Aptus Data Labs vs Infosys: full comparison for 2026
Last updated: July 2026
Quick verdict
Aptus Data Labs (4.0/5) edges ahead of Infosys (3.9/5) overall. Aptus Data Labs is the better choice for companies wanting a boutique, India-based data engineering and analytics firm with AWS AI service depth.. 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.
Aptus Data Labs vs Infosys: head-to-head summary
| Criterion | Aptus Data Labs | Infosys |
|---|---|---|
| Founded | 2014 | 1981 |
| HQ | Bengaluru, India | Bengaluru, India |
| Team size | 51–200 | 10,000+ |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Best for | Companies wanting a boutique, India-based data engineering and analytics firm with AWS AI service depth. | Very large global enterprises wanting a substantial library of pre-built, reusable AI models and assets rather than starting entirely from scratch. |
| Pricing model | Not published; project-based | Not published; enterprise project engagements |
| Min. engagement | Not published | Not published |
| Primary tech stack | AWS AI services, Python, Data engineering/analytics tooling | Infosys Topaz (proprietary), Topaz Fabric (proprietary), Cloud ML platforms (AWS/Azure/GCP) |
| Industries served | Enterprise (cross-industry), Financial services | Banking and financial services, Manufacturing, Retail, Telecommunications |
Aptus Data Labs vs Infosys: overview
Aptus Data Labs
Aptus Data Labs is a data engineering and advanced analytics company founded in 2014 in Bangalore by Ravindra Swamy and Samir Kumar Sahoo. The company offers analytical solutions and consulting services aimed at helping businesses make data-driven decisions, with a practice that spans cloud solutions and AWS AI services alongside core data engineering. Reported employee counts vary across sources from roughly 45 to a few hundred, positioning it as a smaller boutique analytics firm rather than a large-scale delivery organization.
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: Aptus Data Labs vs Infosys
| Capability | Aptus Data 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: Aptus Data Labs vs Infosys
| Framework / platform | Aptus Data 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: Aptus Data Labs vs Infosys
| Criterion | Aptus Data Labs | Infosys |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Fixed project, Consulting engagement | Enterprise project engagement, Managed AI services, Composable agent platform (Topaz Fabric) |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Aptus Data Labs vs Infosys
| Dimension | Aptus Data Labs | Infosys |
|---|---|---|
| Best company size | Startup to mid-market | Enterprise |
| Best industries | Enterprise (cross-industry), Financial services | Banking and financial services, Manufacturing, Retail |
| Best use cases | Building AWS-native data engineering pipelines to support downstream ML models, Running a focused analytics consulting engagement for a mid-market Indian or global company | 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 |
Aptus Data Labs vs Infosys: pros and cons
| Aptus Data Labs | |
|---|---|
| + | Decade-plus operating history as a focused data engineering and analytics boutique. |
| + | Specific AWS AI services expertise adds credibility for AWS-standardized buyers. |
| + | Founder-led with stable leadership since 2014. |
| + | Boutique size may offer more attentive, senior-level engagement than larger firms. |
| - | Employee count estimates vary widely across sources, creating uncertainty about actual delivery capacity. |
| - | Public, named case studies with quantified ML outcomes are limited in available sources. |
| - | No clearly located aggregate Clutch/G2 star rating in available public sources. |
| - | Smaller scale limits suitability for very large, multi-region enterprise programs. |
| 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 Aptus Data Labs?
Aptus Data Labs is the right choice for companies wanting a boutique, India-based data engineering and analytics firm with AWS AI service depth..
Combines core data engineering consulting with specific AWS AI service implementation expertise in a boutique-sized team.. Minimum engagement starts at Not published. Works best with clients in Enterprise (cross-industry), Financial services.
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: Aptus Data Labs vs Infosys
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Aptus Data 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: Aptus Data Labs (Not published) 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: Aptus Data Labs vs Infosys
| Use case | Aptus Data Labs fit | Infosys fit | Winner |
|---|---|---|---|
| Building AWS-native data engineering pipelines to support downstream ML models | Strong | Limited | Aptus Data Labs |
| Running a focused analytics consulting engagement for a mid-market Indian or global company | Strong | Limited | Aptus Data 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: Aptus Data Labs vs Infosys
Aptus Data Labs (4.0/5) is the stronger overall choice for most ML Model Development projects. Combines core data engineering consulting with specific AWS AI service implementation expertise in a boutique-sized team.. It is best for companies wanting a boutique, India-based data engineering and analytics firm with AWS AI service depth..
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.
Related comparisons
Aptus Data Labs vs Infosys FAQ
Is Aptus Data Labs better than Infosys?
Aptus Data Labs (4.0/5) scores higher overall, but "better" depends on your use case. Aptus Data Labs is better for companies wanting a boutique, India-based data engineering and analytics firm with AWS AI service depth.. 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 Aptus Data Labs and Infosys differ in pricing?
Aptus Data Labs uses not published; project-based pricing with a minimum engagement of Not published. 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: Aptus Data Labs or Infosys?
Aptus Data 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 Aptus Data Labs and Infosys?
Aptus Data Labs's primary differentiator is: combines core data engineering consulting with specific aws ai service implementation expertise in a boutique-sized team.. 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 (Not published vs Not published), and primary industries served (Enterprise (cross-industry), Financial services vs Banking and financial services, Manufacturing).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.