deepsense.ai vs Proxify: full comparison for 2026
Quick verdict
deepsense.ai (4.6/5) edges ahead of Proxify (4.4/5) overall. deepsense.ai is the better choice for teams that need a senior ML researcher who can also put models into production. Proxify is the stronger option for european companies that want a vetted ML or data engineer on European working hours. The right choice depends on your project size, budget, and required tech stack.
deepsense.ai vs Proxify: head-to-head summary
| Criterion | deepsense.ai | Proxify |
|---|---|---|
| Founded | 2014 | 2018 |
| HQ | Warsaw, Poland | Stockholm, Sweden |
| Team size | 100–200 | 5,000+ network members |
| Rating | 4.6 / 5 | 4.4 / 5 |
| Primary differentiator | A research-heavy bench of about 120 employed AI specialists with ten years of production work | Senior-engineer interviews with live coding after an automated skills test |
| Pricing model | Team extension billed monthly per engineer; projects quoted separately; rates on request | Hourly rate per developer billed monthly; full-time or part-time; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, PyTorch, TensorFlow |
| Industries served | Manufacturing, Retail, Healthcare, Financial services, Technology | SaaS, Fintech, E-commerce, Media, Healthcare |
deepsense.ai vs Proxify: overview
deepsense.ai
deepsense.ai has done AI work out of Warsaw since 2014, and its job listings describe a team of about 120 AI specialists who have delivered more than 200 commercial and research projects. Most of that team is employed directly, which matters if you want the same engineer for a year. The company sells team extension alongside its consulting work, and its recruiting ads ask for five or more years of production ML experience for senior roles. Strengths cluster around LLM and RAG systems, computer vision, defect detection and models that run on edge devices.
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.
Services and capabilities: deepsense.ai vs Proxify
| Capability | deepsense.ai | Proxify |
|---|---|---|
| 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: deepsense.ai vs Proxify
| Framework / platform | deepsense.ai | Proxify |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | ✓ | N/A |
| Hugging Face | ✓ | N/A |
| OpenAI | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | ✓ |
| Databricks | N/A | ✓ |
| Kubernetes | ✓ | N/A |
Pricing comparison: deepsense.ai vs Proxify
| Criterion | deepsense.ai | Proxify |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Dedicated engineer, Dedicated team, Project delivery | Dedicated engineer, Fractional expert, Freelance contract |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: deepsense.ai vs Proxify
| Dimension | deepsense.ai | Proxify |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Retail, Healthcare | SaaS, Fintech, E-commerce |
| Best use cases | Embedding an MLOps engineer in a platform team for a long engagement, Adding a computer-vision specialist for an edge defect-detection model | Adding a data engineer to a European fintech's analytics team, Hiring a Python ML developer for a recommender system |
| Typical project type | Dedicated engineer | Dedicated engineer |
deepsense.ai vs Proxify: pros and cons
| deepsense.ai | |
|---|---|
| + | Hiring ads for senior ML roles require five or more years of production experience |
| + | Engineers are mostly employees rather than contractors, which helps continuity |
| + | Deep computer-vision and edge-deployment experience, which few staffing firms can match |
| - | About 120 people, so large or sudden requests may wait |
| - | Staff augmentation is not its headline service; consulting projects get more of its marketing |
| - | No published rates or minimums |
| 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 |
Who should choose deepsense.ai?
A typical fit: embedding an MLOps engineer in a platform team for a long engagement.
A research-heavy bench of about 120 employed AI specialists with ten years of production work. Minimum engagement is not publicly disclosed. Works best with clients in Manufacturing, Retail, Healthcare, Financial services, Technology.
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.
Decision matrix: deepsense.ai vs Proxify
| Your situation | Recommended choice |
|---|---|
| You want a working engineer, not a recruiter, to run the technical screen | Both; deepsense.ai rates higher overall |
| You need one specialist for a few days a week | Proxify |
| You need several engineers working as one team | Proxify |
| 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: deepsense.ai (Not published) vs Proxify (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: deepsense.ai vs Proxify
| Use case | deepsense.ai fit | Proxify fit | Winner |
|---|---|---|---|
| Embedding an MLOps engineer in a platform team for a long engagement | Strong | Limited | deepsense.ai |
| Adding a computer-vision specialist for an edge defect-detection model | Strong | Strong | Both equally |
| Adding a data engineer to a European fintech's analytics team | Strong | Strong | Both equally |
| Hiring a Python ML developer for a recommender system | Limited | Strong | Proxify |
Verdict: deepsense.ai vs Proxify
deepsense.ai (4.6/5) is the stronger overall choice for most AI Engineer Staffing projects. A research-heavy bench of about 120 employed AI specialists with ten years of production work.
Proxify (4.4/5) is worth a look if you need hiring a Python ML developer for a recommender system. If your situation matches that, Proxify is a competitive option.
Related comparisons
deepsense.ai vs Proxify FAQ
Is deepsense.ai better than Proxify?
deepsense.ai (4.6/5) scores higher overall, but "better" depends on your use case. deepsense.ai's strongest advantage: hiring ads for senior ML roles require five or more years of production experience. Proxify's strongest advantage: live-coding interviews with in-house senior engineers are part of the published process.
How do deepsense.ai and Proxify differ in pricing?
deepsense.ai uses team extension billed monthly per engineer; projects quoted separately; rates on request pricing. Proxify uses hourly rate per developer billed monthly; full-time or part-time; 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: deepsense.ai or Proxify?
deepsense.ai 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 deepsense.ai and Proxify?
deepsense.ai's primary differentiator is: a research-heavy bench of about 120 employed AI specialists with ten years of production work. Proxify's primary differentiator is: senior-engineer interviews with live coding after an automated skills test. They also differ in team size (100–200 vs 5,000+ network members), minimum engagement (Not published vs Not published), and primary industries served (Manufacturing, Retail vs SaaS, Fintech).
Verify all details directly with each company before making a decision.