Tensorway vs Revelo: full comparison for 2026
Quick verdict
Tensorway (4.4/5) edges ahead of Revelo (3.8/5) overall. Tensorway is the better choice for product teams adding senior AI specialists without vendor lock-in. Revelo is the stronger option for hiring Latin American developers through a marketplace. The right choice depends on your project size, budget, and required tech stack.
Tensorway vs Revelo: head-to-head summary
| Criterion | Tensorway | Revelo |
|---|---|---|
| Founded | 2019 | 2014 |
| HQ | Alicante, Spain | São Paulo, Brazil |
| Team size | 50–249 | 400,000+ developer network (per company) |
| Rating | 4.4 / 5 | 3.8 / 5 |
| Primary differentiator | Senior AI engineers run the technical screening, and every model and line of code stays in the client's repositories | A very large Latin American pool with payroll and compliance included |
| 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 | Marketplace placement with monthly billing; rates on request |
| Min. engagement | Not disclosed | Not published |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, OpenAI, Hugging Face |
| Industries served | Financial services, Software & SaaS, Healthcare & life sciences, Logistics, Manufacturing | Software & SaaS, AI research labs, Financial services |
Tensorway vs Revelo: 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).
Revelo
Revelo was founded in late 2014 in Brazil (some sources say 2015) and began as a domestic hiring platform called Contratado. It now runs a network of more than 400,000 Latin American developers and handles hiring and payment for U.S. customers. TechCrunch reported that work on foundation models made up 22% of Revelo's revenue in 2024. Revelo is a marketplace, so engineers are matched through its platform rather than employed in a delivery center.
Services and capabilities: Tensorway vs Revelo
| Capability | Tensorway | Revelo |
|---|---|---|
| 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 Revelo
| Framework / platform | Tensorway | Revelo |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | N/A |
| LangChain | ✓ | N/A |
| Hugging Face | ✓ | ✓ |
| OpenAI | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Databricks | N/A | N/A |
| MLflow | ✓ | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: Tensorway vs Revelo
| Criterion | Tensorway | Revelo |
|---|---|---|
| Minimum engagement | Not disclosed | Not published |
| Engagement models | Full-time dedicated engineers, Part-time fractional experts, Trial period | Full-time dedicated engineers |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Tensorway vs Revelo
| Dimension | Tensorway | Revelo |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Financial services, Software & SaaS, Healthcare & life sciences | Software & SaaS, AI research labs, 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 | Hiring LLM data specialists for a model-training effort, Adding a Brazilian developer to a U.S. SaaS team |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
Tensorway vs Revelo: 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 |
| Revelo | |
|---|---|
| + | Very large pool across Latin America |
| + | Handles hiring, payroll and compliance |
| + | Foundation-model work gives some engineers LLM training experience |
| - | Marketplace matching means quality varies by candidate |
| - | Founding year is reported as both 2014 and 2015 |
| - | Less hands-on management than employer-based firms |
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 Revelo?
A typical fit: hiring LLM data specialists for a model-training effort.
A very large Latin American pool with payroll and compliance included. Minimum engagement is not publicly disclosed. Works best with clients in Software & SaaS, AI research labs, Financial services.
Decision matrix: Tensorway vs Revelo
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Confirm how many engineers each can staff at once |
| 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 Revelo (Not published) |
| You need overlap with U.S. working hours | Revelo |
| You need specialist depth in a specific vertical | Tensorway |
Use case fit: Tensorway vs Revelo
| Use case | Tensorway fit | Revelo 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 |
| Hiring LLM data specialists for a model-training effort | Limited | Strong | Revelo |
| Adding a Brazilian developer to a U.S. SaaS team | Strong | Strong | Both equally |
Verdict: Tensorway vs Revelo
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.
Revelo (3.8/5) is worth a look if you need adding a Brazilian developer to a U.S. SaaS team. If your situation matches that, Revelo is a competitive option.
Related comparisons
Tensorway vs Revelo FAQ
Is Tensorway better than Revelo?
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. Revelo's strongest advantage: very large pool across Latin America.
How do Tensorway and Revelo 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. Revelo uses marketplace placement with monthly billing; 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 Revelo?
Revelo 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 Revelo?
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. Revelo's primary differentiator is: a very large Latin American pool with payroll and compliance included. They also differ in team size (50–249 vs 400,000+ developer network (per company)), minimum engagement (Not disclosed vs Not published), and primary industries served (Financial services, Software & SaaS vs Software & SaaS, AI research labs).
Verify all details directly with each company before making a decision.