Proxify vs SciForce: full comparison for 2026
Quick verdict
Proxify (4.4/5) edges ahead of SciForce (4.0/5) overall. Proxify is the better choice for european companies that want a vetted ML or data engineer on European working hours. SciForce is the stronger option for healthcare data teams that need NLP or data scientists familiar with medical data standards. The right choice depends on your project size, budget, and required tech stack.
Proxify vs SciForce: head-to-head summary
| Criterion | Proxify | SciForce |
|---|---|---|
| Founded | 2018 | 2015 |
| HQ | Stockholm, Sweden | Lviv, Ukraine (office in Tallinn, Estonia) |
| Team size | 5,000+ network members | 50–99 |
| Rating | 4.4 / 5 | 4.0 / 5 |
| Primary differentiator | Senior-engineer interviews with live coding after an automated skills test | Medical data science experience plus a documented multi-year placement engagement |
| Pricing model | Hourly rate per developer billed monthly; full-time or part-time; rates on request | Dedicated team billed monthly; projects quoted separately; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, PyTorch, TensorFlow |
| Industries served | SaaS, Fintech, E-commerce, Media, Healthcare | Healthcare, Financial services, Logistics, Agriculture, Education |
Proxify vs SciForce: overview
Proxify
Proxify was founded in Stockholm in 2018 (one of its own pages says 2019) and matches companies with vetted developers across web, data, AI and DevOps. Candidates take Codility-based skills tests, then sit in-depth technical interviews with Proxify's senior engineers that include live coding and practical problems. The company quotes an acceptance rate of 1–3%, though the figure varies from page to page. Its network covers more than 5,000 professionals in over 90 countries, and it appeared on the Financial Times 1,000 list in 2025. Matching uses in-house AI tools alongside its hiring team.
SciForce
SciForce has worked on AI and data science since 2015, with R&D offices in Lviv and Kharkiv and a representative office in Tallinn. Directories list 50 to 99 people. The clearest evidence of its staffing work is a Clutch review from a financial services IT director describing an engagement from January 2019 to February 2023 in which SciForce sourced and placed engineering talent and supplied a team of six to ten. Medical data science is a notable specialty, alongside NLP and logistics AI.
Services and capabilities: Proxify vs SciForce
| Capability | Proxify | SciForce |
|---|---|---|
| ML engineers | ✓ | ✓ |
| LLM / GenAI engineers | ✓ | ✗ |
| AI agent developers | ✗ | ✗ |
| MLOps engineers | ✗ | ✗ |
| Computer vision engineers | ✗ | ✗ |
| NLP engineers | ✗ | ✓ |
| Data engineers | ✓ | ✓ |
| Engineer-led technical screen | ✓ | ✗ |
| Fractional / part-time experts | ✓ | ✗ |
| Trial before commitment | ✗ | ✗ |
| Nearshore time-zone overlap | ✗ | ✗ |
| Direct hire option | ✗ | ✗ |
Tech stack comparison: Proxify vs SciForce
| Framework / platform | Proxify | SciForce |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | ✓ |
| OpenAI | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Google Cloud | ✓ | N/A |
| Databricks | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Proxify vs SciForce
| Criterion | Proxify | SciForce |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated engineer, Fractional expert, Freelance contract | Dedicated engineer, Dedicated team, Project delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Proxify vs SciForce
| Dimension | Proxify | SciForce |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | SaaS, Fintech, E-commerce | Healthcare, Financial services, Logistics |
| Best use cases | Adding a data engineer to a European fintech's analytics team, Hiring a Python ML developer for a recommender system | Adding an NLP engineer for clinical text extraction, Staffing a six-person data team for a financial client |
| Typical project type | Dedicated engineer | Dedicated engineer |
Proxify vs SciForce: pros and cons
| Proxify | |
|---|---|
| + | Live-coding interviews with in-house senior engineers are part of the published process |
| + | Most of the network is in European time zones, which suits teams in the EU and UK |
| + | Grew fast enough to make the Financial Times 1,000 list in 2025 |
| - | AI is one of many skill areas, and there is no AI-specific test on the record |
| - | Acceptance-rate and network-size figures differ across the company's own pages |
| - | Developers are contractors on the platform, not Proxify employees |
| SciForce | |
|---|---|
| + | A four-year augmentation engagement rated 5.0 on Clutch |
| + | Medical NLP and healthcare data experience |
| + | Lower cost base than Western European suppliers |
| - | Small team, with only a few engineers free at any time |
| - | Most staffing evidence comes from a single review |
| - | Wartime conditions in Ukraine need a continuity plan |
Who should choose Proxify?
A typical fit: adding a data engineer to a European fintech's analytics team.
Senior-engineer interviews with live coding after an automated skills test. Minimum engagement is not publicly disclosed. Works best with clients in SaaS, Fintech, E-commerce, Media, Healthcare.
Who should choose SciForce?
A typical fit: adding an NLP engineer for clinical text extraction.
Medical data science experience plus a documented multi-year placement engagement. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Financial services, Logistics, Agriculture, Education.
Decision matrix: Proxify vs SciForce
| Your situation | Recommended choice |
|---|---|
| You want a working engineer, not a recruiter, to run the technical screen | Proxify |
| You need one specialist for a few days a week | Proxify |
| You need several engineers working as one team | Both; Proxify rates higher overall |
| You want to test an engineer before committing | Neither publishes a trial; negotiate a short first term |
| Your budget is at the lower end | Compare: Proxify (Not published) vs SciForce (Not published) |
| Your team works U.S. hours | Neither lists Latin American engineers; confirm overlap hours in the contract |
| You may want to hire the engineer permanently later | Neither lists direct hire; agree conversion terms up front |
Use case fit: Proxify vs SciForce
| Use case | Proxify fit | SciForce fit | Winner |
|---|---|---|---|
| Adding a data engineer to a European fintech's analytics team | Strong | Strong | Both equally |
| Hiring a Python ML developer for a recommender system | Strong | Limited | Proxify |
| Adding an NLP engineer for clinical text extraction | Strong | Strong | Both equally |
| Staffing a six-person data team for a financial client | Limited | Strong | SciForce |
Verdict: Proxify vs SciForce
Proxify (4.4/5) is the stronger overall choice for most AI Engineer Staffing projects. Senior-engineer interviews with live coding after an automated skills test.
SciForce (4.0/5) is worth a look if you need staffing a six-person data team for a financial client. If your situation matches that, SciForce is a competitive option.
Related comparisons
Proxify vs SciForce FAQ
Is Proxify better than SciForce?
Proxify (4.4/5) scores higher overall, but "better" depends on your use case. Proxify's strongest advantage: live-coding interviews with in-house senior engineers are part of the published process. SciForce's strongest advantage: a four-year augmentation engagement rated 5.0 on Clutch.
How do Proxify and SciForce differ in pricing?
Proxify uses hourly rate per developer billed monthly; full-time or part-time; rates on request pricing. SciForce uses dedicated team billed monthly; projects quoted separately; rates on request pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Proxify or SciForce?
SciForce 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 Proxify and SciForce?
Proxify's primary differentiator is: senior-engineer interviews with live coding after an automated skills test. SciForce's primary differentiator is: medical data science experience plus a documented multi-year placement engagement. They also differ in team size (5,000+ network members vs 50–99), minimum engagement (Not published vs Not published), and primary industries served (SaaS, Fintech vs Healthcare, Financial services).
Verify all details directly with each company before making a decision.