Markovate vs SoluLab: full comparison for 2026
Last updated: July 2026
Quick verdict
Markovate (4.1/5) edges ahead of SoluLab (4.1/5) overall. Markovate is the better choice for companies wanting AI agent or chatbot development led by an executive with enterprise AI leadership background (AT&T, IBM).. SoluLab is the stronger option for companies that want AI development from a vendor also fluent in blockchain/Web3 integration.. The right choice depends on your project size, budget, and required tech stack.
Markovate vs SoluLab: head-to-head summary
| Criterion | Markovate | SoluLab |
|---|---|---|
| Founded | 2015 | 2014 |
| HQ | San Francisco, California, United States | Woodland Hills, California, United States |
| Team size | 50–100 | 246–250 |
| Rating | 4.1 / 5 | 4.1 / 5 |
| Best for | Companies wanting AI agent or chatbot development led by an executive with enterprise AI leadership background (AT&T, IBM). | Companies that want AI development from a vendor also fluent in blockchain/Web3 integration. |
| Pricing model | Project-based, dedicated team | Project-based, dedicated team |
| Min. engagement | Not published | Not published |
| Primary tech stack | LangChain, OpenAI API, Python | OpenAI API, LangChain, Python |
| Industries served | Healthcare, Retail & E-commerce, FinTech, Travel & Hospitality | Media & Entertainment, Automotive, Education, FinTech |
Markovate vs SoluLab: overview
Markovate
Markovate was founded in 2015 and is led by CEO Rajeev Sharma, an AI veteran with 18+ years of experience who previously led AI initiatives at AT&T and IBM. Headquartered with a San Francisco address (some sources cite Toronto as an operating base), the company has grown to roughly 51 employees, including 50+ engineers described as 'certified AI engineers' (per company website), delivering custom AI agents, chatbot development, and cloud services for healthcare, retail, fintech, SaaS, and travel clients. Its small team size makes it a boutique play best suited to scoped generative AI or agent projects rather than large-scale programs.
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.
Services and capabilities: Markovate vs SoluLab
| Capability | Markovate | SoluLab |
|---|---|---|
| Custom ML Models | ✓ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✗ | ✗ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✗ | ✗ |
Tech stack comparison: Markovate vs SoluLab
| Framework / platform | Markovate | SoluLab |
|---|---|---|
| TensorFlow | N/A | N/A |
| PyTorch | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Google Cloud | ✓ | N/A |
| LangChain | ✓ | ✓ |
| Hugging Face | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Markovate vs SoluLab
| Criterion | Markovate | SoluLab |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Project-based, Dedicated team | Project-based, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Markovate vs SoluLab
| Dimension | Markovate | SoluLab |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, Retail & E-commerce, FinTech | Media & Entertainment, Automotive, Education |
| Best use cases | Company wants an AI agent or chatbot built by a team led by a former enterprise AI executive., Healthcare or fintech startup needs a scoped generative AI project from a small, focused vendor. | 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. |
| Typical project type | Project-based | Project-based |
Markovate vs SoluLab: pros and cons
| Markovate | |
|---|---|
| + | CEO's 18+ years leading AI initiatives at AT&T and IBM brings genuine enterprise AI leadership experience to client engagements. |
| + | Focused service scope (AI agents, chatbots, generative AI) rather than a broad, diluted general-consulting offering. |
| + | Serves a wide industry spread (healthcare to travel) despite small team size, suggesting adaptable delivery patterns. |
| - | At roughly 51 employees, capacity for multiple concurrent large engagements is limited. |
| - | HQ location is inconsistently reported (San Francisco vs. Toronto across sources) — confirm the contracting entity directly. |
| - | "50+ certified AI engineers" claim on a 51-person total headcount is a company claim worth verifying during vendor due diligence. |
| 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. |
Who should choose Markovate?
Markovate is the right choice for companies wanting AI agent or chatbot development led by an executive with enterprise AI leadership background (AT&T, IBM)..
CEO brings direct enterprise AI leadership experience (AT&T, IBM) rather than a purely technical or agency background.. Minimum engagement starts at Not published. Works best with clients in Healthcare, Retail & E-commerce, FinTech, Travel & Hospitality.
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.
Decision matrix: Markovate vs SoluLab
| 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 | Markovate |
| Your budget is at the lower end | Compare: Markovate (Not published) vs SoluLab (Not published) |
| You need specialist depth in a specific vertical | Markovate |
| 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: Markovate vs SoluLab
| Use case | Markovate fit | SoluLab fit | Winner |
|---|---|---|---|
| Company wants an AI agent or chatbot built by a team led by a former enterprise AI executive. | Strong | Strong | Both equally |
| Healthcare or fintech startup needs a scoped generative AI project from a small, focused vendor. | Strong | Limited | Markovate |
| 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 |
| Fixed-scope ML build | Limited | Limited | Both equally |
| Ongoing model retraining | Limited | Limited | Both equally |
Verdict: Markovate vs SoluLab
Markovate (4.1/5) is the stronger overall choice for most Machine Learning Development projects. CEO brings direct enterprise AI leadership experience (AT&T, IBM) rather than a purely technical or agency background.. It is best for companies wanting AI agent or chatbot development led by an executive with enterprise AI leadership background (AT&T, IBM)..
SoluLab (4.1/5) is the better choice when companies that want AI development from a vendor also fluent in blockchain/Web3 integration.. If your situation matches those criteria, SoluLab is a competitive option.
Related comparisons
Markovate vs SoluLab FAQ
Is Markovate better than SoluLab?
Markovate (4.1/5) scores higher overall, but "better" depends on your use case. Markovate is better for companies wanting AI agent or chatbot development led by an executive with enterprise AI leadership background (AT&T, IBM).. SoluLab is better for companies that want AI development from a vendor also fluent in blockchain/Web3 integration..
How do Markovate and SoluLab differ in pricing?
Markovate uses project-based, dedicated team pricing with a minimum engagement of Not published. SoluLab uses project-based, 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: Markovate or SoluLab?
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 Markovate and SoluLab?
Markovate's primary differentiator is: ceo brings direct enterprise ai leadership experience (at&t, ibm) rather than a purely technical or agency background.. SoluLab's primary differentiator is: combines ai-native development with blockchain/web3 expertise under one delivery team.. They also differ in team size (50–100 vs 246–250), minimum engagement (Not published vs Not published), and primary industries served (Healthcare, Retail & E-commerce vs Media & Entertainment, Automotive).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.