Addepto vs Pento: full comparison for 2026
Quick verdict
Addepto (3.9/5) edges ahead of Pento (3.9/5) overall. Addepto is the better choice for industrial and automotive companies adding AI and data engineers to an internal team. Pento is the stronger option for U.S. startups and mid-market firms that want nearshore ML engineers on their hours. The right choice depends on your project size, budget, and required tech stack.
Addepto vs Pento: head-to-head summary
| Criterion | Addepto | Pento |
|---|---|---|
| Founded | 2017 | 2019 |
| HQ | Warsaw, Poland | Montevideo, Uruguay |
| Team size | 50–99 (directory estimate) | 10–49 |
| Rating | 3.9 / 5 | 3.9 / 5 |
| Primary differentiator | AI-heavy team with manufacturing domain experience, now backed by a larger group | AI-only engineering from Uruguay with full U.S. working-hour overlap |
| Pricing model | Collaborative team model or managed delivery; rates on request | Hourly or monthly per engineer; $50–$99/hr (Clutch band) |
| Min. engagement | Not published | $25,000+ (Clutch) |
| Primary tech stack | Python, Databricks, Spark | Python, PyTorch, LangChain |
| Industries served | Manufacturing, Automotive, Retail, Aviation | SaaS, E-commerce, Chemicals, Marketing technology |
Addepto vs Pento: overview
Addepto
Addepto has worked on AI and data in Warsaw since 2017, with a strong client base in industrial and automotive companies. KMS Technology, an Atlanta engineering firm backed by Sunstone Partners, acquired it in December 2025. Its collaborative cooperation model puts Addepto engineers alongside the client's own team, and the company has said publicly it is not a body-leasing firm. After the deal, its CEO said 97% of the team are AI engineers.
Pento
Pento is a Uruguayan AI and machine learning engineering firm founded in 2019, with roughly 25 to 50 people in Montevideo. Team augmentation is one of its two most common engagement types, and directory data puts its average team at about two and a half people with roughly three weeks to hire. DesignRush lists Mercado Libre and BASF among its clients. Montevideo is one to two hours ahead of U.S. Eastern time, so working days overlap almost completely.
Services and capabilities: Addepto vs Pento
| Capability | Addepto | Pento |
|---|---|---|
| LLM / GenAI engineers | ✓ | ✓ |
| AI agent development | ✗ | ✗ |
| MLOps & deployment | ✗ | ✗ |
| Computer vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Data engineering | ✓ | ✗ |
| Fractional / part-time experts | ✗ | ✗ |
| Trial before commitment | ✗ | ✗ |
| Forward-deployed engineers | ✗ | ✗ |
| Access to a wider AI talent network | ✗ | ✗ |
Tech stack comparison: Addepto vs Pento
| Framework / platform | Addepto | Pento |
|---|---|---|
| PyTorch | N/A | ✓ |
| TensorFlow | N/A | N/A |
| LangChain | ✓ | ✓ |
| Hugging Face | N/A | N/A |
| OpenAI | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Google Cloud | N/A | ✓ |
| Databricks | ✓ | N/A |
| MLflow | N/A | N/A |
Pricing comparison: Addepto vs Pento
| Criterion | Addepto | Pento |
|---|---|---|
| Minimum engagement | Not published | $25,000+ (Clutch) |
| Engagement models | Embedded team, Project delivery | Dedicated engineers, Project delivery |
| Rate transparency | Not public | Minimum disclosed |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Addepto vs Pento
| Dimension | Addepto | Pento |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Manufacturing, Automotive, Retail | SaaS, E-commerce, Chemicals |
| Best use cases | Adding Databricks engineers to a manufacturer's data team, Building a GenAI assistant for automotive service documents | Adding an ML engineer to a U.S. SaaS team, Building an LLM feature with a two-person nearshore squad |
| Typical project type | Embedded team | Dedicated engineers |
Addepto vs Pento: pros and cons
| Addepto | |
|---|---|
| + | Nearly the whole team is AI engineers, according to its CEO |
| + | Industrial and automotive client experience |
| + | KMS ownership adds broader engineering capacity behind it |
| - | Acquired by KMS Technology in December 2025; ownership changes can bring new contract terms |
| - | Prefers joint delivery to straight staff placement |
| - | Team size estimates range from 8 to 99 |
| Pento | |
|---|---|
| + | Same working day as U.S. East Coast teams |
| + | Published rate band, unusual for this list |
| + | Reviewers praise value for cost and responsiveness |
| - | Very small team, so only a few engineers can join at once |
| - | Few public reviews to judge consistency |
| - | One reviewer wanted clearer project timelines |
Who should choose Addepto?
A typical fit: adding Databricks engineers to a manufacturer's data team.
AI-heavy team with manufacturing domain experience, now backed by a larger group. Minimum engagement is not publicly disclosed. Works best with clients in Manufacturing, Automotive, Retail, Aviation.
Who should choose Pento?
A typical fit: adding an ML engineer to a U.S. SaaS team.
AI-only engineering from Uruguay with full U.S. working-hour overlap. Minimum engagement starts at $25,000+ (Clutch). Works best with clients in SaaS, E-commerce, Chemicals, Marketing technology.
Decision matrix: Addepto vs Pento
| Your situation | Recommended choice |
|---|---|
| You need one AI specialist part-time | Neither advertises part-time experts; ask about reduced hours |
| You need several engineers working as one team | Both; Addepto rates higher overall |
| You want to test an engineer before committing | Neither publishes a trial; negotiate a short first term |
| Your budget is at the lower end | Compare: Addepto (Not published) vs Pento ($25,000+ (Clutch)) |
| You need engineers deployed inside your organization | Addepto |
| You need specialist depth in a specific vertical | Addepto |
Use case fit: Addepto vs Pento
| Use case | Addepto fit | Pento fit | Winner |
|---|---|---|---|
| Adding Databricks engineers to a manufacturer's data team | Strong | Strong | Both equally |
| Building a GenAI assistant for automotive service documents | Strong | Strong | Both equally |
| Adding an ML engineer to a U.S. SaaS team | Strong | Strong | Both equally |
| Building an LLM feature with a two-person nearshore squad | Strong | Strong | Both equally |
Verdict: Addepto vs Pento
Addepto (3.9/5) is the stronger overall choice for most AI-Native Staff Augmentation projects. AI-heavy team with manufacturing domain experience, now backed by a larger group.
Pento (3.9/5) is worth a look if you need building an LLM feature with a two-person nearshore squad. If your situation matches that, Pento is a competitive option.
Related comparisons
Addepto vs Pento FAQ
Is Addepto better than Pento?
Addepto (3.9/5) scores higher overall, but "better" depends on your use case. Addepto's strongest advantage: nearly the whole team is AI engineers, according to its CEO. Pento's strongest advantage: same working day as U.S. East Coast teams.
How do Addepto and Pento differ in pricing?
Addepto uses collaborative team model or managed delivery; rates on request pricing. Pento uses hourly or monthly per engineer; $50–$99/hr (clutch band) pricing with a minimum engagement of $25,000+ (Clutch). Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Addepto or Pento?
Addepto 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 Addepto and Pento?
Addepto's primary differentiator is: AI-heavy team with manufacturing domain experience, now backed by a larger group. Pento's primary differentiator is: AI-only engineering from Uruguay with full U.S. working-hour overlap. They also differ in team size (50–99 (directory estimate) vs 10–49), minimum engagement (Not published vs $25,000+ (Clutch)), and primary industries served (Manufacturing, Automotive vs SaaS, E-commerce).
Verify all details directly with each company before making a decision.