Azumo vs InData Labs: full comparison for 2026
Quick verdict
Azumo (4.5/5) edges ahead of InData Labs (4.4/5) overall. Azumo is the better choice for startups adding GenAI engineers on U.S. hours. InData Labs is the stronger option for data-science-heavy teams, AWS-based ML work. The right choice depends on your project size, budget, and required tech stack.
Azumo vs InData Labs: head-to-head summary
| Criterion | Azumo | InData Labs |
|---|---|---|
| Founded | 2016 | 2014 |
| HQ | San Francisco, USA | Nicosia, Cyprus |
| Team size | 100–249 | 50–249 |
| Rating | 4.5 / 5 | 4.4 / 5 |
| Primary differentiator | Nearshore staffing with a hiring focus on GenAI and agent roles | Data scientists and data engineers from one AI-only company |
| Pricing model | Monthly per engineer; dedicated team; project-based; rates on request | Dedicated team; time and materials; project budgets from under $50K per Clutch |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | OpenAI, LangChain, Hugging Face | Python, PyTorch, TensorFlow |
| Industries served | SaaS, Fintech, Healthcare, Media | Healthcare, Fintech, Retail and e-commerce, Media |
Azumo vs InData Labs: overview
Azumo
Azumo was founded in San Francisco in 2016 by former investment banker Chike Agbai, whose first client was Twitter. Its engineers are based in more than 20 Latin American countries and work U.S. hours. The company sells three formats: staff augmentation alongside an existing team, dedicated teams, and project delivery, and its recent hiring is weighted toward generative-AI, agent and forward-deployed engineering roles. Headcount estimates range from about 80 to just over 100 depending on the source.
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.
Services and capabilities: Azumo vs InData Labs
| Capability | Azumo | InData Labs |
|---|---|---|
| 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: Azumo vs InData Labs
| Framework / platform | Azumo | InData Labs |
|---|---|---|
| PyTorch | N/A | ✓ |
| TensorFlow | N/A | ✓ |
| LangChain | ✓ | N/A |
| Hugging Face | ✓ | N/A |
| OpenAI | ✓ | N/A |
| AWS SageMaker | N/A | ✓ |
| Azure ML | ✓ | N/A |
| Databricks | N/A | N/A |
| MLflow | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Azumo vs InData Labs
| Criterion | Azumo | InData Labs |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | Dedicated team, Full-time dedicated engineers, Managed delivery |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Azumo vs InData Labs
| Dimension | Azumo | InData Labs |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | SaaS, Fintech, Healthcare | Healthcare, Fintech, Retail and e-commerce |
| Best use cases | Adding an LLM engineer to ship a first GenAI feature, Hiring an agent developer to prototype internal automation | Adding an NLP engineer to a text-analytics product, Placing a computer-vision specialist for an image-recognition feature |
| Typical project type | Full-time dedicated engineers | Dedicated team |
Azumo vs InData Labs: pros and cons
| Azumo | |
|---|---|
| + | Hiring pattern shows real investment in LLM and agent engineering, beyond generic web developers |
| + | No long-term commitment required for augmentation seats |
| + | U.S. time zones and a U.S.-based management team |
| + | Small enough that founders and senior staff stay involved in client accounts |
| - | Headcount is modest, so very large teams may take longer to assemble |
| - | Public detail on how candidates are technically screened is thin |
| - | No published rates |
| 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 |
Who should choose Azumo?
A typical fit: adding an LLM engineer to ship a first GenAI feature.
Nearshore staffing with a hiring focus on GenAI and agent roles. Minimum engagement is not publicly disclosed. Works best with clients in SaaS, Fintech, Healthcare, Media.
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.
Decision matrix: Azumo vs InData Labs
| 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 | Azumo |
| Your budget is at the lower end | Compare: Azumo (Not disclosed) vs InData Labs (Not disclosed) |
| You need specialist depth in a specific vertical | Azumo |
| 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: Azumo vs InData Labs
| Use case | Azumo fit | InData Labs fit | Winner |
|---|---|---|---|
| Adding an LLM engineer to ship a first GenAI feature | Strong | Strong | Both equally |
| Hiring an agent developer to prototype internal automation | Strong | Limited | Azumo |
| Adding an NLP engineer to a text-analytics product | Strong | Strong | Both equally |
| Placing a computer-vision specialist for an image-recognition feature | Limited | Strong | InData Labs |
Verdict: Azumo vs InData Labs
Azumo (4.5/5) is the stronger overall choice for most AI Staffing projects. Nearshore staffing with a hiring focus on GenAI and agent roles.
InData Labs (4.4/5) is worth a look if you need placing a computer-vision specialist for an image-recognition feature. If your situation matches that, InData Labs is a competitive option.
Related comparisons
Azumo vs InData Labs FAQ
Is Azumo better than InData Labs?
Azumo (4.5/5) scores higher overall, but "better" depends on your use case. Azumo's strongest advantage: hiring pattern shows real investment in LLM and agent engineering, beyond generic web developers. InData Labs's strongest advantage: AI and data are the whole business, so placed engineers come from a specialist bench.
How do Azumo and InData Labs differ in pricing?
Azumo uses monthly per engineer; dedicated team; project-based; rates on request pricing. InData Labs uses dedicated team; time and materials; project budgets from under $50k per clutch pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Azumo or InData Labs?
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 agency before shortlisting.
What are the main differences between Azumo and InData Labs?
Azumo's primary differentiator is: nearshore staffing with a hiring focus on GenAI and agent roles. InData Labs's primary differentiator is: data scientists and data engineers from one AI-only company. They also differ in team size (100–249 vs 50–249), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (SaaS, Fintech vs Healthcare, Fintech).
Verify all details directly with each agency before making a decision.