XenonStack vs SoluLab: full comparison for 2026
Last updated: July 2026
Quick verdict
XenonStack (4.4/5) edges ahead of SoluLab (4.1/5) overall. XenonStack is the better choice for companies building agentic AI or real-time data platforms that want a specialist rather than a general IT outsourcer.. 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.
XenonStack vs SoluLab: head-to-head summary
| Criterion | XenonStack | SoluLab |
|---|---|---|
| Founded | 2016 | 2014 |
| HQ | Mohali, India | Woodland Hills, California, United States |
| Team size | 50–100 | 246–250 |
| Rating | 4.4 / 5 | 4.1 / 5 |
| Best for | Companies building agentic AI or real-time data platforms that want a specialist rather than a general IT outsourcer. | Companies that want AI development from a vendor also fluent in blockchain/Web3 integration. |
| Pricing model | Project-based, retainer | Project-based, dedicated team |
| Min. engagement | Not published | Not published |
| Primary tech stack | Kubernetes, Apache Kafka, AWS | OpenAI API, LangChain, Python |
| Industries served | FinTech, Manufacturing, Telecom, Retail & E-commerce | Media & Entertainment, Automotive, Education, FinTech |
XenonStack vs SoluLab: overview
XenonStack
XenonStack was founded in 2016 by Navdeep Singh Gill and is based in Mohali, India, operating as a technology consulting company centered on real-time data, generative AI, and agentic AI platform engineering. The company has grown from roughly 63 employees in 2023 to about 97 in 2026 and holds AWS, Azure, and Google Cloud partner status, alongside membership in the Cloud Native Computing Foundation and LF AI & Data. Its bootstrapped, revenue-funded growth (reported ~$3.8M ARR) suggests a stable but still relatively small operation for enterprise-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: XenonStack vs SoluLab
| Capability | XenonStack | SoluLab |
|---|---|---|
| Custom ML Models | ✗ | ✓ |
| Computer Vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| MLOps | ✓ | ✗ |
| Generative AI | ✓ | ✓ |
| AI Consulting | ✗ | ✗ |
Tech stack comparison: XenonStack vs SoluLab
| Framework / platform | XenonStack | SoluLab |
|---|---|---|
| TensorFlow | N/A | N/A |
| PyTorch | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Google Cloud | ✓ | N/A |
| LangChain | ✓ | ✓ |
| Hugging Face | N/A | N/A |
| Kubernetes | ✓ | N/A |
Pricing comparison: XenonStack vs SoluLab
| Criterion | XenonStack | SoluLab |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Project-based, Retainer, Dedicated team | Project-based, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: XenonStack vs SoluLab
| Dimension | XenonStack | SoluLab |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | FinTech, Manufacturing, Telecom | Media & Entertainment, Automotive, Education |
| Best use cases | Enterprise needs a real-time data platform feeding downstream ML models., Company is building agentic AI workflows and needs specialist platform engineering, not just model development. | 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 |
XenonStack vs SoluLab: pros and cons
| XenonStack | |
|---|---|
| + | Multi-cloud partner status across AWS, Azure, and Google Cloud gives flexibility on platform choice rather than pushing a single vendor stack. |
| + | Bootstrapped and profitable growth trajectory (reported ~$3.8M ARR) signals operational stability without dependence on external funding rounds. |
| + | Cloud Native Computing Foundation and LF AI & Data membership reflects genuine open-source platform engineering involvement, not just marketing claims. |
| + | Specialization in agentic and real-time AI platform engineering is a differentiated niche versus generalist ML shops. |
| - | Team size of roughly 97 (2026) is small relative to the scale of enterprise real-time data platform programs it targets. |
| - | Conflicting HQ reports (Mohali, India vs. Dubai, UAE across sources) make it worth confirming the primary legal entity before contracting. |
| 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 XenonStack?
XenonStack is the right choice for companies building agentic AI or real-time data platforms that want a specialist rather than a general IT outsourcer..
Multi-cloud certified (AWS, Azure, GCP) platform-engineering specialist for real-time and agentic AI.. Minimum engagement starts at Not published. Works best with clients in FinTech, Manufacturing, Telecom, Retail & E-commerce.
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: XenonStack 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 | XenonStack |
| Your budget is at the lower end | Compare: XenonStack (Not published) vs SoluLab (Not published) |
| You need specialist depth in a specific vertical | XenonStack |
| You need production MLOps support after model launch | XenonStack |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: XenonStack vs SoluLab
| Use case | XenonStack fit | SoluLab fit | Winner |
|---|---|---|---|
| Enterprise needs a real-time data platform feeding downstream ML models. | Strong | Strong | Both equally |
| Company is building agentic AI workflows and needs specialist platform engineering, not just model development. | Strong | Strong | Both equally |
| 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: XenonStack vs SoluLab
XenonStack (4.4/5) is the stronger overall choice for most Machine Learning Development projects. Multi-cloud certified (AWS, Azure, GCP) platform-engineering specialist for real-time and agentic AI.. It is best for companies building agentic AI or real-time data platforms that want a specialist rather than a general IT outsourcer..
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
XenonStack vs SoluLab FAQ
Is XenonStack better than SoluLab?
XenonStack (4.4/5) scores higher overall, but "better" depends on your use case. XenonStack is better for companies building agentic AI or real-time data platforms that want a specialist rather than a general IT outsourcer.. SoluLab is better for companies that want AI development from a vendor also fluent in blockchain/Web3 integration..
How do XenonStack and SoluLab differ in pricing?
XenonStack uses project-based, retainer 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: XenonStack 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 XenonStack and SoluLab?
XenonStack's primary differentiator is: multi-cloud certified (aws, azure, gcp) platform-engineering specialist for real-time and agentic ai.. 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 (FinTech, Manufacturing vs Media & Entertainment, Automotive).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.