Persistent Systems vs ScienceSoft: full comparison for 2026
Last updated: July 2026
Quick verdict
ScienceSoft (3.9/5) edges ahead of Persistent Systems (3.8/5) overall. ScienceSoft is the better choice for companies wanting AI/ML delivered by a long-established generalist IT consultancy already handling other IT needs.. Persistent Systems is the stronger option for very large enterprises that want AI/ML delivered by the same vendor already running their broader IT estate.. The right choice depends on your project size, budget, and required tech stack.
Persistent Systems vs ScienceSoft: head-to-head summary
| Criterion | Persistent Systems | ScienceSoft |
|---|---|---|
| Founded | 1990 | 1989 |
| HQ | Pune, India | McKinney, Texas, USA |
| Team size | 10,000+ | 501–1,000 |
| Rating | 3.8 / 5 | 3.9 / 5 |
| Best for | Very large enterprises that want AI/ML delivered by the same vendor already running their broader IT estate. | Companies wanting AI/ML delivered by a long-established generalist IT consultancy already handling other IT needs. |
| Pricing model | Managed services and fixed project | Fixed project and Time & Material |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, Azure OpenAI, AWS | Python, TensorFlow, AWS |
| Industries served | Financial Services, Healthcare, Technology/SaaS, Government | Healthcare, Retail, Financial Services, Manufacturing |
Persistent Systems vs ScienceSoft: overview
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.
ScienceSoft
ScienceSoft is an IT consulting and software development company founded in 1989, headquartered in McKinney, Texas, with additional offices in Europe, the UAE, and Vietnam. The firm reports more than 750 IT professionals and over 3,600 delivered projects across its 36-year history, with AI/ML positioned as one core service area among IT strategy consulting, cloud, cybersecurity, and quality assurance.
Services and capabilities: Persistent Systems vs ScienceSoft
| Capability | Persistent Systems | ScienceSoft |
|---|---|---|
| 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: Persistent Systems vs ScienceSoft
| Framework / platform | Persistent Systems | ScienceSoft |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | N/A | ✓ |
| PyTorch | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | N/A | N/A |
| Kubernetes | N/A | N/A |
| Databricks | N/A | N/A |
| LangChain | N/A | N/A |
Pricing comparison: Persistent Systems vs ScienceSoft
| Criterion | Persistent Systems | ScienceSoft |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Managed services, Fixed project, Staff augmentation | Fixed project, Time & Material |
| Rate transparency | Not public | Not public |
| Price tier | Enterprise / not published | Enterprise / not published |
Target audience comparison: Persistent Systems vs ScienceSoft
| Dimension | Persistent Systems | ScienceSoft |
|---|---|---|
| Best company size | Enterprise | Mid-market to enterprise |
| Best industries | Financial Services, Healthcare, Technology/SaaS | Healthcare, Retail, Financial Services |
| Best use cases | 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 | Companies wanting AI/ML bundled with existing cloud, QA, or cybersecurity work from a single long-established vendor, Healthcare or manufacturing clients needing broad IT consulting plus a specific ML/AI component |
| Typical project type | Managed services | Fixed project |
Persistent Systems vs ScienceSoft: pros and cons
| 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 |
| ScienceSoft | |
|---|---|
| + | 36 years of continuous operation and 3,600+ delivered projects (per company website) among the longest track records reviewed here |
| + | Over half of staff cited as senior-level specialists (per company website) |
| + | Broad IT service catalog means AI/ML can be bundled with cloud, security, or QA from the same vendor |
| + | Multi-region office presence (Europe, UAE, Vietnam) beyond the US HQ |
| - | AI/ML is one of several core services (alongside cloud, cybersecurity, QA) rather than the firm's defining specialty |
| - | Less AI-first branding or ML-specific certification profile than boutique AI consultancies on this list |
| - | Minimum engagement size not publicly disclosed |
Who should choose Persistent Systems?
Persistent Systems is the right choice for very large enterprises that want AI/ML delivered by the same vendor already running their broader IT estate..
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.
Who should choose ScienceSoft?
ScienceSoft is the right choice for companies wanting AI/ML delivered by a long-established generalist IT consultancy already handling other IT needs..
36 years of continuous IT consulting history, one of the longest track records among firms on this list.. Minimum engagement starts at Not published. Works best with clients in Healthcare, Retail, Financial Services, Manufacturing.
Decision matrix: Persistent Systems vs ScienceSoft
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Persistent Systems |
| You need a large dedicated team for an ongoing programme | Check each company's engagement model |
| Your budget is at the lower end | Compare: Persistent Systems (Not published) vs ScienceSoft (Not published) |
| You need specialist depth in a specific vertical | Persistent Systems |
| You need staff augmentation or team extension | Persistent Systems |
| You need consulting before committing to a build | Persistent Systems |
Use case fit: Persistent Systems vs ScienceSoft
| Use case | Persistent Systems fit | ScienceSoft fit | Winner |
|---|---|---|---|
| Enterprises already using Persistent for core IT services wanting to add AI/ML from the same vendor | Strong | Limited | Persistent Systems |
| Very large, multi-year digital transformation programs where AI is one workstream among many | Strong | Strong | Both equally |
| Companies wanting AI/ML bundled with existing cloud, QA, or cybersecurity work from a single long-established vendor | Limited | Strong | ScienceSoft |
| Healthcare or manufacturing clients needing broad IT consulting plus a specific ML/AI component | Limited | Strong | ScienceSoft |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Persistent Systems vs ScienceSoft
ScienceSoft (3.9/5) is the stronger overall choice for most Machine Learning Development projects. 36 years of continuous IT consulting history, one of the longest track records among firms on this list.. It is best for companies wanting AI/ML delivered by a long-established generalist IT consultancy already handling other IT needs..
Persistent Systems (3.8/5) is the better choice when very large enterprises that want AI/ML delivered by the same vendor already running their broader IT estate.. If your situation matches those criteria, Persistent Systems is a competitive option.
Related comparisons
Persistent Systems vs ScienceSoft FAQ
Is Persistent Systems better than ScienceSoft?
ScienceSoft (3.9/5) scores higher overall, but "better" depends on your use case. Persistent Systems is better for very large enterprises that want AI/ML delivered by the same vendor already running their broader IT estate.. ScienceSoft is better for companies wanting AI/ML delivered by a long-established generalist IT consultancy already handling other IT needs..
How do Persistent Systems and ScienceSoft differ in pricing?
Persistent Systems uses managed services and fixed project pricing with a minimum engagement of Not published. ScienceSoft uses fixed project and time & material 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: Persistent Systems or ScienceSoft?
ScienceSoft 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 Persistent Systems and ScienceSoft?
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.. ScienceSoft's primary differentiator is: 36 years of continuous it consulting history, one of the longest track records among firms on this list.. They also differ in team size (10,000+ vs 501–1,000), minimum engagement (Not published vs Not published), and primary industries served (Financial Services, Healthcare vs Healthcare, Retail).
Last reviewed: July 2026. Verify all details directly with each agency before making a decision.