deepest aws-specific partnership credentials among firms researched, including aws genai innovation center preferred-partner status.. They also differ in team size (51–200 vs 1,001–5,000), minimum engagement ($25,000 vs Not published), and primary industries served (Transportation/logistics, Retail vs Public sector, Healthcare).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.