Tensorway vs Azumo: full comparison for 2026
Quick verdict
Tensorway (4.4/5) edges ahead of Azumo (4.1/5) overall. Tensorway is the better choice for product teams adding senior AI specialists without vendor lock-in. Azumo is the stronger option for nearshore LLM and NLP builds for U.S. mid-market. The right choice depends on your project size, budget, and required tech stack.
Tensorway vs Azumo: head-to-head summary
| Criterion | Tensorway | Azumo |
|---|---|---|
| Founded | 2019 | 2016 |
| HQ | Alicante, Spain | San Francisco, California, USA |
| Team size | 50–249 | 100–500 (sources vary) |
| Rating | 4.4 / 5 | 4.1 / 5 |
| Primary differentiator | Senior AI engineers run the technical screening, and every model and line of code stays in the client's repositories | A nearshore team that also builds its own NLP products |
| 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 rates for augmented engineers; dedicated teams; project pricing; rates on request |
| Min. engagement | Not disclosed | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, LangChain, OpenAI |
| Industries served | Financial services, Software & SaaS, Healthcare & life sciences, Logistics, Manufacturing | Healthcare & life sciences, Media, Software & SaaS, Financial services |
Tensorway vs Azumo: 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).
Azumo
Azumo is headquartered in San Francisco and has built AI-driven applications since 2016, with most of its engineers in Latin America. Directory headcounts range from under 100 to several hundred people. It offers staff augmentation, dedicated teams and full product outsourcing, and it also maintains its own AI products, including an NLU toolkit. Named clients include Meta and UnitedHealth (per company website; independently unverifiable).
Services and capabilities: Tensorway vs Azumo
| Capability | Tensorway | Azumo |
|---|---|---|
| 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 Azumo
| Framework / platform | Tensorway | Azumo |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | ✓ | ✓ |
| Hugging Face | ✓ | ✓ |
| OpenAI | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Databricks | N/A | N/A |
| MLflow | ✓ | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: Tensorway vs Azumo
| Criterion | Tensorway | Azumo |
|---|---|---|
| Minimum engagement | Not disclosed | Not published |
| Engagement models | Full-time dedicated engineers, Part-time fractional experts, Trial period | Full-time dedicated engineers, Dedicated team, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Tensorway vs Azumo
| Dimension | Tensorway | Azumo |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial services, Software & SaaS, Healthcare & life sciences | Healthcare & life sciences, Media, Software & SaaS |
| 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 conversational-AI engineer to a healthcare app team, Building a document-search assistant on internal knowledge |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
Tensorway vs Azumo: 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 |
| Azumo | |
|---|---|
| + | Its own AI products show applied NLP experience |
| + | Latin American engineers share U.S. working hours |
| + | Flexible mix of augmentation and project delivery |
| - | Headcount reports vary widely, so ask how many AI engineers are actually on staff |
| - | Smaller bench than the large nearshore firms on this list |
| - | Founding year differs across sources (2013 or 2016) |
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 Azumo?
A typical fit: adding a conversational-AI engineer to a healthcare app team.
A nearshore team that also builds its own NLP products. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare & life sciences, Media, Software & SaaS, Financial services.
Decision matrix: Tensorway vs Azumo
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Azumo |
| You want the supplier to own delivery as well as staffing | Azumo |
| 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 Azumo (Not published) |
| You need overlap with U.S. working hours | Azumo |
| You need specialist depth in a specific vertical | Tensorway |
Use case fit: Tensorway vs Azumo
| Use case | Tensorway fit | Azumo 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 conversational-AI engineer to a healthcare app team | Strong | Strong | Both equally |
| Building a document-search assistant on internal knowledge | Limited | Strong | Azumo |
Verdict: Tensorway vs Azumo
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.
Azumo (4.1/5) is worth a look if you need building a document-search assistant on internal knowledge. If your situation matches that, Azumo is a competitive option.
Related comparisons
Tensorway vs Azumo FAQ
Is Tensorway better than Azumo?
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. Azumo's strongest advantage: its own AI products show applied NLP experience.
How do Tensorway and Azumo 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. Azumo uses monthly rates for augmented engineers; dedicated teams; project pricing; 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 Azumo?
Azumo 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 Azumo?
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. Azumo's primary differentiator is: a nearshore team that also builds its own NLP products. They also differ in team size (50–249 vs 100–500 (sources vary)), minimum engagement (Not disclosed vs Not published), and primary industries served (Financial services, Software & SaaS vs Healthcare & life sciences, Media).
Verify all details directly with each company before making a decision.