SoluLab vs SoftServe: full comparison for 2026
Last updated: July 2026
Quick verdict
SoluLab (4.1/5) edges ahead of SoftServe (4.0/5) overall. SoluLab is the better choice for companies that want AI development from a vendor also fluent in blockchain/Web3 integration.. 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.
SoluLab vs SoftServe: head-to-head summary
| Criterion | SoluLab | SoftServe |
|---|---|---|
| Founded | 2014 | 1993 |
| HQ | Woodland Hills, California, United States | Austin, Texas, United States / Lviv, Ukraine |
| Team size | 246–250 | 12,000+ |
| Rating | 4.1 / 5 | 4.0 / 5 |
| Best for | Companies that want AI development from a vendor also fluent in blockchain/Web3 integration. | 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 | OpenAI API, LangChain, Python | AWS, Azure, Google Cloud |
| Industries served | Media & Entertainment, Automotive, Education, FinTech | Healthcare, FinTech, Retail & E-commerce, Manufacturing, Energy |
SoluLab vs SoftServe: overview
SoluLab
SoluLab was founded in 2014–2015 by Chintan Thakkar and Rajat Lala and is headquartered in Woodland Hills, California, with a team of roughly 246–250 engineers, data scientists, and AI specialists. The firm positions itself as an 'AI-native, Blockchain, and Web3' development company and reports having delivered 1,500+ projects across 15+ countries for clients including The Walt Disney Company, Mercedes-Benz, and the University of Cambridge (per company website; independently unverifiable at this scale). Its dual focus on AI and blockchain/Web3 makes it broader than a pure ML specialist.
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: SoluLab vs SoftServe
| Capability | SoluLab | SoftServe |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| Generative AI | ✓ | ✗ |
| AI Consulting | ✗ | ✓ |
Tech stack comparison: SoluLab vs SoftServe
| Framework / platform | SoluLab | SoftServe |
|---|---|---|
| TensorFlow | N/A | ✓ |
| PyTorch | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | N/A | ✓ |
| LangChain | ✓ | N/A |
| Hugging Face | N/A | N/A |
| Kubernetes | N/A | ✓ |
Pricing comparison: SoluLab vs SoftServe
| Criterion | SoluLab | 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: SoluLab vs SoftServe
| Dimension | SoluLab | SoftServe |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Media & Entertainment, Automotive, Education | Healthcare, FinTech, Retail & E-commerce |
| Best use cases | Company building an AI product with a blockchain or Web3 component needs a single integrated vendor., Enterprise wants a vendor with named brand-name reference clients for procurement comfort. | 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 |
SoluLab vs SoftServe: pros and cons
| SoluLab | |
|---|---|
| + | Named enterprise clients (The Walt Disney Company, Mercedes-Benz, University of Cambridge) offer verifiable reference points, though the specific scope of each engagement is unconfirmed. |
| + | 246–250 team size supports mid-to-large engagements without enterprise-firm overhead. |
| + | Combined AI and blockchain/Web3 capability is useful for clients building tokenized or decentralized AI products. |
| + | 10 years of company history (since 2014–2015) under continuous founder leadership. |
| - | 1,500+ projects claim across 15+ countries is difficult to independently verify at face value. |
| - | Blockchain/Web3 focus alongside AI means clients purely interested in ML may be paying for adjacent expertise they don't need. |
| 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 SoluLab?
SoluLab is the right choice for companies that want AI development from a vendor also fluent in blockchain/Web3 integration..
Combines AI-native development with blockchain/Web3 expertise under one delivery team.. Minimum engagement starts at Not published. Works best with clients in Media & Entertainment, Automotive, Education, FinTech.
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: SoluLab 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 | SoluLab |
| Your budget is at the lower end | Compare: SoluLab (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: SoluLab vs SoftServe
| Use case | SoluLab fit | SoftServe fit | Winner |
|---|---|---|---|
| Company building an AI product with a blockchain or Web3 component needs a single integrated vendor. | Strong | Strong | Both equally |
| Enterprise wants a vendor with named brand-name reference clients for procurement comfort. | Strong | Strong | Both equally |
| 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: SoluLab vs SoftServe
SoluLab (4.1/5) is the stronger overall choice for most Machine Learning Development projects. Combines AI-native development with blockchain/Web3 expertise under one delivery team.. It is best for companies that want AI development from a vendor also fluent in blockchain/Web3 integration..
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
SoluLab vs SoftServe FAQ
Is SoluLab better than SoftServe?
SoluLab (4.1/5) scores higher overall, but "better" depends on your use case. SoluLab is better for companies that want AI development from a vendor also fluent in blockchain/Web3 integration.. 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 SoluLab and SoftServe differ in pricing?
SoluLab 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: SoluLab or SoftServe?
SoluLab 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 SoluLab and SoftServe?
SoluLab's primary differentiator is: combines ai-native development with blockchain/web3 expertise under one delivery team.. 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 (246–250 vs 12,000+), minimum engagement (Not published vs Not published), and primary industries served (Media & Entertainment, Automotive vs Healthcare, FinTech).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.