Tensorway vs Quantiphi: full comparison for 2026
Quick verdict
Tensorway (4.8/5) edges ahead of Quantiphi (4.3/5) overall. Tensorway is the better choice for CTOs who want an engineer, not a recruiter, to have vetted every candidate before the first interview. 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.
Tensorway vs Quantiphi: head-to-head summary
| Criterion | Tensorway | Quantiphi |
|---|---|---|
| Founded | 2019 | 2013 |
| HQ | Alicante, Spain | Marlborough, Massachusetts, USA |
| Team size | 50–249 | 3,000–4,000+ |
| Rating | 4.8 / 5 | 4.3 / 5 |
| Primary differentiator | Senior AI engineers run a code review and a specialization-specific task on every candidate | The biggest AI-only bench here, sold through a named staffing program with AWS |
| 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 | Elastic Staffing billed per specialist; consulting quoted separately; rates on request |
| Min. engagement | Not disclosed | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, TensorFlow, PyTorch |
| Industries served | SaaS, Fintech, Healthcare, Retail and e-commerce, Manufacturing, Logistics, Education | Healthcare, Financial services, Energy, Retail, Media |
Tensorway vs Quantiphi: 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).
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: Tensorway vs Quantiphi
| Capability | Tensorway | 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: Tensorway vs Quantiphi
| Framework / platform | Tensorway | Quantiphi |
|---|---|---|
| PyTorch | ✓ | ✓ |
| TensorFlow | ✓ | ✓ |
| LangChain | ✓ | N/A |
| Hugging Face | ✓ | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Google Cloud | ✓ | ✓ |
| Databricks | N/A | ✓ |
| Kubernetes | ✓ | ✓ |
Pricing comparison: Tensorway vs Quantiphi
| Criterion | Tensorway | Quantiphi |
|---|---|---|
| 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 Quantiphi
| Dimension | Tensorway | Quantiphi |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | SaaS, Fintech, Healthcare | Healthcare, Financial services, Energy |
| 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 | 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 |
Tensorway vs Quantiphi: 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 |
| 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 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 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: Tensorway vs Quantiphi
| Your situation | Recommended choice |
|---|---|
| You want a working engineer, not a recruiter, to run the technical screen | Tensorway |
| You need one specialist for a few days a week | Tensorway |
| You need several engineers working as one team | Quantiphi |
| You want to test an engineer before committing | Tensorway |
| Your budget is at the lower end | Compare: Tensorway (Not disclosed) 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: Tensorway vs Quantiphi
| Use case | Tensorway fit | Quantiphi 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 |
| 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: Tensorway vs Quantiphi
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.
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
Tensorway vs Quantiphi FAQ
Is Tensorway better than Quantiphi?
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. Quantiphi's strongest advantage: can staff several AI specialties in parallel, which no other AI-only firm here can.
How do Tensorway and Quantiphi 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. 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: Tensorway 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 Tensorway and Quantiphi?
Tensorway's primary differentiator is: senior AI engineers run a code review and a specialization-specific task on every candidate. 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 (50–249 vs 3,000–4,000+), minimum engagement (Not disclosed vs Not published), and primary industries served (SaaS, Fintech vs Healthcare, Financial services).
Verify all details directly with each company before making a decision.