Tensorway vs Persistent Systems: full comparison for 2026
Quick verdict
Tensorway (4.8/5) edges ahead of Persistent Systems (3.9/5) overall. Tensorway is the better choice for mid-market fintech and SaaS, hybrid forecasting models. Persistent Systems is the stronger option for Mid-market/enterprise buyers, MLOps plus explainable-AI accelerators. 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 | 4.8 / 5 | 3.9 / 5 |
| Primary differentiator | Combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in | 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 |
| Pricing model | Time & Material, Fixed-Price PoC, Extended/Dedicated Team, and MVP Development Models | Not published; enterprise project engagements |
| Min. engagement | $10,000 | Not published |
| Primary tech stack | Python, TensorFlow, PyTorch | AWS, Microsoft Azure, Google Cloud |
| Industries served | Fintech, Supply chain, Energy, B2B SaaS, Healthcare, Retail | Healthcare, Financial services, Technology/software, Life sciences |
Tensorway vs Persistent Systems: overview
Tensorway
Tensorway builds and fine-tunes machine learning models for fintech, supply chain, energy, and B2B SaaS clients, with particular depth in hybrid approaches that combine statistical forecasting baselines with deep learning. The company was founded in 2019. Its delivery team spans data scientists, full-stack AI engineers, MLOps specialists, and QA engineers who support the full lifecycle from custom model training through deployment and monitoring. Case studies published on its site include a Named Entity Recognition model for automated Latvian/English invoice processing and a multi-agent deal-sourcing system for an investment firm.
Persistent Systems
Persistent Systems Limited was founded in 1990 in Pune, India, by Dr. Anand Deshpande, and has grown into a publicly traded (NSE/BSE: PERSISTENT) multinational technology services company with more than 24,000 employees. Its Data Science and Machine Learning practice spans data engineering through enterprise ML deployment across AWS, Azure, and Google Cloud, supported by its Data Experience Hub (DxH), a set of accelerators aimed at operationalizing ML and detecting bias in models through explainable AI. Persistent was named a Leader in the Everest Group Data & AI PEAK Matrix 2025 for the mid-market segment, and holds AWS Premier Tier Partner and Google Cloud Data & Analytics plus Machine Learning Specializations.
Services and capabilities: Tensorway vs Persistent Systems
| Capability | Tensorway | Persistent Systems |
|---|---|---|
| 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: Tensorway vs Persistent Systems
| Framework / platform | Tensorway | Persistent Systems |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| MLflow | ✓ | N/A |
| AWS SageMaker | ✓ | N/A |
| Amazon Bedrock | N/A | N/A |
| Google Cloud | ✓ | ✓ |
| Microsoft Azure | ✓ | ✓ |
| Kubernetes | ✓ | N/A |
| Snowflake | ✓ | N/A |
| NVIDIA | ✓ | N/A |
Pricing comparison: Tensorway vs Persistent Systems
| Criterion | Tensorway | Persistent Systems |
|---|---|---|
| Minimum engagement | $10,000 | Not published |
| Engagement models | Dedicated team, Fixed project, Retainer, Time & Material | Enterprise project engagement, Managed AI services |
| Rate transparency | $50 - $99 / hr | Not public |
| Price tier | Accessible | Mid-market |
Target audience comparison: Tensorway vs Persistent Systems
| Dimension | Tensorway | Persistent Systems |
|---|---|---|
| Best company size | Startup to mid-market | Enterprise |
| Best industries | Fintech, Supply chain, Energy | Healthcare, Financial services, Technology/software |
| Best use cases | Building a hybrid time-series forecasting model for supply chain or energy demand planning, Fine-tuning an NER model for multilingual document/invoice extraction | Operationalizing ML models at enterprise scale using pre-built MLOps accelerators, Running bias detection and explainable AI reviews on existing production models |
| Typical project type | Dedicated team | Enterprise project engagement |
Tensorway vs Persistent Systems: pros and cons
| Tensorway | |
|---|---|
| + | Strong delivery track record in deep learning and NLP, with client references available under NDA |
| + | Combines statistical and deep-learning methods rather than over-indexing on one approach. |
| + | Established project-management and QA processes for predictable, well-documented delivery |
| + | Published, verifiable case studies with concrete outcomes (e.g., NER-based invoice automation). |
| + | Broad four-tier engagement menu makes it accessible for both PoC-stage and scaling clients. |
| - | Relatively small team (51–200) limits capacity for very large, multi-workstream enterprise programs. |
| - | Published minimum engagement ($10,000) may still require a scoping call to firm up cost for larger projects. |
| Persistent Systems | |
|---|---|
| + | Everest Group Leader ranking in the Data & AI PEAK Matrix 2025 (mid-market segment) is an independently sourced third-party validation. |
| + | Purpose-built DxH accelerators for MLOps and bias detection add concrete, named tooling beyond generic claims. |
| + | Publicly traded with 35-year operating history, providing financial transparency. |
| + | Named healthcare client work (e.g., cancer-detection collaboration) with a specific, checkable use case. |
| - | Very large scale (24,000+ employees) means ML/AI is one of several major practice areas competing for delivery focus. |
| - | No clearly located aggregate Clutch/G2 star rating specific to its AI practice in available public sources. |
| - | Pricing model and minimum engagement are not published. |
| - | India-centric delivery model may require additional coordination for clients preferring more localized teams. |
Who should choose Tensorway?
A typical fit: building a hybrid time-series forecasting model for supply chain or energy demand planning.
Combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in. Minimum engagement starts at $10,000. Works best with clients in Fintech, Supply chain, Energy, B2B SaaS, Healthcare, Retail.
Who should choose Persistent Systems?
A typical fit: operationalizing ML models at enterprise scale using pre-built MLOps 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. Minimum engagement starts at Not published. Works best with clients in Healthcare, Financial services, Technology/software, Life sciences.
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 ($10,000) vs Persistent Systems (Not published) |
| You need specialist depth in a specific vertical | Tensorway |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: Tensorway vs Persistent Systems
| Use case | Tensorway fit | Persistent Systems fit | Winner |
|---|---|---|---|
| Building a hybrid time-series forecasting model for supply chain or energy demand planning | Strong | Limited | Tensorway |
| Fine-tuning an NER model for multilingual document/invoice extraction | Strong | Limited | Tensorway |
| Operationalizing ML models at enterprise scale using pre-built MLOps accelerators | Limited | Strong | Persistent Systems |
| Running bias detection and explainable AI reviews on existing production models | Limited | Strong | Persistent Systems |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Limited | Strong | Persistent Systems |
Verdict: Tensorway vs Persistent Systems
Tensorway (4.8/5) is the stronger overall choice for most ML Model Development projects. Combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in.
Persistent Systems (3.9/5) is worth a look if you need running bias detection and explainable AI reviews on existing production models. 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 (4.8/5) scores higher overall, but "better" depends on your use case. Tensorway's strongest advantage: strong delivery track record in deep learning and NLP, with client references available under NDA. Persistent Systems's strongest advantage: everest Group Leader ranking in the Data & AI PEAK Matrix 2025 (mid-market segment) is an independently sourced third-party validation.
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 $10,000. Persistent Systems 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: 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 company before shortlisting.
What are the main differences between Tensorway and Persistent Systems?
Tensorway's primary differentiator is: combines classical statistical forecasting with deep learning rather than defaulting to deep learning alone, and ships with experiment tracking and monitoring built in. Persistent Systems's primary differentiator is: 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. They also differ in team size (51–200 vs 10,000+), minimum engagement ($10,000 vs Not published), and primary industries served (Fintech, Supply chain vs Healthcare, Financial services).