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
custom-model-trainingmlops-pipelinemodel-deploymentml-consulting
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 |
|---|
| Custom Model Training |
| MLOps Pipeline |
| Model Deployment & Serving |
| ML Strategy Consulting |
Infosys use cases
Short answer: Infosys is best suited for global enterprises, large library of reusable AI assets.
| Use case |
|---|
| 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 |
| Global banking or manufacturing clients needing multi-region, multi-year AI transformation support |
| Organizations wanting a single vendor to manage both legacy IT modernization and new AI initiatives |
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 and SaaS, hybrid forecasting models. | 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, engineering-led production AI. | 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 needing a senior AI-only team, LLM/CV projects. | 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 wanting ML/CV bundled with mobile/web engineering. | 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, cloud data plus ML in one. | 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 AI feasibility phase. | 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, MLOps consulting or advisory-only. | 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, certified data-platform plus ML partner. | 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 predictive-analytics or CV, documented accuracy. | Publishes concrete, quantified accuracy figures in its case studies rather than only qualitative outcome claims. | 4.3 | Full comparison |
| MobiDev | Small/mid-size companies, dedicated ML consulting arm. | Historical Clutch #1 ranking for machine learning development (2021) combined with a specifically SME-oriented service model. | 4.3 | Full comparison |
| Sciforce | Research-oriented boutique, NLP, DSP, computer vision. | 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 with data-infrastructure bottlenecks ahead of ML. | 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, vertical analytics for supply chain at scale. | 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 | AWS-standardized enterprises, deepest AWS AI/ML credentials. | 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 Snowflake-certified data platform plus ML. | Snowflake AI Data Cloud partnership combined with unusually broad industry diversification (AdTech through aviation to gaming). | 4.1 | Full comparison |
| Intellectsoft | Companies wanting enterprise clients, dedicated AI Lab. | 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, established European engineering plus data science. | 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, scaled AI partner with a research... | 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, craftsmanship-rooted production AI consultancy. | 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, publicly-traded ML partner. | 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 | Regulated enterprises, fully private-infrastructure AI deployment. | 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 orgs, remote-first data plus AI/ML delivery. | Structured AI Data Foundation assessment methodology that explicitly evaluates data readiness before committing to model development. | 4.0 | Full comparison |
| Aptus Data Labs | Boutique India-based data engineering, AWS AI depth. | Combines core data engineering consulting with specific AWS AI service implementation expertise in a boutique-sized team. | 4.0 | Full comparison |
| SoftServe | Enterprises, edge CV and asset-monitoring ML at scale. | 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 standardizing on one automated ML platform. | 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/enterprise buyers, MLOps plus 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. | 3.9 | Full comparison |
| EPAM Systems | Very large enterprises, proprietary AI orchestration platform. | 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, pre-packaged industry "AI Pods". | 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 | BFSI and tech enterprises, dedicated ModelOps and governance. | 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 | Healthcare-focused enterprises, large AI/analytics consulting bench. | 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, full-stack AI from chip to... | 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 | Largest global enterprises, AI within multi-year transformation. | 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 wanting Devbridge's model within Cognizant's structure. | 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?
Global enterprises, large library of reusable AI assets. 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)