Equal Experts vs STX Next: full comparison for 2026
Quick verdict
Equal Experts (4.2/5) edges ahead of STX Next (4.2/5) overall. Equal Experts is the better choice for senior engineers for complex delivery, government and retail. STX Next is the stronger option for python back ends and data products, Polish rates. The right choice depends on your project size, budget, and required tech stack.
Equal Experts vs STX Next: head-to-head summary
| Criterion | Equal Experts | STX Next |
|---|---|---|
| Founded | 2007 | 2005 |
| HQ | London, United Kingdom | Poznań, Poland |
| Team size | 1,000–4,000 (staff and associates) | 500–600 |
| Rating | 4.2 / 5 | 4.2 / 5 |
| Primary differentiator | Senior-only teams drawn from a large associate network | Deep Python specialism across web back ends, data engineering and machine learning |
| Pricing model | Time and materials, capability-based | Time and materials, dedicated team |
| Min. engagement | Not published | Not published |
| Primary tech stack | Java, Kotlin, TypeScript | Python, Django, FastAPI |
| Industries served | Public sector, Retail, Financial services, Telecoms | Fintech, Healthcare, Media, Retail |
Equal Experts vs STX Next: overview
Equal Experts
Equal Experts launched in London in 2007 with a flat structure built around experienced engineers. The company lists a network of around 4,000 people, a mix of permanent staff and independent associates, and a consultancy directory reports that it is owned by its consultant and customer network through the Equal Experts Trust. UK clients have included HMRC, Defra and John Lewis. Teams are mostly senior and onshore, and its pricing reflects that.
STX Next
STX Next was founded in Poznań in 2005 and built its name as one of Europe's largest Python-focused development companies. Its March 2025 anniversary release cited about 500 people and more than 1,000 projects delivered. A later combination with Gliwice's Brainhub added roughly 100 more engineers. Innova Capital has been an investor since 2020.
Services and capabilities: Equal Experts vs STX Next
| Capability | Equal Experts | STX Next |
|---|---|---|
| Custom software development | ✓ | ✓ |
| Mobile app development | ✗ | ✗ |
| Cloud & DevOps | ✓ | ✓ |
| Data & AI | ✓ | ✓ |
| Legacy modernisation | ✓ | ✗ |
| Embedded & IoT | ✗ | ✗ |
| Dedicated team model | ✗ | ✓ |
| Fixed-price projects | ✗ | ✗ |
Tech stack comparison: Equal Experts vs STX Next
| Framework / platform | Equal Experts | STX Next |
|---|---|---|
| .NET | N/A | N/A |
| Java | ✓ | N/A |
| Python | N/A | ✓ |
| React | N/A | ✓ |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Kubernetes | ✓ | N/A |
| Embedded | N/A | N/A |
Pricing comparison: Equal Experts vs STX Next
| Criterion | Equal Experts | STX Next |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Time and materials, Team extension | Dedicated team, Team extension, Time and materials |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Equal Experts vs STX Next
| Dimension | Equal Experts | STX Next |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Public sector, Retail, Financial services | Fintech, Healthcare, Media |
| Best use cases | A government department needs senior engineers to rescue a struggling platform., A retailer wants a cloud platform team to build internal developer tooling. | A fintech needs a Django back end rebuilt and scaled., A media company wants data ingestion jobs and recommendation models written in Python. |
| Typical project type | Time and materials | Dedicated team |
Equal Experts vs STX Next: pros and cons
| Equal Experts | |
|---|---|
| + | Consultants are typically very experienced, which reduces the need for close supervision |
| + | Proven track record on large UK government platforms such as HMRC's |
| + | Ownership structure aligns consultants with long-term client outcomes |
| - | Associate-heavy model means team members may change between engagements |
| - | Mostly onshore and senior, so day rates are among the higher ones on this list |
| STX Next | |
|---|---|
| + | Python depth is hard to match among mid-sized European vendors |
| + | Combined STX Next and Brainhub team adds JavaScript and product capacity |
| + | Polish location gives near-total working-day overlap with the UK |
| - | No staffed UK office was found, so account management is run from Poland |
| - | The Brainhub merger is recent, and integration may take time |
Who should choose Equal Experts?
A typical fit: a government department needs senior engineers to rescue a struggling platform.
Senior-only teams drawn from a large associate network. Minimum engagement is not publicly disclosed. Works best with clients in Public sector, Retail, Financial services, Telecoms.
Who should choose STX Next?
A typical fit: a fintech needs a Django back end rebuilt and scaled.
Deep Python specialism across web back ends, data engineering and machine learning. Minimum engagement is not publicly disclosed. Works best with clients in Fintech, Healthcare, Media, Retail.
Decision matrix: Equal Experts vs STX Next
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| You need a large dedicated team for an ongoing programme | STX Next |
| Your budget is at the lower end | Compare: Equal Experts (Not published) vs STX Next (Not published) |
| You need specialist depth in a specific vertical | Equal Experts |
| You want engineers added to your own in-house team | Equal Experts |
| Your product includes hardware, firmware or connected devices | Neither; consider an embedded specialist |
Use case fit: Equal Experts vs STX Next
| Use case | Equal Experts fit | STX Next fit | Winner |
|---|---|---|---|
| A government department needs senior engineers to rescue a struggling platform. | Strong | Limited | Equal Experts |
| A retailer wants a cloud platform team to build internal developer tooling. | Strong | Limited | Equal Experts |
| A fintech needs a Django back end rebuilt and scaled. | Limited | Strong | STX Next |
| A media company wants data ingestion jobs and recommendation models written in Python. | Limited | Strong | STX Next |
| You need hardware, firmware or IoT work in the same contract | Limited | Limited | Both equally |
| You want a fixed-price first phase before a longer engagement | Limited | Limited | Both equally |
Verdict: Equal Experts vs STX Next
Equal Experts (4.2/5) is the stronger overall choice for most Software Development Outsourcing projects. Senior-only teams drawn from a large associate network.
STX Next (4.2/5) is worth a look in a scenario like this: a media company wants data ingestion jobs and recommendation models written in Python. If your situation matches that, STX Next is a competitive option.
Related comparisons
Equal Experts vs STX Next FAQ
Is Equal Experts better than STX Next?
Equal Experts (4.2/5) scores higher overall, but "better" depends on your use case. Equal Experts's strongest advantage: consultants are typically very experienced, which reduces the need for close supervision. STX Next's strongest advantage: python depth is hard to match among mid-sized European vendors.
How do Equal Experts and STX Next differ in pricing?
Equal Experts uses time and materials, capability-based pricing. STX Next uses time and materials, dedicated team pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Equal Experts or STX Next?
Equal Experts 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 Equal Experts and STX Next?
Equal Experts's primary differentiator is: senior-only teams drawn from a large associate network. STX Next's primary differentiator is: deep Python specialism across web back ends, data engineering and machine learning. They also differ in team size (1,000–4,000 (staff and associates) vs 500–600), minimum engagement (Not published vs Not published), and primary industries served (Public sector, Retail vs Fintech, Healthcare).
Verify all details directly with each company before making a decision.