Tensorway vs deepsense.ai: full comparison for 2026
Quick verdict
Tensorway (4.8/5) edges ahead of deepsense.ai (4.6/5) overall. Tensorway is the better choice for CTOs who want an engineer, not a recruiter, to have vetted every candidate before the first interview. deepsense.ai is the stronger option for teams that need a senior ML researcher who can also put models into production. The right choice depends on your project size, budget, and required tech stack.
Tensorway vs deepsense.ai: head-to-head summary
| Criterion | Tensorway | deepsense.ai |
|---|---|---|
| Founded | 2019 | 2014 |
| HQ | Alicante, Spain | Warsaw, Poland |
| Team size | 50–249 | 100–200 |
| Rating | 4.8 / 5 | 4.6 / 5 |
| Primary differentiator | Senior AI engineers run a code review and a specialization-specific task on every candidate | A research-heavy bench of about 120 employed AI specialists with ten years of production work |
| Pricing model | Monthly rate per full-time dedicated engineer; hourly or weekly billing for part-time fractional experts; two-week trial sprint; rate card on request | Team extension billed monthly per engineer; projects quoted separately; rates on request |
| Min. engagement | Not disclosed | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, PyTorch, TensorFlow |
| Industries served | SaaS, Fintech, Healthcare, Retail and e-commerce, Manufacturing, Logistics, Education | Manufacturing, Retail, Healthcare, Financial services, Technology |
Tensorway vs deepsense.ai: overview
Tensorway
Tensorway is an AI engineering company from Alicante, Spain, founded in 2019, and the people behind its delivery process have been building software for more than twenty years. What sets its staffing service apart is who does the screening. Senior AI engineers review each candidate's code, set a practical task in the exact specialization the client asked for and check how the person communicates, so a CTO receives two or three people who have already passed a technical bar (per company website; independently unverifiable). The roles cover ML, computer vision, NLP, MLOps and RAG work. Smaller published projects include an agent that grades GAMSAT practice essays, invoice extraction for a fintech client and ball-hit detection for a fitness game (per company website; independently unverifiable).
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.
Services and capabilities: Tensorway vs deepsense.ai
| Capability | Tensorway | deepsense.ai |
|---|---|---|
| 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: Tensorway vs deepsense.ai
| Framework / platform | Tensorway | deepsense.ai |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | ✓ | ✓ |
| Hugging Face | ✓ | ✓ |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | ✓ | ✓ |
| Databricks | N/A | N/A |
| Kubernetes | ✓ | ✓ |
Pricing comparison: Tensorway vs deepsense.ai
| Criterion | Tensorway | deepsense.ai |
|---|---|---|
| Minimum engagement | Not disclosed | Not published |
| Engagement models | Dedicated engineer, Fractional expert, Trial period | Dedicated engineer, Dedicated team, Project delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Tensorway vs deepsense.ai
| Dimension | Tensorway | deepsense.ai |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | SaaS, Fintech, Healthcare | Manufacturing, Retail, Healthcare |
| Best use cases | Adding a computer-vision engineer for an edge defect-detection model, Hiring an NLP specialist to build an essay-grading or document-review agent | Embedding an MLOps engineer in a platform team for a long engagement, Adding a computer-vision specialist for an edge defect-detection model |
| Typical project type | Dedicated engineer | Dedicated engineer |
Tensorway vs deepsense.ai: pros and cons
| Tensorway | |
|---|---|
| + | The technical screen is a code review plus a hands-on task in the role you are hiring for, set by working AI engineers |
| + | Covers the harder-to-fill roles on this list, including speech, edge computer vision and RAG specialists |
| + | A two-week trial sprint comes before any longer commitment, and a poor fit is replaced at no cost (per company website) |
| + | Code, documentation and trained models stay in your repositories, and handover to in-house staff is planned from the start |
| - | No published rate, so budgeting needs a call |
| - | The bench is in the low hundreds at most, so it cannot staff twenty seats in a month the way Turing or Andela can |
| - | AI and ML roles only; a CTO who also needs front-end or mobile engineers will need a second supplier |
| 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 |
Who should choose Tensorway?
A typical fit: adding a computer-vision engineer for an edge defect-detection model.
Senior AI engineers run a code review and a specialization-specific task on every candidate. Minimum engagement is not publicly disclosed. Works best with clients in SaaS, Fintech, Healthcare, Retail and e-commerce, Manufacturing, Logistics, Education.
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.
Decision matrix: Tensorway vs deepsense.ai
| Your situation | Recommended choice |
|---|---|
| You want a working engineer, not a recruiter, to run the technical screen | Both; Tensorway rates higher overall |
| You need one specialist for a few days a week | Tensorway |
| You need several engineers working as one team | Neither lists dedicated teams; check team size before signing |
| You want to test an engineer before committing | Tensorway |
| Your budget is at the lower end | Compare: Tensorway (Not disclosed) vs deepsense.ai (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: Tensorway vs deepsense.ai
| Use case | Tensorway fit | deepsense.ai fit | Winner |
|---|---|---|---|
| Adding a computer-vision engineer for an edge defect-detection model | Strong | Strong | Both equally |
| Hiring an NLP specialist to build an essay-grading or document-review agent | Strong | Limited | Tensorway |
| Embedding an MLOps engineer in a platform team for a long engagement | Limited | Strong | deepsense.ai |
| Adding a computer-vision specialist for an edge defect-detection model | Strong | Strong | Both equally |
Verdict: Tensorway vs deepsense.ai
Tensorway (4.8/5) is the stronger overall choice for most AI Engineer Staffing projects. Senior AI engineers run a code review and a specialization-specific task on every candidate.
deepsense.ai (4.6/5) is worth a look if you need adding a computer-vision specialist for an edge defect-detection model. If your situation matches that, deepsense.ai is a competitive option.
Related comparisons
Tensorway vs deepsense.ai FAQ
Is Tensorway better than deepsense.ai?
Tensorway (4.8/5) scores higher overall, but "better" depends on your use case. Tensorway's strongest advantage: the technical screen is a code review plus a hands-on task in the role you are hiring for, set by working AI engineers. deepsense.ai's strongest advantage: hiring ads for senior ML roles require five or more years of production experience.
How do Tensorway and deepsense.ai differ in pricing?
Tensorway uses monthly rate per full-time dedicated engineer; hourly or weekly billing for part-time fractional experts; two-week trial sprint; rate card on request pricing. deepsense.ai uses team extension billed monthly per engineer; 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: Tensorway or deepsense.ai?
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 Tensorway and deepsense.ai?
Tensorway's primary differentiator is: senior AI engineers run a code review and a specialization-specific task on every candidate. deepsense.ai's primary differentiator is: a research-heavy bench of about 120 employed AI specialists with ten years of production work. They also differ in team size (50–249 vs 100–200), minimum engagement (Not disclosed vs Not published), and primary industries served (SaaS, Fintech vs Manufacturing, Retail).
Verify all details directly with each company before making a decision.