SoftServe vs Infosys: full comparison for 2026
Last updated: July 2026
Quick verdict
SoftServe (4.0/5) edges ahead of Infosys (3.9/5) overall. SoftServe is the better choice for enterprises needing edge computer vision or asset-monitoring ML at scale, backed by the deepest multi-cloud/GPU certification stack in this comparison.. 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.
SoftServe vs Infosys: head-to-head summary
| Criterion | SoftServe | Infosys |
|---|---|---|
| Founded | 1993 | 1981 |
| HQ | Austin, USA (European hub: Lviv, Ukraine) | Bengaluru, India |
| Team size | 10,000+ | 10,000+ |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Best for | Enterprises needing edge computer vision or asset-monitoring ML at scale, backed by the deepest multi-cloud/GPU certification stack in this comparison. | 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; enterprise project engagements | Not published; enterprise project engagements |
| Min. engagement | Not published | Not published |
| Primary tech stack | AWS, Google Cloud, NVIDIA Jetson | Infosys Topaz (proprietary), Topaz Fabric (proprietary), Cloud ML platforms (AWS/Azure/GCP) |
| Industries served | Energy/oil and gas, Retail, Food manufacturing, Automotive | Banking and financial services, Manufacturing, Retail, Telecommunications |
SoftServe vs Infosys: overview
SoftServe
SoftServe was founded in 1993 in Lviv, Ukraine, and has grown into one of the largest privately held IT services companies headquartered out of Austin, Texas, with a European operating hub still in Lviv. The company reports more than 12,000 employees across 58 offices in 14 countries. Its AI/ML practice centers on computer vision at the edge for use cases including oil well monitoring, crop analysis, retail loss prevention, food manufacturing, and automotive production lines, supported by multimodal RAG assistants and asset-monitoring ML for the energy sector. SoftServe holds AWS Machine Learning Premier Consulting Partner status, Google Cloud Big Data/AI/ML Specialization, and NVIDIA Elite Consulting Partner and Jetson edge-AI partner status.
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: SoftServe vs Infosys
| Capability | SoftServe | 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: SoftServe vs Infosys
| Framework / platform | SoftServe | 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 |
| Microsoft Azure | N/A | N/A |
| Kubernetes | N/A | N/A |
| Snowflake | N/A | N/A |
| NVIDIA | ✓ | N/A |
Pricing comparison: SoftServe vs Infosys
| Criterion | SoftServe | Infosys |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Enterprise project engagement, Dedicated team | 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: SoftServe vs Infosys
| Dimension | SoftServe | Infosys |
|---|---|---|
| Best company size | Enterprise | Enterprise |
| Best industries | Energy/oil and gas, Retail, Food manufacturing | Banking and financial services, Manufacturing, Retail |
| Best use cases | Deploying edge computer vision for industrial monitoring (oil wells, production lines, food manufacturing), Building multimodal RAG assistants on top of enterprise knowledge bases | 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 | Enterprise project engagement | Enterprise project engagement |
SoftServe vs Infosys: pros and cons
| SoftServe | |
|---|---|
| + | Triple-certified across AWS, Google Cloud, and NVIDIA — the broadest verified partner-tier stack researched for this list. |
| + | Specific, detailed edge computer vision use cases (oil wells, crop monitoring, production lines) rather than generic AI claims. |
| + | Very large scale (12,000+ employees) supports substantial concurrent program capacity. |
| + | Three-decade operating history with continuity through significant regional disruption. |
| - | Clutch review volume is notably thin (only 3 reviews found) for a company of this size, limiting independent buyer feedback signal. |
| - | Enterprise scale may be less accessible or cost-effective for smaller buyers. |
| - | Pricing model and minimum engagement are not published. |
| - | Named enterprise clients for specific ML case studies are described by industry rather than by name in available sources. |
| 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 SoftServe?
SoftServe is the right choice for enterprises needing edge computer vision or asset-monitoring ML at scale, backed by the deepest multi-cloud/GPU certification stack in this comparison..
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.. Minimum engagement starts at Not published. Works best with clients in Energy/oil and gas, Retail, Food manufacturing, Automotive.
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: SoftServe vs Infosys
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| You need a large dedicated team for an ongoing programme | SoftServe |
| Your budget is at the lower end | Compare: SoftServe (Not published) vs Infosys (Not published) |
| You need specialist depth in a specific vertical | SoftServe |
| 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: SoftServe vs Infosys
| Use case | SoftServe fit | Infosys fit | Winner |
|---|---|---|---|
| Deploying edge computer vision for industrial monitoring (oil wells, production lines, food manufacturing) | Strong | Strong | Both equally |
| Building multimodal RAG assistants on top of enterprise knowledge bases | Strong | Limited | SoftServe |
| Very large enterprises wanting to accelerate AI delivery using a large library of pre-built models and assets | Strong | Strong | Both equally |
| Deploying composable AI agents via the Topaz Fabric platform across multiple business functions | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Limited | Limited | Both equally |
Verdict: SoftServe vs Infosys
SoftServe (4.0/5) is the stronger overall choice for most ML Model Development projects. 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.. It is best for enterprises needing edge computer vision or asset-monitoring ML at scale, backed by the deepest multi-cloud/GPU certification stack in this comparison..
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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SoftServe vs Infosys FAQ
Is SoftServe better than Infosys?
SoftServe (4.0/5) scores higher overall, but "better" depends on your use case. SoftServe is better for enterprises needing edge computer vision or asset-monitoring ML at scale, backed by the deepest multi-cloud/GPU certification stack in this comparison.. 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 SoftServe and Infosys differ in pricing?
SoftServe uses not published; enterprise project engagements 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: SoftServe or Infosys?
SoftServe 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 SoftServe and Infosys?
SoftServe's primary differentiator is: 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.. 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 (10,000+ vs 10,000+), minimum engagement (Not published vs Not published), and primary industries served (Energy/oil and gas, Retail vs Banking and financial services, Manufacturing).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.