Modus Create vs Persistent Systems: full comparison for 2026
Last updated: July 2026
Quick verdict
Modus Create (4.0/5) edges ahead of Persistent Systems (3.9/5) overall. Modus Create is the better choice for distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery.. Persistent Systems is the stronger option for mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and explainable-AI accelerators.. The right choice depends on your project size, budget, and required tech stack.
Modus Create vs Persistent Systems: head-to-head summary
| Criterion | Modus Create | Persistent Systems |
|---|---|---|
| Founded | 2011 | 1990 |
| HQ | Reston, USA | Pune, India |
| Team size | 501–1,000 | 10,000+ |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Best for | Distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery. | Mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and explainable-AI accelerators. |
| Pricing model | Not published; project and dedicated team | Not published; enterprise project engagements |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, AWS, Data governance tooling | AWS, Microsoft Azure, Google Cloud |
| Industries served | Technology/SaaS, Retail, Healthcare | Healthcare, Financial services, Technology/software, Life sciences |
Modus Create vs Persistent Systems: overview
Modus Create
Modus Create is a fully remote, distributed product engineering company founded in 2011 and headquartered in Reston, Virginia, with team members spread across more than 55 countries. The company's AI/ML and data engineering practice includes AI Strategy Roadmap assessments and AI Data Foundation assessments intended to ensure underlying data is reliable and properly governed before or alongside model development work. Modus Create has partnered with technology providers including Atlassian, GitHub, and AWS, and has been recognized on the Inc. 5000 list for nine consecutive years.
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: Modus Create vs Persistent Systems
| Capability | Modus Create | 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: Modus Create vs Persistent Systems
| Framework / platform | Modus Create | Persistent Systems |
|---|---|---|
| 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 | ✓ |
| Kubernetes | N/A | N/A |
| Snowflake | N/A | N/A |
| NVIDIA | N/A | N/A |
Pricing comparison: Modus Create vs Persistent Systems
| Criterion | Modus Create | Persistent Systems |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Fixed project, Dedicated team, Assessment/audit engagement | Enterprise project engagement, Managed AI services |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Modus Create vs Persistent Systems
| Dimension | Modus Create | Persistent Systems |
|---|---|---|
| Best company size | Mid-market to enterprise | Enterprise |
| Best industries | Technology/SaaS, Retail, Healthcare | Healthcare, Financial services, Technology/software |
| Best use cases | Running an AI Data Foundation assessment before committing to a full model-development engagement, Building an AI strategy roadmap for an organization new to machine learning adoption | 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 | Fixed project | Enterprise project engagement |
Modus Create vs Persistent Systems: pros and cons
| Modus Create | |
|---|---|
| + | Structured AI Data Foundation assessment reduces risk of building models on ungoverned or unreliable data. |
| + | Fully remote, globally distributed team (55+ countries) offers broad timezone coverage. |
| + | Nine consecutive years on the Inc. 5000 list signals sustained growth. |
| + | Technology partnerships with Atlassian, GitHub, and AWS support integrated delivery tooling. |
| - | AI/ML is one of several product engineering service lines rather than the company's sole specialization. |
| - | No clearly located aggregate Clutch/G2 star rating in available public sources. |
| - | Pricing model and minimum engagement are not published. |
| - | Fully remote delivery model may not suit buyers who prefer localized or on-site teams. |
| 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 Modus Create?
Modus Create is the right choice for distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery..
Structured AI Data Foundation assessment methodology that explicitly evaluates data readiness before committing to model development.. Minimum engagement starts at Not published. Works best with clients in Technology/SaaS, Retail, Healthcare.
Who should choose Persistent Systems?
Persistent Systems is the right choice for mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and 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.. Minimum engagement starts at Not published. Works best with clients in Healthcare, Financial services, Technology/software, Life sciences.
Decision matrix: Modus Create vs Persistent Systems
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Modus Create |
| You need a large dedicated team for an ongoing programme | Modus Create |
| Your budget is at the lower end | Compare: Modus Create (Not published) vs Persistent Systems (Not published) |
| You need specialist depth in a specific vertical | Persistent Systems |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Modus Create |
Use case fit: Modus Create vs Persistent Systems
| Use case | Modus Create fit | Persistent Systems fit | Winner |
|---|---|---|---|
| Running an AI Data Foundation assessment before committing to a full model-development engagement | Strong | Strong | Both equally |
| Building an AI strategy roadmap for an organization new to machine learning adoption | Strong | Limited | Modus Create |
| 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 | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| MLOps pipeline setup | Limited | Strong | Persistent Systems |
Verdict: Modus Create vs Persistent Systems
Modus Create (4.0/5) is the stronger overall choice for most ML Model Development projects. Structured AI Data Foundation assessment methodology that explicitly evaluates data readiness before committing to model development.. It is best for distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery..
Persistent Systems (3.9/5) is the better choice when mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and explainable-AI accelerators.. If your situation matches those criteria, Persistent Systems is a competitive option.
Related comparisons
Modus Create vs Persistent Systems FAQ
Is Modus Create better than Persistent Systems?
Modus Create (4.0/5) scores higher overall, but "better" depends on your use case. Modus Create is better for distributed organizations wanting a remote-first partner that pairs data-foundation assessments with AI/ML model delivery.. Persistent Systems is better for mid-market and enterprise buyers wanting a publicly traded, multi-cloud certified partner with pre-built MLOps and explainable-AI accelerators..
How do Modus Create and Persistent Systems differ in pricing?
Modus Create uses not published; project and dedicated team pricing with a minimum engagement of Not published. 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: Modus Create or Persistent Systems?
Modus Create 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 Modus Create and Persistent Systems?
Modus Create's primary differentiator is: structured ai data foundation assessment methodology that explicitly evaluates data readiness before committing to model development.. 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 (501–1,000 vs 10,000+), minimum engagement (Not published vs Not published), and primary industries served (Technology/SaaS, Retail vs Healthcare, Financial services).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.