Persistent Systems vs Django Stars: full comparison for 2026
Quick verdict
Persistent Systems (4.0/5) edges ahead of Django Stars (3.7/5) overall. Persistent Systems is the better choice for mid-large enterprise programs that don't need mega-giant scale. Django Stars is the stronger option for a buyer standardized on Python who wants that as a genuine specialty. The right choice depends on your project size, budget, and required tech stack.
Persistent Systems vs Django Stars: head-to-head summary
| Criterion | Persistent Systems | Django Stars |
|---|---|---|
| Founded | 1990 | 2008 |
| HQ | Pune, India | Kyiv, Ukraine |
| Team size | 23,900+ | 150–300 (per company website; independently unverifiable precise figure) |
| Rating | 4.0 / 5 | 3.7 / 5 |
| Primary differentiator | A public Indian IT company sized between the true mega-giants and the mid-market global players | One of the only companies on this list literally named after its founding technology stack |
| Pricing model | Enterprise consulting and dedicated-team engagements | Dedicated-team and fixed-project engagements |
| Min. engagement | Not published | Not published |
| Primary tech stack | AWS, Azure, Salesforce | Python, Django, React |
| Industries served | Technology, Life sciences, Financial services, Healthcare | Fintech, Healthcare, Logistics |
Persistent Systems vs Django Stars: overview
Persistent Systems
Persistent Systems was founded in Pune, India in 1990 and has grown to nearly 24,000 employees, occupying a middle tier between the true mega-giants on this list and the mid-market global players: large enough for serious enterprise programs, small enough that account relationships are less layered. Its practice covers software engineering, data, AI, and cloud services for enterprise clients, with a particular focus on technology and life sciences sectors. Its scale sits well below Accenture or TCS but comfortably above most of the boutique firms reviewed here.
Django Stars
Django Stars started in Kyiv in 2008 built specifically around Python and the Django framework, an unusually literal company name for a niche that most of the giants on this list would fold into a generic 'full-stack development' service line instead. It has since broadened into fintech and healthtech product work without losing the Python-heavy backend culture that gave it its name, staying at a boutique scale, 150-300 people, that a mega-giant would consider a rounding error. That specific framework specialization is real value for a buyer building a Python-heavy product; it's simply invisible to a buyer standardized on a different stack.
Services and capabilities: Persistent Systems vs Django Stars
| Capability | Persistent Systems | Django Stars |
|---|---|---|
| Enterprise modernization | ✓ | ✗ |
| Cloud & DevOps | ✓ | ✗ |
| AI/ML development | ✓ | ✗ |
| Custom software development | ✗ | ✓ |
| Staff augmentation | ✗ | ✗ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: Persistent Systems vs Django Stars
| Framework / platform | Persistent Systems | Django Stars |
|---|---|---|
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| SAP | N/A | N/A |
| Java | N/A | N/A |
| React | N/A | ✓ |
Pricing comparison: Persistent Systems vs Django Stars
| Criterion | Persistent Systems | Django Stars |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated team, Consulting, Fixed project | Dedicated team, Fixed project |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Persistent Systems vs Django Stars
| Dimension | Persistent Systems | Django Stars |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Technology, Life sciences, Financial services | Fintech, Healthcare, Logistics |
| Best use cases | A life sciences or technology company wanting a public, mid-large Indian IT vendor., An enterprise program too large for a boutique but not requiring mega-giant scale. | A fintech or healthtech startup building a Python or Django-based product from scratch., A buyer specifically wanting deep framework-level expertise over generalist breadth. |
| Typical project type | Dedicated team | Dedicated team |
Persistent Systems vs Django Stars: pros and cons
| Persistent Systems | |
|---|---|
| + | Public-company scale and financial transparency without mega-giant account layers. |
| + | Genuine technology and life sciences vertical depth. |
| + | Three decades of continuous operation since 1990. |
| - | Smaller global delivery capacity than Accenture, TCS, or Capgemini for the very largest programs |
| - | Less brand recognition outside India and the technology/life sciences sectors specifically |
| Django Stars | |
|---|---|
| + | Strong Python and Django engineering culture traceable to the company's own founding specialization. |
| + | Named fintech and healthtech product experience beyond generic full-stack claims. |
| + | Kyiv-founded and still headquartered there. |
| - | Python specialization is invisible value to a buyer standardized on a different backend stack |
| - | A team size a mega-giant would consider a rounding error, unsuited to very large programs |
Who should choose Persistent Systems?
A typical fit: a life sciences or technology company wanting a public, mid-large Indian IT vendor.
A public Indian IT company sized between the true mega-giants and the mid-market global players. Minimum engagement is not publicly disclosed. Works best with clients in Technology, Life sciences, Financial services, Healthcare.
Who should choose Django Stars?
A typical fit: a fintech or healthtech startup building a Python or Django-based product from scratch.
One of the only companies on this list literally named after its founding technology stack. Minimum engagement is not publicly disclosed. Works best with clients in Fintech, Healthcare, Logistics.
Decision matrix: Persistent Systems vs Django Stars
| 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 | Persistent Systems |
| Your budget is at the lower end | Compare: Persistent Systems (Not published) vs Django Stars (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 | Both may offer discovery engagements |
Use case fit: Persistent Systems vs Django Stars
| Use case | Persistent Systems fit | Django Stars fit | Winner |
|---|---|---|---|
| A life sciences or technology company wanting a public, mid-large Indian IT vendor. | Strong | Strong | Both equally |
| An enterprise program too large for a boutique but not requiring mega-giant scale. | Strong | Strong | Both equally |
| A fintech or healthtech startup building a Python or Django-based product from scratch. | Strong | Strong | Both equally |
| A buyer specifically wanting deep framework-level expertise over generalist breadth. | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Persistent Systems vs Django Stars
Persistent Systems (4.0/5) is the stronger overall choice for most IT Outsourcing projects. A public Indian IT company sized between the true mega-giants and the mid-market global players.
Django Stars (3.7/5) is worth a look if you need a buyer specifically wanting deep framework-level expertise over generalist breadth. If your situation matches that, Django Stars is a competitive option.
Related comparisons
Persistent Systems vs Django Stars FAQ
Is Persistent Systems better than Django Stars?
Persistent Systems (4.0/5) scores higher overall, but "better" depends on your use case. Persistent Systems's strongest advantage: public-company scale and financial transparency without mega-giant account layers. Django Stars's strongest advantage: strong Python and Django engineering culture traceable to the company's own founding specialization.
How do Persistent Systems and Django Stars differ in pricing?
Persistent Systems uses enterprise consulting and dedicated-team engagements pricing. Django Stars uses dedicated-team and fixed-project engagements pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Persistent Systems or Django Stars?
Django Stars 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 Persistent Systems and Django Stars?
Persistent Systems's primary differentiator is: a public Indian IT company sized between the true mega-giants and the mid-market global players. Django Stars's primary differentiator is: one of the only companies on this list literally named after its founding technology stack. They also differ in team size (23,900+ vs 150–300 (per company website; independently unverifiable precise figure)), minimum engagement (Not published vs Not published), and primary industries served (Technology, Life sciences vs Fintech, Healthcare).
Verify all details directly with each company before making a decision.