Proxify vs Quantiphi: full comparison for 2026
Quick verdict
Proxify (4.4/5) edges ahead of Quantiphi (4.3/5) overall. Proxify is the better choice for european companies that want a vetted ML or data engineer on European working hours. Quantiphi is the stronger option for enterprises that need many AI roles filled at once by one AI-only supplier. The right choice depends on your project size, budget, and required tech stack.
Proxify vs Quantiphi: head-to-head summary
| Criterion | Proxify | Quantiphi |
|---|---|---|
| Founded | 2018 | 2013 |
| HQ | Stockholm, Sweden | Marlborough, Massachusetts, USA |
| Team size | 5,000+ network members | 3,000–4,000+ |
| Rating | 4.4 / 5 | 4.3 / 5 |
| Primary differentiator | Senior-engineer interviews with live coding after an automated skills test | The biggest AI-only bench here, sold through a named staffing program with AWS |
| Pricing model | Hourly rate per developer billed monthly; full-time or part-time; rates on request | Elastic Staffing billed per specialist; consulting 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 | Healthcare, Financial services, Energy, Retail, Media |
Proxify vs Quantiphi: 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.
Quantiphi
Quantiphi, based in Marlborough, Massachusetts and founded in 2013, is the largest company on this page that works only on AI and data, with directory estimates between 3,000 and more than 4,000 people. Its Elastic Staffing program, built with AWS, places generative AI and ML specialists into client teams. That scale is the reason it ranks here: no other AI-only supplier can staff ML, MLOps, data and LLM roles in parallel. Google Cloud named it 2025 AI Partner of the Year for North America. The cost is attention, since staffing is one product inside a large consulting business.
Services and capabilities: Proxify vs Quantiphi
| Capability | Proxify | Quantiphi |
|---|---|---|
| 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 Quantiphi
| Framework / platform | Proxify | Quantiphi |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Google Cloud | ✓ | ✓ |
| Databricks | ✓ | ✓ |
| Kubernetes | N/A | ✓ |
Pricing comparison: Proxify vs Quantiphi
| Criterion | Proxify | Quantiphi |
|---|---|---|
| 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 Quantiphi
| Dimension | Proxify | Quantiphi |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | SaaS, Fintech, E-commerce | Healthcare, Financial services, Energy |
| Best use cases | Adding a data engineer to a European fintech's analytics team, Hiring a Python ML developer for a recommender system | Staffing eight GenAI specialists into an enterprise program, Adding Vertex AI or SageMaker engineers for a cloud ML migration |
| Typical project type | Dedicated engineer | Dedicated engineer |
Proxify vs Quantiphi: 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 |
| Quantiphi | |
|---|---|
| + | Can staff several AI specialties in parallel, which no other AI-only firm here can |
| + | Top partner tiers with Google Cloud and AWS help on cloud-specific ML roles |
| + | A named staffing product makes procurement simpler |
| - | Requests for one or two engineers compete with large consulting programs |
| - | Rates appear only after scoping |
| - | Headcount estimates vary widely between sources |
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 Quantiphi?
A typical fit: staffing eight GenAI specialists into an enterprise program.
The biggest AI-only bench here, sold through a named staffing program with AWS. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Financial services, Energy, Retail, Media.
Decision matrix: Proxify vs Quantiphi
| 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 Quantiphi (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 Quantiphi
| Use case | Proxify fit | Quantiphi 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 |
| Staffing eight GenAI specialists into an enterprise program | Limited | Strong | Quantiphi |
| Adding Vertex AI or SageMaker engineers for a cloud ML migration | Strong | Strong | Both equally |
Verdict: Proxify vs Quantiphi
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.
Quantiphi (4.3/5) is worth a look if you need adding Vertex AI or SageMaker engineers for a cloud ML migration. If your situation matches that, Quantiphi is a competitive option.
Related comparisons
Proxify vs Quantiphi FAQ
Is Proxify better than Quantiphi?
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. Quantiphi's strongest advantage: can staff several AI specialties in parallel, which no other AI-only firm here can.
How do Proxify and Quantiphi differ in pricing?
Proxify uses hourly rate per developer billed monthly; full-time or part-time; rates on request pricing. Quantiphi uses elastic staffing billed per specialist; consulting 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 Quantiphi?
Quantiphi 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 Quantiphi?
Proxify's primary differentiator is: senior-engineer interviews with live coding after an automated skills test. Quantiphi's primary differentiator is: the biggest AI-only bench here, sold through a named staffing program with AWS. They also differ in team size (5,000+ network members vs 3,000–4,000+), 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.