InData Labs vs Howdy.com: full comparison for 2026
Quick verdict
InData Labs (4.4/5) edges ahead of Howdy.com (4.1/5) overall. InData Labs is the better choice for data-science-heavy teams, AWS-based ML work. Howdy.com is the stronger option for teams that want transparent nearshore pricing. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs Howdy.com: head-to-head summary
| Criterion | InData Labs | Howdy.com |
|---|---|---|
| Founded | 2014 | 2018 |
| HQ | Nicosia, Cyprus | Austin, Texas, USA |
| Team size | 50–249 | 100–249 |
| Rating | 4.4 / 5 | 4.1 / 5 |
| Primary differentiator | Data scientists and data engineers from one AI-only company | Published 15% fee on top of the engineer's pay |
| Pricing model | Dedicated team; time and materials; project budgets from under $50K per Clutch | Engineer salary plus a flat 15% service fee (published) |
| Min. engagement | Not disclosed | 1 engineer |
| Primary tech stack | Python, PyTorch, TensorFlow | Python, AWS, Databricks |
| Industries served | Healthcare, Fintech, Retail and e-commerce, Media | SaaS, Fintech, Healthcare, E-commerce |
InData Labs vs Howdy.com: overview
InData Labs
InData Labs was founded in 2014 and is headquartered in Nicosia, Cyprus, with offices in Vilnius and Miami. Its services include AI research and development, generative AI, predictive analytics, computer vision, data engineering, and a dedicated-team or staff-augmentation option. Clutch lists it as a certified AWS partner with 50–249 employees. Clutch reviewers single out its data-science and ML engineering skills.
Howdy.com
Howdy.com was founded in Austin, Texas, in 2018 by Jacqueline Samira, with Frank Licea later joining as co-founder and CTO. Unlike a pure marketplace, it recruits, employs and supports its engineers, and it runs in-country hubs it calls Howdy Houses in Latin American cities. In August 2023 it acquired the Brazilian talent marketplace GeekHunter, citing rising demand for AI and ML skills. A 2026 industry ranking lists its fee as a flat 15% of the billed rate.
Services and capabilities: InData Labs vs Howdy.com
| Capability | InData Labs | Howdy.com |
|---|---|---|
| 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: InData Labs vs Howdy.com
| Framework / platform | InData Labs | Howdy.com |
|---|---|---|
| PyTorch | ✓ | N/A |
| TensorFlow | ✓ | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | N/A | N/A |
| OpenAI | N/A | N/A |
| AWS SageMaker | ✓ | N/A |
| Azure ML | N/A | N/A |
| Databricks | N/A | ✓ |
| MLflow | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: InData Labs vs Howdy.com
| Criterion | InData Labs | Howdy.com |
|---|---|---|
| Minimum engagement | Not disclosed | 1 engineer |
| Engagement models | Dedicated team, Full-time dedicated engineers, Managed delivery | Full-time dedicated engineers, Dedicated team |
| Rate transparency | Not public | Minimum disclosed |
| Price tier | Mid-market | Accessible |
Target audience comparison: InData Labs vs Howdy.com
| Dimension | InData Labs | Howdy.com |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, Fintech, Retail and e-commerce | SaaS, Fintech, Healthcare |
| Best use cases | Adding an NLP engineer to a text-analytics product, Placing a computer-vision specialist for an image-recognition feature | Hiring a full-time LatAm data engineer with clear cost visibility, Building a two-to-four person nearshore team for a U.S. startup |
| Typical project type | Dedicated team | Full-time dedicated engineers |
InData Labs vs Howdy.com: pros and cons
| InData Labs | |
|---|---|
| + | AI and data are the whole business, so placed engineers come from a specialist bench |
| + | Combines NLP, computer vision and predictive analytics under one contract |
| + | AWS partnership is useful for SageMaker-based teams |
| + | EU-registered company, which simplifies contracting for European buyers |
| - | Smaller bench than nearshore generalists |
| - | Staff augmentation is a secondary offer next to project work |
| - | Limited time-zone overlap with the U.S. West Coast |
| Howdy.com | |
|---|---|
| + | Fee structure is published, so you can see what the engineer actually earns |
| + | Engineers are employed and supported locally, which helps retention |
| + | GeekHunter acquisition widened its Brazilian candidate pool |
| + | U.S. time zones |
| - | AI depth depends on who is available; it is a general engineering staffing company |
| - | Disclosure: acquired GeekHunter in 2023, so part of its candidate supply comes through a marketplace subsidiary |
| - | Technical screening is lighter than at AI-only companies |
Who should choose InData Labs?
A typical fit: adding an NLP engineer to a text-analytics product.
Data scientists and data engineers from one AI-only company. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Fintech, Retail and e-commerce, Media.
Who should choose Howdy.com?
A typical fit: hiring a full-time LatAm data engineer with clear cost visibility.
Published 15% fee on top of the engineer's pay. Minimum engagement starts at 1 engineer. Works best with clients in SaaS, Fintech, Healthcare, E-commerce.
Decision matrix: InData Labs vs Howdy.com
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Both offer fixed-price models |
| You need a large dedicated team for an ongoing programme | InData Labs |
| Your budget is at the lower end | Compare: InData Labs (Not disclosed) vs Howdy.com (1 engineer) |
| You need specialist depth in a specific vertical | InData Labs |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: InData Labs vs Howdy.com
| Use case | InData Labs fit | Howdy.com fit | Winner |
|---|---|---|---|
| Adding an NLP engineer to a text-analytics product | Strong | Strong | Both equally |
| Placing a computer-vision specialist for an image-recognition feature | Strong | Limited | InData Labs |
| Hiring a full-time LatAm data engineer with clear cost visibility | Limited | Strong | Howdy.com |
| Building a two-to-four person nearshore team for a U.S. startup | Strong | Strong | Both equally |
Verdict: InData Labs vs Howdy.com
InData Labs (4.4/5) is the stronger overall choice for most AI Staffing projects. Data scientists and data engineers from one AI-only company.
Howdy.com (4.1/5) is worth a look if you need building a two-to-four person nearshore team for a U.S. startup. If your situation matches that, Howdy.com is a competitive option.
Related comparisons
InData Labs vs Howdy.com FAQ
Is InData Labs better than Howdy.com?
InData Labs (4.4/5) scores higher overall, but "better" depends on your use case. InData Labs's strongest advantage: AI and data are the whole business, so placed engineers come from a specialist bench. Howdy.com's strongest advantage: fee structure is published, so you can see what the engineer actually earns.
How do InData Labs and Howdy.com differ in pricing?
InData Labs uses dedicated team; time and materials; project budgets from under $50k per clutch pricing. Howdy.com uses engineer salary plus a flat 15% service fee (published) pricing with a minimum engagement of 1 engineer. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: InData Labs or Howdy.com?
Howdy.com is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each agency before shortlisting.
What are the main differences between InData Labs and Howdy.com?
InData Labs's primary differentiator is: data scientists and data engineers from one AI-only company. Howdy.com's primary differentiator is: published 15% fee on top of the engineer's pay. They also differ in team size (50–249 vs 100–249), minimum engagement (Not disclosed vs 1 engineer), and primary industries served (Healthcare, Fintech vs SaaS, Fintech).
Verify all details directly with each agency before making a decision.