Infosys
Publicly traded (NYSE: INFY) Indian IT services company founded in 1981, with its Topaz AI practice offering 150+ pre-trained models.
Founded 1981 | Bengaluru, India | 10,000+ employees | Last updated: July 2026
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What is 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.
Infosys was founded in 1981 and is headquartered in Bengaluru, India. The firm employs 10,000+ people and works primarily with clients in Banking and financial services, Manufacturing, Retail, Telecommunications sectors. Its 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..
Infosys tech stack and services
Infosys Topaz (proprietary)Topaz Fabric (proprietary)Cloud ML platforms (AWS/Azure/GCP)
| Service area | Details |
|---|---|
| Very large enterprises wanting to accelerate AI delivery using a large library of pre-built models and assets | Available for Banking and financial services, Manufacturing, Retail, Telecommunications clients |
| Deploying composable AI agents via the Topaz Fabric platform across multiple business functions | Available for Banking and financial services, Manufacturing, Retail, Telecommunications clients |
| Global banking or manufacturing clients needing multi-region, multi-year AI transformation support | Available for Banking and financial services, Manufacturing, Retail, Telecommunications clients |
| Organizations wanting a single vendor to manage both legacy IT modernization and new AI initiatives | Available for Banking and financial services, Manufacturing, Retail, Telecommunications clients |
Infosys use cases
Short answer: Infosys is best suited for very large global enterprises wanting a substantial library of pre-built, reusable AI models and assets rather than starting entirely from scratch..
| Use case | Industries | Approach |
|---|---|---|
| Very large enterprises wanting to accelerate AI delivery using a large library of pre-built models and assets | Banking and financial services, Manufacturing | Infosys Topaz (proprietary), Topaz Fabric (proprietary) |
| Deploying composable AI agents via the Topaz Fabric platform across multiple business functions | Banking and financial services, Manufacturing | Infosys Topaz (proprietary), Topaz Fabric (proprietary) |
| Global banking or manufacturing clients needing multi-region, multi-year AI transformation support | Banking and financial services, Manufacturing | Infosys Topaz (proprietary), Topaz Fabric (proprietary) |
| Organizations wanting a single vendor to manage both legacy IT modernization and new AI initiatives | Banking and financial services, Manufacturing | Infosys Topaz (proprietary), Topaz Fabric (proprietary) |
Infosys pricing
Short answer: Infosys uses a not published; enterprise project engagements pricing approach. Minimum engagement starts at Not published.
| Engagement model | Typical range | Best for |
|---|---|---|
| Enterprise project engagement | Variable; depends on team size | Large programmes or team augmentation |
| Managed AI services | Variable; depends on team size | Large programmes or team augmentation |
| Composable agent platform (Topaz Fabric) | Variable; depends on team size | Large programmes or team augmentation |
Infosys does not publish a public rate card. Contact them directly via their website to get project-specific pricing.
Infosys pros and cons
| Advantages | Things to consider |
|---|---|
| +Largest disclosed pre-built AI asset library in this comparison (12,000+ assets, 150+ pre-trained models) can materially speed up delivery. | -Specific founding date, headquarters, and team size for the Topaz practice itself are not separately disclosed from the parent company in available public sources. |
| +New Topaz Fabric composable AI agent platform reflects continued investment in productized AI tooling. | -No clearly located aggregate Clutch/G2 star rating specific to its AI practice. |
| +Publicly traded (NYSE: INFY) with more than four decades of operating history and strong financial transparency. | -Pricing model and minimum engagement are not published, and typical minimums are substantial for enterprise engagements. |
| +Very large global workforce (330,000+) supports substantial multi-region program capacity. | -Heavy reliance on pre-built assets may be less suited to clients needing a fully custom, from-scratch model architecture. |
Infosys vs alternatives
How Infosys compares to the other top ML Model Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Mid-market fintech, supply chain, and SaaS companies that... | Combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in. | 4.8 | Full comparison |
| Neurons Lab | Financial services firms wanting a boutique, engineering-led partner... | End-to-end delivery model from use-case scoping to continuous production support, with declared depth in financial services. | 4.6 | Full comparison |
| DataRoot Labs | Startups and mid-market companies wanting a senior, AI-only... | Has never diversified beyond AI/ML services, and backs its delivery bench with an in-house ML training program (DataRoot University). | 4.6 | Full comparison |
| Miquido | Companies that need ML/computer-vision capability bundled with broader... | Combines a large, review-verified product engineering practice with a dedicated AI/ML/CV specialization, useful for teams needing both app and model work from one vendor. | 4.6 | Full comparison |
| Provectus | Mid-market companies that need cloud data infrastructure and... | Grew out of cloud and big-data engineering roots, giving it particular strength in the data infrastructure layer underneath ML models, not just the models themselves. | 4.5 | Full comparison |
| Neoteric | Organizations wanting a structured feasibility/strategy phase before committing... | Two-decade operating history combined with a formal upfront feasibility-assessment stage before any model-building work begins. | 4.5 | Full comparison |
| Addepto | Cost-conscious teams that specifically need MLOps consulting or... | Dedicated MLOps-consulting service line and Clutch-reported project pricing well below several peers in this list, making it the more budget-accessible option. | 4.4 | Full comparison |
| N-iX | Enterprise buyers wanting a large, heavily certified engineering... | Broadest cloud certification footprint in this comparison (350+ across five major platforms), backed by a 200+ person dedicated data practice. | 4.4 | Full comparison |
| InData Labs | Companies needing a focused predictive-analytics or computer-vision model... | Publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims. | 4.3 | Full comparison |
| MobiDev | Small and mid-sized companies wanting a dedicated ML/data-science... | Historical Clutch #1 ranking for machine learning development (2021) combined with a specifically SME-oriented service model. | 4.3 | Full comparison |
| Sciforce | Companies needing a research-oriented boutique for NLP, digital... | R&D-first culture with named specializations in digital signal processing and NLP that are less commonly offered as distinct practice areas by peers. | 4.2 | Full comparison |
| Sigmoid | Enterprises whose primary bottleneck is data infrastructure and... | Data-engineering-first approach with 950+ multi-cloud certified engineers, positioning it as an infrastructure specialist that also delivers ML rather than the reverse. | 4.2 | Full comparison |
| Tredence | Enterprises needing vertical-specific analytics and ML applied to... | Venture-backed growth trajectory ($205M raised) with named specialization in supply chain and customer analytics rather than generic horizontal AI consulting. | 4.2 | Full comparison |
| Quantiphi | Enterprises standardized on AWS wanting a partner with... | Deepest AWS-specific partnership credentials among firms researched, including AWS GenAI Innovation Center preferred-partner status. | 4.2 | Full comparison |
| Sigma Software Group | Companies wanting a large, diversified engineering group with... | Snowflake AI Data Cloud partnership combined with unusually broad industry diversification (AdTech through aviation to gaming). | 4.1 | Full comparison |
| Intellectsoft | Companies wanting an enterprise-name client roster and a... | Unusually strong roster of large, publicly named enterprise clients (EY, Qualcomm, London Stock Exchange) for a company of its relatively modest team size. | 4.1 | Full comparison |
| ELEKS | Enterprises wanting a long-established European software engineering partner... | One of the longest operating histories (since 1991) among firms researched for this list, predating the AI consulting boom by decades. | 4.1 | Full comparison |
| Fractal Analytics | Large enterprises wanting a scaled analytics and AI... | Maintains a dedicated internal foundational AI research team alongside client delivery work, and is now a publicly listed company (NSE/BSE) rather than privately held like most peers of similar size. | 4.1 | Full comparison |
| Xebia | Enterprises wanting a large, engineering-craftsmanship-rooted consultancy that has... | Quarter-century software craftsmanship and technical training heritage now applied specifically to production AI/ML delivery rather than AI strategy alone. | 4.0 | Full comparison |
| Grid Dynamics | Fortune 1000 companies wanting the financial transparency and... | The only publicly traded company (NASDAQ: GDYN) in this comparison among the mid-to-large tier, giving buyers audited financial transparency unavailable from private peers. | 4.0 | Full comparison |
| Iterate.ai | Data-sensitive enterprises (e.g., regulated industries) that require AI... | Purpose-built for on-premise/private-infrastructure AI deployment, so client data and proprietary code never leave the client's own environment. | 4.0 | Full comparison |
| Modus Create | Distributed organizations wanting a remote-first partner that pairs... | Structured AI Data Foundation assessment methodology that explicitly evaluates data readiness before committing to model development. | 4.0 | Full comparison |
| Aptus Data Labs | Companies wanting a boutique, India-based data engineering and... | Combines core data engineering consulting with specific AWS AI service implementation expertise in a boutique-sized team. | 4.0 | Full comparison |
| SoftServe | Enterprises needing edge computer vision or asset-monitoring ML... | Only company in this list simultaneously holding AWS Premier, Google Cloud AI/ML Specialization, and NVIDIA Elite Consulting Partner status, reflecting particular strength in edge and GPU-accelerated computer vision. | 4.0 | Full comparison |
| DataRobot | Enterprises that want to standardize on a single... | The only platform-first vendor in this comparison, meaning model development work happens on and around DataRobot's own automated ML software rather than being platform-agnostic. | 3.9 | Full comparison |
| Persistent Systems | Mid-market and enterprise buyers wanting a publicly traded,... | 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. | 3.9 | Full comparison |
| EPAM Systems | Very large enterprises wanting a publicly traded, AWS... | Proprietary EPAM DIAL platform for enterprise AI orchestration, combined with the 2025 AWS Global Innovation Partner of the Year distinction, an award-level differentiator not held by most peers. | 3.9 | Full comparison |
| Globant | Large enterprises wanting industry-specific pre-packaged AI solutions ("AI... | Only company in this list organized around a formal "studio + AI Pods" delivery model, and the only one with an IDC MarketScape Worldwide Leader in AI Services designation. | 3.9 | Full comparison |
| LTIMindtree | Large enterprises, particularly in BFSI and technology/media sectors,... | Explicit ModelOps templates and model-governance/responsible-AI tooling as named, productized capabilities rather than only bespoke consulting delivery, backed by an IBM watsonx Center of Excellence. | 3.9 | Full comparison |
| Cognizant | Large enterprises, especially in healthcare, wanting a very... | Dedicated, named MLOps platform specifically built for healthcare, combined with one of the largest disclosed data/AI consultant headcounts (23,000+) in this comparison. | 3.9 | Full comparison |
| HCLTech | Very large enterprises wanting a full-stack AI vendor... | Unusually broad "chip-to-cloud" AI stack claim backed by two named proprietary platforms (Graviton for ML development, AION for AI lifecycle management), a combination not matched by most peers in this list. | 3.9 | Full comparison |
| Accenture | The largest global enterprises needing AI model development... | By far the largest scale of any company in this comparison (approximately 779,000 employees, $69.67B FY2025 revenue), trading breadth and compliance maturity for less niche, hands-on model-engineering depth than boutique specialists. | 3.9 | Full comparison |
| Devbridge (a Cognizant company) | Clients who want Devbridge's original product-engineering delivery model... | The clearest ownership-change disclosure in this comparison: a formerly independent boutique now operating explicitly as a Cognizant subsidiary, combining boutique delivery heritage with large-parent-company backing. | 3.8 | Full comparison |
Infosys FAQ
What is 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.
How much does Infosys charge?
Infosys uses not published; enterprise project engagements pricing. Minimum engagement starts at Not published. A discovery call is required to get project-specific quotes.
What tech stack does Infosys use?
Infosys works with Infosys Topaz (proprietary), Topaz Fabric (proprietary), Cloud ML platforms (AWS/Azure/GCP). Primary industries served include Banking and financial services, Manufacturing, Retail, Telecommunications.
Is Infosys right for enterprise?
Very large global enterprises wanting a substantial library of pre-built, reusable AI models and assets rather than starting entirely from scratch.. 10,000+ team size. Key consideration: Specific founding date, headquarters, and team size for the Topaz practice itself are not separately disclosed from the parent company in available public sources..
What are the best Infosys alternatives?
The best alternatives to Infosys depend on your use case. Top options are:
- Tensorway: combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in.
- Neurons Lab: end-to-end delivery model from use-case scoping to continuous production support, with declared depth in financial services.
- DataRoot Labs: has never diversified beyond ai/ml services, and backs its delivery bench with an in-house ml training program (dataroot university).
Compare Infosys with other ML Model Development companies
Infosys vs Tensorway Infosys vs Neurons Lab Infosys vs DataRoot Labs Infosys vs Miquido Infosys vs Provectus Infosys vs Neoteric Infosys vs Addepto Infosys vs N-iX Infosys vs InData Labs Infosys vs MobiDev Infosys vs Sciforce Infosys vs Sigmoid Infosys vs Tredence Infosys vs Quantiphi Infosys vs Sigma Software Group Infosys vs Intellectsoft Infosys vs ELEKS Infosys vs Fractal Analytics Infosys vs Xebia Infosys vs Grid Dynamics Infosys vs Iterate.ai Infosys vs Modus Create Infosys vs Aptus Data Labs Infosys vs SoftServe Infosys vs DataRobot Infosys vs Persistent Systems Infosys vs EPAM Systems Infosys vs Globant Infosys vs LTIMindtree Infosys vs Cognizant Infosys vs HCLTech Infosys vs Accenture Infosys vs Devbridge (a Cognizant company)
Last reviewed: July 2026. Verify all details directly with Infosys before making a decision.