Fuzzy Labs vs Pento: full comparison for 2026
Quick verdict
Fuzzy Labs (4.0/5) edges ahead of Pento (3.9/5) overall. Fuzzy Labs is the better choice for UK data science teams, including public sector, that need MLOps engineers working alongside them. 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.
Fuzzy Labs vs Pento: head-to-head summary
| Criterion | Fuzzy Labs | Pento |
|---|---|---|
| Founded | 2019 | 2019 |
| HQ | Manchester, UK | Montevideo, Uruguay |
| Team size | Under 50 (registry filing lists a micro company) | 10–49 |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Primary differentiator | Open-source MLOps specialists with security-cleared engineers for government work | AI-only engineering from Uruguay with full U.S. working-hour overlap |
| Pricing model | Day-rate or retainer per engineer; rates on request | Hourly or monthly per engineer; $50–$99/hr (Clutch band) |
| Min. engagement | Not published | $25,000+ (Clutch) |
| Primary tech stack | Python, Kubernetes, MLflow | Python, PyTorch, LangChain |
| Industries served | Public sector & policing, Startups, Enterprise | SaaS, E-commerce, Chemicals, Marketing technology |
Fuzzy Labs vs Pento: overview
Fuzzy Labs
Fuzzy Labs is a small MLOps consultancy incorporated in January 2019 and based at the GM Digital Security Hub in Manchester. It works side by side with data science teams to get models into production with less technical debt, describing itself as the client's in-house MLOps team and an extension of that team. Clients range from startups to policing and secure government work, and some roles require UK security clearance. The company says it doubled revenue in its most recent year and runs a fellowship to train new MLOps 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: Fuzzy Labs vs Pento
| Capability | Fuzzy Labs | 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: Fuzzy Labs vs Pento
| Framework / platform | Fuzzy Labs | Pento |
|---|---|---|
| PyTorch | N/A | ✓ |
| TensorFlow | N/A | N/A |
| LangChain | N/A | ✓ |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | ✓ |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Google Cloud | ✓ | ✓ |
| Databricks | N/A | N/A |
| MLflow | ✓ | N/A |
Pricing comparison: Fuzzy Labs vs Pento
| Criterion | Fuzzy Labs | 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: Fuzzy Labs vs Pento
| Dimension | Fuzzy Labs | Pento |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Public sector & policing, Startups, Enterprise | SaaS, E-commerce, Chemicals |
| Best use cases | Getting a police force's ML models into production, Adding an MLOps engineer to a startup's data science team | 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 |
Fuzzy Labs vs Pento: pros and cons
| Fuzzy Labs | |
|---|---|
| + | Security-cleared engineers can work in sensitive UK environments |
| + | Open-source tooling choices keep you free of vendor-specific platforms |
| + | Small team means you work directly with senior people |
| - | Very small; registry data lists eight employees, though the firm is hiring |
| - | MLOps only, so data scientists and LLM application developers come from elsewhere |
| - | UK-centric; limited overlap for U.S. or Asian teams |
| 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 Fuzzy Labs?
A typical fit: getting a police force's ML models into production.
Open-source MLOps specialists with security-cleared engineers for government work. Minimum engagement is not publicly disclosed. Works best with clients in Public sector & policing, Startups, Enterprise.
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: Fuzzy Labs 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; Fuzzy Labs 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: Fuzzy Labs (Not published) vs Pento ($25,000+ (Clutch)) |
| You need engineers deployed inside your organization | Fuzzy Labs |
| You need specialist depth in a specific vertical | Pento |
Use case fit: Fuzzy Labs vs Pento
| Use case | Fuzzy Labs fit | Pento fit | Winner |
|---|---|---|---|
| Getting a police force's ML models into production | Strong | Limited | Fuzzy Labs |
| Adding an MLOps engineer to a startup's data science team | 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 | Limited | Strong | Pento |
Verdict: Fuzzy Labs vs Pento
Fuzzy Labs (4.0/5) is the stronger overall choice for most AI-Native Staff Augmentation projects. Open-source MLOps specialists with security-cleared engineers for government work.
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
Fuzzy Labs vs Pento FAQ
Is Fuzzy Labs better than Pento?
Fuzzy Labs (4.0/5) scores higher overall, but "better" depends on your use case. Fuzzy Labs's strongest advantage: security-cleared engineers can work in sensitive UK environments. Pento's strongest advantage: same working day as U.S. East Coast teams.
How do Fuzzy Labs and Pento differ in pricing?
Fuzzy Labs uses day-rate or retainer per engineer; 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: Fuzzy Labs or Pento?
Pento 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 Fuzzy Labs and Pento?
Fuzzy Labs's primary differentiator is: open-source MLOps specialists with security-cleared engineers for government work. Pento's primary differentiator is: AI-only engineering from Uruguay with full U.S. working-hour overlap. They also differ in team size (Under 50 (registry filing lists a micro company) vs 10–49), minimum engagement (Not published vs $25,000+ (Clutch)), and primary industries served (Public sector & policing, Startups vs SaaS, E-commerce).
Verify all details directly with each company before making a decision.