Tensorway vs Nearsure: full comparison for 2026
Quick verdict
Tensorway (4.4/5) edges ahead of Nearsure (3.9/5) overall. Tensorway is the better choice for product teams adding senior AI specialists without vendor lock-in. Nearsure is the stronger option for U.S. teams adding Latin American GenAI developers. The right choice depends on your project size, budget, and required tech stack.
Tensorway vs Nearsure: head-to-head summary
| Criterion | Tensorway | Nearsure |
|---|---|---|
| Founded | 2019 | 2018 |
| HQ | Alicante, Spain | Montevideo, Uruguay (U.S.-incorporated) |
| Team size | 50–249 | 500–850 (sources vary) |
| Rating | 4.4 / 5 | 3.9 / 5 |
| Primary differentiator | Senior AI engineers run the technical screening, and every model and line of code stays in the client's repositories | Augmentation-first business model with a growing AI studio |
| Pricing model | Monthly rate for full-time dedicated engineers; hourly or weekly billing for part-time fractional experts; two-week trial sprint; rate card on request | Monthly staff augmentation rates; project development; rates on request |
| Min. engagement | Not disclosed | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, OpenAI, AWS |
| Industries served | Financial services, Software & SaaS, Healthcare & life sciences, Logistics, Manufacturing | Software & SaaS, Healthcare & life sciences, Financial services |
Tensorway vs Nearsure: overview
Tensorway
Tensorway, founded in 2019 and based in Alicante, Spain, supplies AI engineers who join a client's own team and work inside its Slack, Jira and version control under its coding standards. The firm has more than 20 years of software engineering practice behind its delivery methods. Its central promise concerns ownership: code, documentation and trained models stay in the client's repositories, and knowledge transfer to in-house staff is part of every engagement (per company website; independently unverifiable). Available roles include LLM engineers, RAG specialists, MLOps architects, computer-vision and NLP engineers, with teams usually starting as a squad of two to five. In one published case, a U.S. trading platform serving more than 100,000 investors reports 40% faster market-data processing and 35% lower operating costs (per company website; independently unverifiable).
Nearsure
Nearsure started operations in 2018 under co-founder and CEO Giuliana Corbo and is described by Bloomberg as a Uruguayan IT services company, though it is incorporated in the United States. Bloomberg reported a 2024 plan to grow to about 850 staff. Remote staff augmentation for U.S. clients is its core business, and the service list has widened to generative AI, cloud migration and Salesforce work through a Data & AI studio.
Services and capabilities: Tensorway vs Nearsure
| Capability | Tensorway | Nearsure |
|---|---|---|
| LLM / GenAI engineers | ✓ | ✓ |
| MLOps & deployment | ✓ | ✗ |
| Computer vision | ✓ | ✗ |
| Data engineering | ✗ | ✓ |
| AI agent development | ✗ | ✗ |
| Fractional / part-time experts | ✓ | ✗ |
| Risk-free trial period | ✓ | ✗ |
| Nearshore time-zone overlap | ✗ | ✓ |
Tech stack comparison: Tensorway vs Nearsure
| Framework / platform | Tensorway | Nearsure |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | ✓ | N/A |
| OpenAI | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Databricks | N/A | N/A |
| MLflow | ✓ | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: Tensorway vs Nearsure
| Criterion | Tensorway | Nearsure |
|---|---|---|
| Minimum engagement | Not disclosed | Not published |
| Engagement models | Full-time dedicated engineers, Part-time fractional experts, Trial period | Full-time dedicated engineers, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Tensorway vs Nearsure
| Dimension | Tensorway | Nearsure |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial services, Software & SaaS, Healthcare & life sciences | Software & SaaS, Healthcare & life sciences, Financial services |
| Best use cases | Adding RAG and evaluation expertise to a SaaS team shipping its first LLM feature, Bringing GPU inference costs under control for a production model | Adding a GenAI developer to a U.S. SaaS team, Staffing data engineers for a cloud migration |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
Tensorway vs Nearsure: pros and cons
| Tensorway | |
|---|---|
| + | Candidates pass a code review, a practical task in their specialty and a communication check run by senior AI engineers |
| + | Clients keep all code, documentation and trained models in their own repositories |
| + | First engineer typically starts in one to two weeks and a full squad in three to four (per company website; independently unverifiable) |
| + | Engineers bring GPU and inference cost control, fine-tuning and vector-database experience |
| + | Commitment is monthly and can be adjusted between sprints, with no-cost replacement for a poor fit |
| - | No public rate card, so budgeting starts with a sales call |
| - | Its bench is far smaller than EPAM's or Turing's, which limits how many engineers can start at once |
| - | Only AI and ML roles are offered, so general full-stack or QA staffing has to come from elsewhere |
| Nearsure | |
|---|---|
| + | Staff augmentation is the main business, so processes are built around it |
| + | Latin American engineers on U.S. hours |
| + | Has been profitable since early in its history, per AméricaEconomía |
| - | AI is a newer studio inside a general staffing company |
| - | Headcount reports vary between 525 and 850 |
| - | HQ location differs between sources |
Who should choose Tensorway?
A typical fit: adding RAG and evaluation expertise to a SaaS team shipping its first LLM feature.
Senior AI engineers run the technical screening, and every model and line of code stays in the client's repositories. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Software & SaaS, Healthcare & life sciences, Logistics, Manufacturing.
Who should choose Nearsure?
A typical fit: adding a GenAI developer to a U.S. SaaS team.
Augmentation-first business model with a growing AI studio. Minimum engagement is not publicly disclosed. Works best with clients in Software & SaaS, Healthcare & life sciences, Financial services.
Decision matrix: Tensorway vs Nearsure
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Nearsure |
| You want the supplier to own delivery as well as staffing | Neither offers managed delivery; you will lead the work |
| You need one expert part-time | Tensorway |
| You want to test an engineer before signing for months | Tensorway |
| Your budget is at the lower end | Compare: Tensorway (Not disclosed) vs Nearsure (Not published) |
| You need overlap with U.S. working hours | Nearsure |
| You need specialist depth in a specific vertical | Tensorway |
Use case fit: Tensorway vs Nearsure
| Use case | Tensorway fit | Nearsure fit | Winner |
|---|---|---|---|
| Adding RAG and evaluation expertise to a SaaS team shipping its first LLM feature | Strong | Strong | Both equally |
| Bringing GPU inference costs under control for a production model | Strong | Limited | Tensorway |
| Adding a GenAI developer to a U.S. SaaS team | Strong | Strong | Both equally |
| Staffing data engineers for a cloud migration | Strong | Strong | Both equally |
Verdict: Tensorway vs Nearsure
Tensorway (4.4/5) is the stronger overall choice for most AI Staff Augmentation projects. Senior AI engineers run the technical screening, and every model and line of code stays in the client's repositories.
Nearsure (3.9/5) is worth a look if you need staffing data engineers for a cloud migration. If your situation matches that, Nearsure is a competitive option.
Related comparisons
Tensorway vs Nearsure FAQ
Is Tensorway better than Nearsure?
Tensorway (4.4/5) scores higher overall, but "better" depends on your use case. Tensorway's strongest advantage: candidates pass a code review, a practical task in their specialty and a communication check run by senior AI engineers. Nearsure's strongest advantage: staff augmentation is the main business, so processes are built around it.
How do Tensorway and Nearsure differ in pricing?
Tensorway uses monthly rate for full-time dedicated engineers; hourly or weekly billing for part-time fractional experts; two-week trial sprint; rate card on request pricing. Nearsure uses monthly staff augmentation rates; project development; 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 Nearsure?
Nearsure 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 Nearsure?
Tensorway's primary differentiator is: senior AI engineers run the technical screening, and every model and line of code stays in the client's repositories. Nearsure's primary differentiator is: augmentation-first business model with a growing AI studio. They also differ in team size (50–249 vs 500–850 (sources vary)), minimum engagement (Not disclosed vs Not published), and primary industries served (Financial services, Software & SaaS vs Software & SaaS, Healthcare & life sciences).
Verify all details directly with each company before making a decision.