InData Labs vs SoftServe: full comparison for 2026
Last updated: July 2026
Quick verdict
InData Labs (4.5/5) edges ahead of SoftServe (4.0/5) overall. InData Labs is the better choice for finTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead.. SoftServe is the stronger option for enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices.. The right choice depends on your project size, budget, and required tech stack.
InData Labs vs SoftServe: head-to-head summary
| Criterion | InData Labs | SoftServe |
|---|---|---|
| Founded | 2014 | 1993 |
| HQ | Limassol, Cyprus | Austin, Texas, United States / Lviv, Ukraine |
| Team size | 50–100 | 12,000+ |
| Rating | 4.5 / 5 | 4.0 / 5 |
| Best for | FinTech, healthcare, and SaaS companies that want a decade-old AI specialist without enterprise-scale overhead. | Enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices. |
| Pricing model | Project-based, dedicated team | Time & materials, managed engagement |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, TensorFlow, PyTorch | AWS, Azure, Google Cloud |
| Industries served | FinTech, Healthcare, Retail & E-commerce, Logistics & Supply Chain | Healthcare, FinTech, Retail & E-commerce, Manufacturing, Energy |
InData Labs vs SoftServe: 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.
SoftServe
SoftServe was founded in 1993 in Lviv, Ukraine and now operates with a US headquarters in Austin, Texas and a European headquarters in Lviv, employing more than 12,000 people across 58 offices in 14 countries (with one source citing roughly 10,336 as of a recent count). The company's offerings span digital engineering, data analytics, cloud services, AI, machine learning, and IoT, and it ranked seventh among more than 130 Western European companies in Clutch's 2019 software development category. Its scale and 30+ year history make it a large, generalist engineering firm with AI as one of several core practices.
Services and capabilities: InData Labs vs SoftServe
| Capability | InData Labs | SoftServe |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✓ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| Generative AI | ✓ | ✗ |
| AI Consulting | ✗ | ✓ |
Tech stack comparison: InData Labs vs SoftServe
| Framework / platform | InData Labs | SoftServe |
|---|---|---|
| TensorFlow | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | N/A | ✓ |
| LangChain | N/A | N/A |
| Hugging Face | ✓ | N/A |
| Kubernetes | N/A | ✓ |
Pricing comparison: InData Labs vs SoftServe
| Criterion | InData Labs | SoftServe |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Project-based, Dedicated team | Managed engagement, Time & materials, Staff augmentation |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: InData Labs vs SoftServe
| Dimension | InData Labs | SoftServe |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | FinTech, Healthcare, Retail & E-commerce | Healthcare, FinTech, Retail & E-commerce |
| 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. | Large enterprise wants a single vendor covering AI/ML alongside cloud, data analytics, and IoT services., Company needs a choice between US and EU contracting jurisdictions from the same firm. |
| Typical project type | Project-based | Managed engagement |
InData Labs vs SoftServe: 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. |
| SoftServe | |
|---|---|
| + | 12,000+ employees across 58 offices in 14 countries gives it enterprise-scale delivery capacity and geographic redundancy. |
| + | 31 years of continuous operation (since 1993) through multiple technology cycles, including the post-2022 relocation pressures on Ukraine-founded firms. |
| + | Ranked 7th among 130+ Western European companies in Clutch's 2019 software development category, an independently sourced recognition. |
| + | Dual US/Ukraine headquarters structure gives clients a choice of contracting jurisdiction. |
| - | 12,000+ person scale means AI/ML is one of several mature practices (alongside cloud, data analytics, IoT) rather than the firm's core identity. |
| - | Reported employee counts vary by thousands across sources (10,336 vs. 12,000+), reflecting the difficulty of pinning down exact current headcount at this scale. |
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 SoftServe?
SoftServe is the right choice for enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices..
31 years of engineering history (since 1993) with dual US and Ukraine headquarters and 12,000+ employees.. Minimum engagement starts at Not published. Works best with clients in Healthcare, FinTech, Retail & E-commerce, Manufacturing, Energy.
Decision matrix: InData Labs vs SoftServe
| 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 SoftServe (Not published) |
| You need specialist depth in a specific vertical | SoftServe |
| You need production MLOps support after model launch | SoftServe |
| You need consulting before committing to a build | SoftServe |
Use case fit: InData Labs vs SoftServe
| Use case | InData Labs fit | SoftServe 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 |
| Large enterprise wants a single vendor covering AI/ML alongside cloud, data analytics, and IoT services. | Limited | Strong | SoftServe |
| Company needs a choice between US and EU contracting jurisdictions from the same firm. | Strong | Strong | Both equally |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | Limited | Limited | Both equally |
Verdict: InData Labs vs SoftServe
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..
SoftServe (4.0/5) is the better choice when enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices.. If your situation matches those criteria, SoftServe is a competitive option.
Related comparisons
InData Labs vs SoftServe FAQ
Is InData Labs better than SoftServe?
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.. SoftServe is better for enterprises that want an established, dual-HQ (US/Ukraine) engineering firm with AI as one of several mature practices..
How do InData Labs and SoftServe differ in pricing?
InData Labs uses project-based, dedicated team pricing with a minimum engagement of Not published. SoftServe uses time & materials, managed engagement 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 SoftServe?
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 SoftServe?
InData Labs's primary differentiator is: ten-plus years as a pure-play ai/data-science firm with no general software-development sideline.. SoftServe's primary differentiator is: 31 years of engineering history (since 1993) with dual us and ukraine headquarters and 12,000+ employees.. They also differ in team size (50–100 vs 12,000+), minimum engagement (Not published vs Not published), and primary industries served (FinTech, Healthcare vs Healthcare, FinTech).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.