Azumo vs InData Labs: full comparison for 2026
Quick verdict
Azumo (4.1/5) edges ahead of InData Labs (4.1/5) overall. Azumo is the better choice for nearshore LLM and NLP builds for U.S. mid-market. InData Labs is the stronger option for mid-sized companies adding data scientists to product teams. 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, California, USA | Nicosia, Cyprus |
| Team size | 100–500 (sources vary) | 50–249 |
| Rating | 4.1 / 5 | 4.1 / 5 |
| Primary differentiator | A nearshore team that also builds its own NLP products | A data-science-only firm small enough that senior staff stay involved |
| Pricing model | Monthly rates for augmented engineers; dedicated teams; project pricing; rates on request | Time and materials; dedicated engineers; rates on request |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, LangChain, OpenAI | Python, PyTorch, TensorFlow |
| Industries served | Healthcare & life sciences, Media, Software & SaaS, Financial services | Retail & e-commerce, Healthcare & life sciences, Financial services, Media |
Azumo vs InData Labs: overview
Azumo
Azumo is headquartered in San Francisco and has built AI-driven applications since 2016, with most of its engineers in Latin America. Directory headcounts range from under 100 to several hundred people. It offers staff augmentation, dedicated teams and full product outsourcing, and it also maintains its own AI products, including an NLU toolkit. Named clients include Meta and UnitedHealth (per company website; independently unverifiable).
InData Labs
InData Labs was founded in 2014 and is headquartered in Nicosia, Cyprus, with additional locations including Vilnius and Miami. Most directories put its headcount below 250 people. The company works only on data science and AI, covering predictive analytics, NLP, computer vision and generative AI, and it supplies engineers to client teams as well as delivering projects.
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 | ✓ | ✓ |
| Azure | ✓ | 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 published | Not published |
| Engagement models | Full-time dedicated engineers, Dedicated team, Managed delivery | 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 | Healthcare & life sciences, Media, Software & SaaS | Retail & e-commerce, Healthcare & life sciences, Financial services |
| Best use cases | Adding a conversational-AI engineer to a healthcare app team, Building a document-search assistant on internal knowledge | Adding a computer-vision engineer to a retail analytics team, Building churn and demand models with in-house analysts |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
Azumo vs InData Labs: pros and cons
| Azumo | |
|---|---|
| + | Its own AI products show applied NLP experience |
| + | Latin American engineers share U.S. working hours |
| + | Flexible mix of augmentation and project delivery |
| - | Headcount reports vary widely, so ask how many AI engineers are actually on staff |
| - | Smaller bench than the large nearshore firms on this list |
| - | Founding year differs across sources (2013 or 2016) |
| InData Labs | |
|---|---|
| + | Data science and AI are its only line of work |
| + | Experience across vision, language and predictive models |
| + | Clients deal with a small firm where senior staff stay close to the work |
| - | Headcount estimates vary widely, so confirm bench depth for your role |
| - | Limited capacity for large multi-team programs |
| - | Less visible LLM-agent work than some newer specialists |
Who should choose Azumo?
A typical fit: adding a conversational-AI engineer to a healthcare app team.
A nearshore team that also builds its own NLP products. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare & life sciences, Media, Software & SaaS, Financial services.
Who should choose InData Labs?
A typical fit: adding a computer-vision engineer to a retail analytics team.
A data-science-only firm small enough that senior staff stay involved. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, Healthcare & life sciences, Financial services, Media.
Decision matrix: Azumo vs InData Labs
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | Azumo |
| You want the supplier to own delivery as well as staffing | Both; Azumo rates higher overall |
| You need one expert part-time | Neither lists part-time experts; ask about reduced hours |
| You want to test an engineer before signing for months | Neither publishes a trial; ask for a short first term |
| Your budget is at the lower end | Compare: Azumo (Not published) vs InData Labs (Not published) |
| You need overlap with U.S. working hours | Azumo |
| You need specialist depth in a specific vertical | Azumo |
Use case fit: Azumo vs InData Labs
| Use case | Azumo fit | InData Labs fit | Winner |
|---|---|---|---|
| Adding a conversational-AI engineer to a healthcare app team | Strong | Strong | Both equally |
| Building a document-search assistant on internal knowledge | Strong | Strong | Both equally |
| Adding a computer-vision engineer to a retail analytics team | Strong | Strong | Both equally |
| Building churn and demand models with in-house analysts | Strong | Strong | Both equally |
Verdict: Azumo vs InData Labs
Azumo (4.1/5) is the stronger overall choice for most AI Staff Augmentation projects. A nearshore team that also builds its own NLP products.
InData Labs (4.1/5) is worth a look if you need building churn and demand models with in-house analysts. 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 (4.1/5) scores higher overall, but "better" depends on your use case. Azumo's strongest advantage: its own AI products show applied NLP experience. InData Labs's strongest advantage: data science and AI are its only line of work.
How do Azumo and InData Labs differ in pricing?
Azumo uses monthly rates for augmented engineers; dedicated teams; project pricing; rates on request pricing. InData Labs uses time and materials; dedicated engineers; 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: 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 company before shortlisting.
What are the main differences between Azumo and InData Labs?
Azumo's primary differentiator is: a nearshore team that also builds its own NLP products. InData Labs's primary differentiator is: a data-science-only firm small enough that senior staff stay involved. They also differ in team size (100–500 (sources vary) vs 50–249), minimum engagement (Not published vs Not published), and primary industries served (Healthcare & life sciences, Media vs Retail & e-commerce, Healthcare & life sciences).
Verify all details directly with each company before making a decision.