SoluLab vs ScienceSoft: full comparison for 2026
Last updated: July 2026
Quick verdict
SoluLab (4.1/5) edges ahead of ScienceSoft (3.8/5) overall. SoluLab is the better choice for companies that want AI development from a vendor also fluent in blockchain/Web3 integration.. ScienceSoft is the stronger option for enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record.. The right choice depends on your project size, budget, and required tech stack.
SoluLab vs ScienceSoft: head-to-head summary
| Criterion | SoluLab | ScienceSoft |
|---|---|---|
| Founded | 2014 | 1989 |
| HQ | Woodland Hills, California, United States | McKinney, Texas, United States |
| Team size | 246–250 | 750+ |
| Rating | 4.1 / 5 | 3.8 / 5 |
| Best for | Companies that want AI development from a vendor also fluent in blockchain/Web3 integration. | Enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record. |
| 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 ML, Google Cloud |
| Industries served | Media & Entertainment, Automotive, Education, FinTech | Healthcare, Insurance, Manufacturing, Retail & E-commerce, Telecom |
SoluLab vs ScienceSoft: 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.
ScienceSoft
ScienceSoft was founded in 1989 and is headquartered in McKinney, Texas, bringing together more than 750 engineers and consultants with a track record of over 4,200 successful projects for 1,400+ clients across healthcare, insurance, investment, manufacturing, retail, and telecom. Its AI practice includes AI engineers, generative AI consultants, and MLOps experts working with both open-source frameworks and cloud-native AI services, and Clutch has named ScienceSoft a 2018 Global IT Leader among its Clutch 1000 companies. At 35+ years old, it is one of the longest-established firms in this list, with AI as a newer addition to a much older core business.
Services and capabilities: SoluLab vs ScienceSoft
| Capability | SoluLab | ScienceSoft |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✗ | ✓ |
Tech stack comparison: SoluLab vs ScienceSoft
| Framework / platform | SoluLab | ScienceSoft |
|---|---|---|
| TensorFlow | N/A | 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 | N/A |
Pricing comparison: SoluLab vs ScienceSoft
| Criterion | SoluLab | ScienceSoft |
|---|---|---|
| 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 ScienceSoft
| Dimension | SoluLab | ScienceSoft |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Media & Entertainment, Automotive, Education | Healthcare, Insurance, Manufacturing |
| 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. | Enterprise wants AI/MLOps delivery from a vendor with 35+ years of institutional stability., Insurance or healthcare company needs a vendor with a very large (4,200+) project delivery track record. |
| Typical project type | Project-based | Managed engagement |
SoluLab vs ScienceSoft: 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. |
| ScienceSoft | |
|---|---|
| + | 35+ years of operating history (since 1989) is among the longest track records of any firm in this list. |
| + | 4,200+ successful projects for 1,400+ clients provides an extensive delivery pattern library across industries. |
| + | 2018 Global IT Leader recognition from Clutch, part of the Clutch 1000, is an independently sourced distinction. |
| + | 750+ engineers and consultants with dedicated MLOps and generative AI consulting roles, not just generalist developers relabeled. |
| - | AI is a comparatively newer addition to a company whose core 35-year identity is broader IT consulting. |
| - | 750-person total headcount spans many practice areas, so AI-specific bench depth is smaller than the total suggests. |
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 ScienceSoft?
ScienceSoft is the right choice for enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record..
35+ years of continuous operating history (since 1989), among the longest-established vendors in this list.. Minimum engagement starts at Not published. Works best with clients in Healthcare, Insurance, Manufacturing, Retail & E-commerce, Telecom.
Decision matrix: SoluLab vs ScienceSoft
| 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 ScienceSoft (Not published) |
| You need specialist depth in a specific vertical | ScienceSoft |
| You need production MLOps support after model launch | ScienceSoft |
| You need consulting before committing to a build | ScienceSoft |
Use case fit: SoluLab vs ScienceSoft
| Use case | SoluLab fit | ScienceSoft 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 |
| Enterprise wants AI/MLOps delivery from a vendor with 35+ years of institutional stability. | Strong | Strong | Both equally |
| Insurance or healthcare company needs a vendor with a very large (4,200+) project delivery track record. | Limited | Strong | ScienceSoft |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | Limited | Limited | Both equally |
Verdict: SoluLab vs ScienceSoft
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..
ScienceSoft (3.8/5) is the better choice when enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record.. If your situation matches those criteria, ScienceSoft is a competitive option.
Related comparisons
SoluLab vs ScienceSoft FAQ
Is SoluLab better than ScienceSoft?
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.. ScienceSoft is better for enterprises wanting AI/MLOps delivery from a 35-year-old IT consultancy with an extensive multi-industry track record..
How do SoluLab and ScienceSoft differ in pricing?
SoluLab uses project-based, dedicated team pricing with a minimum engagement of Not published. ScienceSoft 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 ScienceSoft?
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 ScienceSoft?
SoluLab's primary differentiator is: combines ai-native development with blockchain/web3 expertise under one delivery team.. ScienceSoft's primary differentiator is: 35+ years of continuous operating history (since 1989), among the longest-established vendors in this list.. They also differ in team size (246–250 vs 750+), minimum engagement (Not published vs Not published), and primary industries served (Media & Entertainment, Automotive vs Healthcare, Insurance).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.