InData Labs vs Innowise: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.5/5) edges ahead of Innowise (3.7/5) overall. InData Labs is the better choice for finTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead.. Innowise is the stronger option for companies wanting a dedicated 300-person AI/data hub backed by the staffing depth of a 3,500+ engineer group.. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs Innowise: head-to-head summary
| Criterion | InData Labs | Innowise |
|---|---|---|
| Founded | 2014 | 2007 |
| HQ | Limassol, Cyprus | Warsaw, Poland |
| Team size | 50–100 | 3,500+ |
| Rating | 4.5 / 5 | 3.7 / 5 |
| Best for | FinTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead. | Companies wanting a dedicated 300-person AI/data hub backed by the staffing depth of a 3,500+ engineer group. |
| Pricing model | Project-based, dedicated team | Time & materials, dedicated team |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, TensorFlow, PyTorch | Python, AWS, Apache Spark |
| Industries served | FinTech, Healthcare, Retail & E-commerce, Logistics & Supply Chain | FinTech, Retail & E-commerce, Healthcare, Manufacturing |
InData Labs vs Innowise: overview
InData Labs
InData Labs was founded in 2014 by Marat Karpeko and is headquartered in Limassol, Cyprus, with additional offices in Lithuania and the United States. The company has stayed a pure-play AI/data-science consultancy for over a decade, building production ML systems for fintech, healthcare, SaaS, retail, and logistics clients, and is listed in Clutch's Top 10 AI Software Companies leaders matrix. At roughly 80 professionals, it is one of the smaller specialist firms in this list, trading scale for narrower focus.
Innowise
Innowise was founded in 2007 and is headquartered in Warsaw, Poland, with more than 3,500 vetted engineers on staff. The company's Data and AI hub reportedly unites 300+ specialists who have delivered 200+ AI-enabled projects, maintaining dedicated practices in machine learning, big data analytics, robotic process automation, and metaverse development. While the AI hub's 300-person headcount is sizable in absolute terms, it represents less than 10% of Innowise's total 3,500+ engineering staff, reflecting the company's broader identity as a general software engineering group.
Services and capabilities: InData Labs vs Innowise
| Capability | InData Labs | Innowise |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✓ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✗ | ✗ |
| Generative AI | ✓ | ✗ |
| AI Consulting | ✗ | ✗ |
Tech stack comparison: InData Labs vs Innowise
| Framework / platform | InData Labs | Innowise |
|---|---|---|
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Google Cloud | N/A | N/A |
| LangChain | N/A | N/A |
| Hugging Face | ✓ | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: InData Labs vs Innowise
| Criterion | InData Labs | Innowise |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Project-based, Dedicated team | Dedicated team, Time & materials, Staff augmentation |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: InData Labs vs Innowise
| Dimension | InData Labs | Innowise |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | FinTech, Healthcare, Retail & E-commerce | FinTech, Retail & E-commerce, Healthcare |
| Best use cases | FinTech company needs predictive analytics built by a team that has done nothing but AI/data science since 2014., Healthcare startup needs a computer vision model with a small, senior delivery team. | Company wants a dedicated AI/data hub with 200+ prior AI project deliveries, backed by a large staffing pool., Enterprise needs machine learning plus robotic process automation from a single large vendor. |
| Typical project type | Project-based | Dedicated team |
InData Labs vs Innowise: pros and cons
| InData Labs | |
|---|---|
| + | Has operated as a dedicated AI/data science firm since 2014 with no pivot to general software outsourcing. |
| + | Ranked in Clutch's Top 10 AI Software Companies leaders matrix. |
| + | Covers the full pipeline from data engineering through generative AI and computer vision, avoiding narrow single-service lock-in. |
| + | Smaller team size (~80) generally means less account-management overhead between client and engineers. |
| - | At roughly 80 people, InData Labs cannot staff large multi-workstream enterprise programs the way a 2,000+ person firm can. |
| - | Limassol, Cyprus HQ has a thinner regional case-study base in North America compared to US-headquartered peers. |
| Innowise | |
|---|---|
| + | 300+ person Data and AI hub is a specifically named, dedicated practice rather than an unstructured claim of AI capability. |
| + | 200+ AI-enabled projects delivered gives the AI hub a meaningful, quantified track record. |
| + | 3,500+ total engineers provide substantial staffing depth to scale an engagement quickly if needed. |
| + | 17 years of company history (since 2007) as an award-winning custom software developer with strong Clutch client reviews. |
| - | The 300-person AI hub represents a small fraction (well under 10%) of Innowise's total 3,500+ engineering staff — confirm the engagement is staffed from the AI hub specifically. |
| - | Broader company identity is general custom software development, with AI/ML as one of several practice areas (alongside RPA and metaverse development). |
Who should choose InData Labs?
InData Labs is the right choice for finTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead..
Ten-plus years as a pure-play AI/data-science firm with no general software-development sideline.. Minimum engagement starts at Not published. Works best with clients in FinTech, Healthcare, Retail & E-commerce, Logistics & Supply Chain.
Who should choose Innowise?
Innowise is the right choice for companies wanting a dedicated 300-person AI/data hub backed by the staffing depth of a 3,500+ engineer group..
A specifically named 300+ person Data and AI hub within a much larger 3,500+ engineer group, giving both focus and scale.. Minimum engagement starts at Not published. Works best with clients in FinTech, Retail & E-commerce, Healthcare, Manufacturing.
Decision matrix: InData Labs vs Innowise
| 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 published) vs Innowise (Not published) |
| You need specialist depth in a specific vertical | InData Labs |
| You need production MLOps support after model launch | Both offer MLOps support |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: InData Labs vs Innowise
| Use case | InData Labs fit | Innowise fit | Winner |
|---|---|---|---|
| FinTech company needs predictive analytics built by a team that has done nothing but AI/data science since 2014. | Strong | Limited | InData Labs |
| Healthcare startup needs a computer vision model with a small, senior delivery team. | Strong | Limited | InData Labs |
| Company wants a dedicated AI/data hub with 200+ prior AI project deliveries, backed by a large staffing pool. | Strong | Strong | Both equally |
| Enterprise needs machine learning plus robotic process automation from a single large vendor. | Limited | Strong | Innowise |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | Limited | Limited | Both equally |
Verdict: InData Labs vs Innowise
InData Labs (4.5/5) is the stronger overall choice for most Machine Learning Development projects. Ten-plus years as a pure-play AI/data-science firm with no general software-development sideline.. It is best for finTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead..
Innowise (3.7/5) is the better choice when companies wanting a dedicated 300-person AI/data hub backed by the staffing depth of a 3,500+ engineer group.. If your situation matches those criteria, Innowise is a competitive option.
Related comparisons
InData Labs vs Innowise FAQ
Is InData Labs better than Innowise?
InData Labs (4.5/5) scores higher overall, but "better" depends on your use case. InData Labs is better for finTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead.. Innowise is better for companies wanting a dedicated 300-person AI/data hub backed by the staffing depth of a 3,500+ engineer group..
How do InData Labs and Innowise differ in pricing?
InData Labs uses project-based, dedicated team pricing with a minimum engagement of Not published. Innowise uses time & materials, dedicated team pricing with a minimum engagement of Not published. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: InData Labs or Innowise?
InData Labs 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 InData Labs and Innowise?
InData Labs's primary differentiator is: ten-plus years as a pure-play ai/data-science firm with no general software-development sideline.. Innowise's primary differentiator is: a specifically named 300+ person data and ai hub within a much larger 3,500+ engineer group, giving both focus and scale.. They also differ in team size (50–100 vs 3,500+), minimum engagement (Not published vs Not published), and primary industries served (FinTech, Healthcare vs FinTech, Retail & E-commerce).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.