Tensorway vs Persistent Systems: full comparison for 2026
Quick verdict
Tensorway (4.6/5) edges ahead of Persistent Systems (3.8/5) overall. Tensorway is the better choice for mid-market companies, full-stack ML plus agentic AI. Persistent Systems is the stronger option for very large enterprises, AI from their existing IT vendor. The right choice depends on your project size, budget, and required tech stack.
Tensorway vs Persistent Systems: head-to-head summary
| Criterion | Tensorway | Persistent Systems |
|---|---|---|
| Founded | 2019 | 1990 |
| HQ | Alicante, Spain | Pune, India |
| Team size | 51–200 | 10,000+ |
| Rating | 4.6 / 5 | 3.8 / 5 |
| Primary differentiator | full-stack ml delivery — data science, mlops, and llm/agentic frameworks (langchain, langgraph, autogen) — in one team | Enterprise-wide scale (24,000+ employees) supporting AI/ML as part of a full IT services portfolio, not a standalone specialty |
| Pricing model | Time & Material, Fixed-Price PoC, Extended/Dedicated Team, and MVP Development Models | Managed services and fixed project |
| Min. engagement | $10K | Not published |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, Azure OpenAI, AWS |
| Industries served | Healthcare, Finance, Retail, Manufacturing, Entertainment, Technology/SaaS | Financial Services, Healthcare, Technology/SaaS, Government |
Tensorway vs Persistent Systems: overview
Tensorway
Tensorway is a machine learning development company founded in 2019 and headquartered in Alicante, Spain, operating as an AI-focused entity. The firm focuses on deep learning, computer vision, and NLP systems for mid-market and enterprise clients in fintech, healthcare, retail, and edtech. Tensorway's engineering practice covers object detection, image segmentation, real-time video analytics, and large-scale NLP pipelines, with delivery backed by its parent company's 25-year software engineering track record. The team of 50+ ML engineers operates remotely across Europe and Latin America.
Persistent Systems
Persistent Systems is an Indian multinational technology company founded in 1990 by Anand Deshpande, headquartered in Pune, with roughly 24,600 employees as of March 2025. Its AI/ML offerings, including the Persistent GenAI Hub, sit within a much larger portfolio spanning enterprise software, cloud, and digital engineering services rather than being the company's core specialization.
Services and capabilities: Tensorway vs Persistent Systems
| Capability | Tensorway | Persistent Systems |
|---|---|---|
| Custom ML model development | ✓ | ✓ |
| Deep learning & computer vision | ✓ | ✗ |
| NLP & LLM / Generative AI | ✓ | ✗ |
| MLOps & production deployment | ✓ | ✗ |
| Data engineering | ✓ | ✓ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✓ | ✓ |
Tech stack comparison: Tensorway vs Persistent Systems
| Framework / platform | Tensorway | Persistent Systems |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | N/A |
| Kubernetes | ✓ | N/A |
| Databricks | N/A | N/A |
| LangChain | ✓ | N/A |
Pricing comparison: Tensorway vs Persistent Systems
| Criterion | Tensorway | Persistent Systems |
|---|---|---|
| Minimum engagement | $10K | Not published |
| Engagement models | Fixed project, Dedicated team, Time & Material, Consulting retainer, Managed services, Staff augmentation | Managed services, Fixed project, Staff augmentation |
| Rate transparency | Minimum disclosed | Not public |
| Price tier | Accessible | Enterprise / not published |
Target audience comparison: Tensorway vs Persistent Systems
| Dimension | Tensorway | Persistent Systems |
|---|---|---|
| Best company size | Startup to mid-market | Enterprise |
| Best industries | Healthcare, Finance, Retail | Financial Services, Healthcare, Technology/SaaS |
| Best use cases | Building a computer-vision pipeline for document or image understanding, Integrating a retrieval-augmented LLM chatbot or AI tutor into an existing product | Enterprises already using Persistent for core IT services wanting to add AI/ML from the same vendor, Very large, multi-year digital transformation programs where AI is one workstream among many |
| Typical project type | Fixed project | Managed services |
Tensorway vs Persistent Systems: pros and cons
| Tensorway | |
|---|---|
| + | Broad technical coverage across classic ML, deep learning, computer vision, NLP, and LLM/agentic frameworks |
| + | Multiple flexible pricing structures, including a fixed-price proof-of-concept option for buyers wary of open-ended T&M |
| + | Explicit MLOps/DevSecOps practice rather than treating deployment as an afterthought |
| + | Established project-management and QA processes for predictable, well-documented delivery |
| - | Public case studies name project types (document understanding, customer segmentation) but rarely name enterprise clients |
| - | Smaller core team than several larger competitors on this list, limiting parallel workstream capacity |
| Persistent Systems | |
|---|---|
| + | 35 years of operating history and one of the largest headcounts on this list (24,000+) |
| + | AI capability delivered alongside a company's existing broader IT services relationship, reducing vendor sprawl |
| + | 16,000+ AI-trained staff cited internally, suggesting significant AI upskilling investment (per company website) |
| + | Public-company scale supports very large, multi-year enterprise transformation programs |
| - | AI/ML is one offering within a much larger, more generalist IT services portfolio rather than the firm's core focus |
| - | Buyers seeking cutting-edge ML specialization may find deeper expertise at AI-first boutiques on this list |
| - | Very large organization can mean slower response times and more layered account management than smaller firms |
Who should choose Tensorway?
A typical fit: building a computer-vision pipeline for document or image understanding.
full-stack ml delivery — data science, mlops, and llm/agentic frameworks (langchain, langgraph, autogen) — in one team. Minimum engagement starts at $10K. Works best with clients in Healthcare, Finance, Retail, Manufacturing, Entertainment, Technology/SaaS.
Who should choose Persistent Systems?
A typical fit: enterprises already using Persistent for core IT services wanting to add AI/ML from the same vendor.
Enterprise-wide scale (24,000+ employees) supporting AI/ML as part of a full IT services portfolio, not a standalone specialty. Minimum engagement starts at Not published. Works best with clients in Financial Services, Healthcare, Technology/SaaS, Government.
Decision matrix: Tensorway vs Persistent Systems
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Tensorway |
| You need a large dedicated team for an ongoing programme | Tensorway |
| Your budget is at the lower end | Compare: Tensorway ($10K) vs Persistent Systems (Not published) |
| You need specialist depth in a specific vertical | Tensorway |
| You need staff augmentation or team extension | Persistent Systems |
| You need consulting before committing to a build | Tensorway |
Use case fit: Tensorway vs Persistent Systems
| Use case | Tensorway fit | Persistent Systems fit | Winner |
|---|---|---|---|
| Building a computer-vision pipeline for document or image understanding | Strong | Limited | Tensorway |
| Integrating a retrieval-augmented LLM chatbot or AI tutor into an existing product | Strong | Limited | Tensorway |
| Enterprises already using Persistent for core IT services wanting to add AI/ML from the same vendor | Limited | Strong | Persistent Systems |
| Very large, multi-year digital transformation programs where AI is one workstream among many | Limited | Strong | Persistent Systems |
| Fixed-price build | Strong | Limited | Tensorway |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Tensorway vs Persistent Systems
Tensorway (4.6/5) is the stronger overall choice for most Machine Learning Development projects. full-stack ml delivery — data science, mlops, and llm/agentic frameworks (langchain, langgraph, autogen) — in one team.
Persistent Systems (3.8/5) is worth a look if you need very large, multi-year digital transformation programs where AI is one workstream among many. If your situation matches that, Persistent Systems is a competitive option.
Related comparisons
Tensorway vs Persistent Systems FAQ
Is Tensorway better than Persistent Systems?
Tensorway (4.6/5) scores higher overall, but "better" depends on your use case. Tensorway's strongest advantage: broad technical coverage across classic ML, deep learning, computer vision, NLP, and LLM/agentic frameworks. Persistent Systems's strongest advantage: 35 years of operating history and one of the largest headcounts on this list (24,000+).
How do Tensorway and Persistent Systems differ in pricing?
Tensorway uses time & material, fixed-price poc, extended/dedicated team, and mvp development models pricing with a minimum engagement of $10K. Persistent Systems uses managed services and fixed project 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: Tensorway or Persistent Systems?
Tensorway is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each agency before shortlisting.
What are the main differences between Tensorway and Persistent Systems?
Tensorway's primary differentiator is: full-stack ml delivery — data science, mlops, and llm/agentic frameworks (langchain, langgraph, autogen) — in one team. Persistent Systems's primary differentiator is: enterprise-wide scale (24,000+ employees) supporting AI/ML as part of a full IT services portfolio, not a standalone specialty. They also differ in team size (51–200 vs 10,000+), minimum engagement ($10K vs Not published), and primary industries served (Healthcare, Finance vs Financial Services, Healthcare).