Proxify vs Fusemachines: full comparison for 2026
Quick verdict
Proxify (4.4/5) edges ahead of Fusemachines (4.0/5) overall. Proxify is the better choice for european companies that want a vetted ML or data engineer on European working hours. Fusemachines is the stronger option for cost-conscious companies that want mid-level ML engineers from a publicly listed supplier. The right choice depends on your project size, budget, and required tech stack.
Proxify vs Fusemachines: head-to-head summary
| Criterion | Proxify | Fusemachines |
|---|---|---|
| Founded | 2018 | 2013 |
| HQ | Stockholm, Sweden | New York, USA |
| Team size | 5,000+ network members | 250–500 |
| Rating | 4.4 / 5 | 4.0 / 5 |
| Primary differentiator | Senior-engineer interviews with live coding after an automated skills test | Its own AI education programs feed an employed bench in emerging markets |
| Pricing model | Hourly rate per developer billed monthly; full-time or part-time; rates on request | Monthly per engineer or team; projects quoted separately; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, TensorFlow, PyTorch |
| Industries served | SaaS, Fintech, E-commerce, Media, Healthcare | Media, Financial services, Education, Retail, Healthcare |
Proxify vs Fusemachines: 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.
Fusemachines
Fusemachines was founded in New York in 2013 to bring AI talent and education to underserved countries, and it trains and employs engineers in Nepal, the Dominican Republic and elsewhere. It began trading on the Nasdaq in October 2025 after a SPAC merger, which makes its finances public. Clients can take on its engineers as dedicated AI staff or buy its products and projects. Its training programs feed the bench, so junior and mid-level ML engineers are easier to find here than senior researchers.
Services and capabilities: Proxify vs Fusemachines
| Capability | Proxify | Fusemachines |
|---|---|---|
| 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 Fusemachines
| Framework / platform | Proxify | Fusemachines |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | ✓ |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | N/A |
| Databricks | ✓ | ✓ |
| Kubernetes | N/A | N/A |
Pricing comparison: Proxify vs Fusemachines
| Criterion | Proxify | Fusemachines |
|---|---|---|
| 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 Fusemachines
| Dimension | Proxify | Fusemachines |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | SaaS, Fintech, E-commerce | Media, Financial services, Education |
| Best use cases | Adding a data engineer to a European fintech's analytics team, Hiring a Python ML developer for a recommender system | Adding two mid-level ML engineers for a media recommendation project, Staffing a data engineering team on a fixed budget |
| Typical project type | Dedicated engineer | Dedicated engineer |
Proxify vs Fusemachines: 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 |
| Fusemachines | |
|---|---|
| + | Public listing means audited financial disclosure |
| + | Lower rates than U.S. or Western European engineers |
| + | Dominican Republic team overlaps with U.S. hours |
| - | Listed on the Nasdaq through a SPAC merger in October 2025, so its strategy may change under public-market pressure |
| - | Bench skews toward mid-level engineers |
| - | Nepal hours overlap poorly with the Americas |
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 Fusemachines?
A typical fit: adding two mid-level ML engineers for a media recommendation project.
Its own AI education programs feed an employed bench in emerging markets. Minimum engagement is not publicly disclosed. Works best with clients in Media, Financial services, Education, Retail, Healthcare.
Decision matrix: Proxify vs Fusemachines
| 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 Fusemachines (Not published) |
| Your team works U.S. hours | Fusemachines |
| You may want to hire the engineer permanently later | Neither lists direct hire; agree conversion terms up front |
Use case fit: Proxify vs Fusemachines
| Use case | Proxify fit | Fusemachines 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 two mid-level ML engineers for a media recommendation project | Strong | Strong | Both equally |
| Staffing a data engineering team on a fixed budget | Limited | Strong | Fusemachines |
Verdict: Proxify vs Fusemachines
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.
Fusemachines (4.0/5) is worth a look if you need staffing a data engineering team on a fixed budget. If your situation matches that, Fusemachines is a competitive option.
Related comparisons
Proxify vs Fusemachines FAQ
Is Proxify better than Fusemachines?
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. Fusemachines's strongest advantage: public listing means audited financial disclosure.
How do Proxify and Fusemachines differ in pricing?
Proxify uses hourly rate per developer billed monthly; full-time or part-time; rates on request pricing. Fusemachines uses monthly per engineer or team; 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 Fusemachines?
Fusemachines 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 Fusemachines?
Proxify's primary differentiator is: senior-engineer interviews with live coding after an automated skills test. Fusemachines's primary differentiator is: its own AI education programs feed an employed bench in emerging markets. They also differ in team size (5,000+ network members vs 250–500), minimum engagement (Not published vs Not published), and primary industries served (SaaS, Fintech vs Media, Financial services).
Verify all details directly with each company before making a decision.