InData Labs vs Infosys: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.3/5) edges ahead of Infosys (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.. Infosys is the stronger option for very large global enterprises wanting a substantial library of pre-built, reusable AI models and assets rather than starting entirely from scratch.. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs Infosys: head-to-head summary
| Criterion | InData Labs | Infosys |
|---|---|---|
| Founded | 2014 | 1981 |
| HQ | Nicosia, Cyprus (delivery center: Minsk, Belarus) | Bengaluru, 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. | Very large global enterprises wanting a substantial library of pre-built, reusable AI models and assets rather than starting entirely from scratch. |
| Pricing model | Project-based | Not published; enterprise project engagements |
| Min. engagement | $25,000 | Not published |
| Primary tech stack | Python, Computer vision frameworks, NLP toolkits | Infosys Topaz (proprietary), Topaz Fabric (proprietary), Cloud ML platforms (AWS/Azure/GCP) |
| Industries served | Transportation/logistics, Retail, Finance | Banking and financial services, Manufacturing, Retail, Telecommunications |
InData Labs vs Infosys: 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.
Infosys
Infosys was founded in 1981 in Pune by seven engineers including N.R. Narayana Murthy and Nandan Nilekani, and is headquartered in Bengaluru with more than 330,000 employees worldwide, trading publicly on the NYSE under INFY. Its AI practice, branded Infosys Topaz, reports more than 12,000 AI assets, over 150 pre-trained AI models, and more than ten AI platforms supporting machine learning, generative AI, conversational AI, and intelligent automation work across industry verticals. The company recently launched Topaz Fabric, a composable stack of AI agents, services, and models intended to accelerate enterprise AI investment value.
Services and capabilities: InData Labs vs Infosys
| Capability | InData Labs | Infosys |
|---|---|---|
| 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 Infosys
| Framework / platform | InData Labs | Infosys |
|---|---|---|
| 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 | N/A |
| Microsoft Azure | N/A | N/A |
| Kubernetes | N/A | N/A |
| Snowflake | N/A | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: InData Labs vs Infosys
| Criterion | InData Labs | Infosys |
|---|---|---|
| Minimum engagement | $25,000 | Not published |
| Engagement models | Fixed project, Time & Material | Enterprise project engagement, Managed AI services, Composable agent platform (Topaz Fabric) |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: InData Labs vs Infosys
| Dimension | InData Labs | Infosys |
|---|---|---|
| Best company size | Startup to mid-market | Enterprise |
| Best industries | Transportation/logistics, Retail, Finance | Banking and financial services, Manufacturing, Retail |
| 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 | Very large enterprises wanting to accelerate AI delivery using a large library of pre-built models and assets, Deploying composable AI agents via the Topaz Fabric platform across multiple business functions |
| Typical project type | Fixed project | Enterprise project engagement |
InData Labs vs Infosys: 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. |
| Infosys | |
|---|---|
| + | Largest disclosed pre-built AI asset library in this comparison (12,000+ assets, 150+ pre-trained models) can materially speed up delivery. |
| + | New Topaz Fabric composable AI agent platform reflects continued investment in productized AI tooling. |
| + | Publicly traded (NYSE: INFY) with more than four decades of operating history and strong financial transparency. |
| + | Very large global workforce (330,000+) supports substantial multi-region program capacity. |
| - | Specific founding date, headquarters, and team size for the Topaz practice itself are not separately disclosed from the parent company in available public sources. |
| - | No clearly located aggregate Clutch/G2 star rating specific to its AI practice. |
| - | Pricing model and minimum engagement are not published, and typical minimums are substantial for enterprise engagements. |
| - | Heavy reliance on pre-built assets may be less suited to clients needing a fully custom, from-scratch model architecture. |
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 Infosys?
Infosys is the right choice for very large global enterprises wanting a substantial library of pre-built, reusable AI models and assets rather than starting entirely from scratch..
Largest disclosed library of reusable, pre-trained AI assets in this comparison (12,000+ assets, 150+ pre-trained models), positioned to accelerate delivery versus fully bespoke builds.. Minimum engagement starts at Not published. Works best with clients in Banking and financial services, Manufacturing, Retail, Telecommunications.
Decision matrix: InData Labs vs Infosys
| 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 Infosys (Not published) |
| You need specialist depth in a specific vertical | Infosys |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Infosys |
Use case fit: InData Labs vs Infosys
| Use case | InData Labs fit | Infosys 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 |
| Very large enterprises wanting to accelerate AI delivery using a large library of pre-built models and assets | Limited | Strong | Infosys |
| Deploying composable AI agents via the Topaz Fabric platform across multiple business functions | Limited | Strong | Infosys |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Limited | Limited | Both equally |
Verdict: InData Labs vs Infosys
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..
Infosys (3.9/5) is the better choice when very large global enterprises wanting a substantial library of pre-built, reusable AI models and assets rather than starting entirely from scratch.. If your situation matches those criteria, Infosys is a competitive option.
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InData Labs vs Infosys FAQ
Is InData Labs better than Infosys?
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.. Infosys is better for very large global enterprises wanting a substantial library of pre-built, reusable AI models and assets rather than starting entirely from scratch..
How do InData Labs and Infosys differ in pricing?
InData Labs uses project-based pricing with a minimum engagement of $25,000. Infosys 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 Infosys?
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 Infosys?
InData Labs's primary differentiator is: publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims.. Infosys's primary differentiator is: largest disclosed library of reusable, pre-trained ai assets in this comparison (12,000+ assets, 150+ pre-trained models), positioned to accelerate delivery versus fully bespoke builds.. 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 Banking and financial services, Manufacturing).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.