Intuz vs Deviniti: full comparison for 2026
Last updated: August 2026
Quick verdict
Intuz (3.7/5) edges ahead of Deviniti (3.5/5) overall. Intuz is the better choice for buyers wanting a documented count of live production agent deployments, not just pilot case studies. Deviniti is the stronger option for teams already on Atlassian tooling wanting AI agents integrated into that ecosystem. The right choice depends on your project size, budget, and required tech stack.
Intuz vs Deviniti: head-to-head summary
| Criterion | Intuz | Deviniti |
|---|---|---|
| Founded | 2008 | 2004 |
| HQ | San Francisco, USA | Wrocław, Poland |
| Team size | 51-200 | 201-250 |
| Rating | 3.7 / 5 | 3.5 / 5 |
| Best for | Buyers wanting a documented count of live production agent deployments, not just pilot case studies | Teams already on Atlassian tooling wanting AI agents integrated into that ecosystem |
| Pricing model | Dedicated team, fixed project | Fixed project, dedicated team |
| Min. engagement | $20K | $15K |
| Primary tech stack | LangGraph, CrewAI, AutoGen | AWS, Azure, Python |
| Industries served | Healthcare, E-commerce, Logistics | SaaS, Manufacturing, Fintech |
Intuz vs Deviniti: overview
Intuz
Intuz was founded in 2008 and is a US-headquartered company with offices in San Francisco and San Ramon, California, plus an engineering center in Ahmedabad, India, and 51-200 employees. The firm designs, builds, and operates production AI agents on LangGraph, CrewAI, and AutoGen, reporting 100+ enterprise deployments across healthcare, e-commerce, and logistics.
Deviniti
Deviniti was founded on December 13, 2004 in Wrocław, Poland by Piotr Jan Dorosz and Jacek Michał Machata, and now has 250+ employees. The company combines Atlassian-focused consulting and marketplace apps with custom software development, cloud/DevOps, and AI application services.
Services and capabilities: Intuz vs Deviniti
| Capability | Intuz | Deviniti |
|---|---|---|
| Multi-agent systems | ✗ | ✗ |
| RAG & knowledge agents | ✗ | ✗ |
| Workflow integration | ✓ | ✓ |
| Agent orchestration | ✓ | ✗ |
| Enterprise automation | ✓ | ✓ |
| Customer support agents | ✗ | ✗ |
Tech stack comparison: Intuz vs Deviniti
| Framework / platform | Intuz | Deviniti |
|---|---|---|
| LangChain | N/A | N/A |
| LangGraph | ✓ | N/A |
| AutoGen | ✓ | N/A |
| LlamaIndex | N/A | N/A |
| OpenAI | N/A | N/A |
| Anthropic Claude | N/A | N/A |
| Pinecone | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Kubernetes | N/A | N/A |
Pricing comparison: Intuz vs Deviniti
| Criterion | Intuz | Deviniti |
|---|---|---|
| Minimum engagement | $20K | $15K |
| Engagement models | Dedicated team, Fixed project, T&M | Fixed project, Dedicated team, Staff augmentation |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Intuz vs Deviniti
| Dimension | Intuz | Deviniti |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthcare, E-commerce, Logistics | SaaS, Manufacturing, Fintech |
| Best use cases | Production multi-agent orchestration, Healthcare/logistics agent deployment | Atlassian-integrated workflow agents, Custom AI application development |
| Typical project type | Dedicated team | Fixed project |
Intuz vs Deviniti: pros and cons
| Intuz | |
|---|---|
| + | Reports a specific, high production-deployment count (100+) rather than vague claims |
| + | US HQ with an India engineering center balances access and delivery cost |
| + | Multi-framework fluency (LangGraph, CrewAI, AutoGen) avoids lock-in to one stack |
| - | Deployment-count figures are self-reported (per company website; independently unverifiable) |
| - | Mid-size team (51-200) may face capacity limits on very large multi-region programs |
| Deviniti | |
|---|---|
| + | 20+ years of operating history with a clear founding date and leadership |
| + | Deep Atlassian ecosystem expertise supports agent integration into existing workflow tools |
| + | Combines marketplace product development with custom consulting delivery |
| - | Atlassian-ecosystem specialization is a narrower fit for buyers outside that toolchain |
| - | AI application work is a newer addition relative to its two-decade core consulting history |
Who should choose Intuz?
Intuz is the right choice for buyers wanting a documented count of live production agent deployments, not just pilot case studies.
Reports 100+ enterprise agent deployments already in production across three named framework stacks. Minimum engagement starts at $20K. Works best with clients in Healthcare, E-commerce, Logistics.
Who should choose Deviniti?
Deviniti is the right choice for teams already on Atlassian tooling wanting AI agents integrated into that ecosystem.
Atlassian marketplace-app pedigree gives it unusually deep workflow-tool integration experience. Minimum engagement starts at $15K. Works best with clients in SaaS, Manufacturing, Fintech.
Decision matrix: Intuz vs Deviniti
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Intuz |
| You need a large dedicated team for an ongoing programme | Intuz |
| Your budget is at the lower end | Deviniti |
| You need specialist depth in a specific vertical | Intuz |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Both may offer discovery engagements |
Use case fit: Intuz vs Deviniti
| Use case | Intuz fit | Deviniti fit | Winner |
|---|---|---|---|
| Production multi-agent orchestration | Strong | Limited | Intuz |
| Healthcare/logistics agent deployment | Strong | Limited | Intuz |
| Atlassian-integrated workflow agents | Limited | Strong | Deviniti |
| Custom AI application development | Limited | Strong | Deviniti |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Intuz vs Deviniti
Intuz (3.7/5) is the stronger overall choice for most AI Agent Development projects. Reports 100+ enterprise agent deployments already in production across three named framework stacks. It is best for buyers wanting a documented count of live production agent deployments, not just pilot case studies.
Deviniti (3.5/5) is the better choice when teams already on Atlassian tooling wanting AI agents integrated into that ecosystem. If your situation matches those criteria, Deviniti is a competitive option.
Related comparisons
Intuz vs Deviniti FAQ
Is Intuz better than Deviniti?
Intuz (3.7/5) scores higher overall, but "better" depends on your use case. Intuz is better for buyers wanting a documented count of live production agent deployments, not just pilot case studies. Deviniti is better for teams already on Atlassian tooling wanting AI agents integrated into that ecosystem.
How do Intuz and Deviniti differ in pricing?
Intuz uses dedicated team, fixed project pricing with a minimum engagement of $20K. Deviniti uses fixed project, dedicated team pricing with a minimum engagement of $15K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Intuz or Deviniti?
Deviniti 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 Intuz and Deviniti?
Intuz's primary differentiator is: reports 100+ enterprise agent deployments already in production across three named framework stacks. Deviniti's primary differentiator is: atlassian marketplace-app pedigree gives it unusually deep workflow-tool integration experience. They also differ in team size (51-200 vs 201-250), minimum engagement ($20K vs $15K), and primary industries served (Healthcare, E-commerce vs SaaS, Manufacturing).
Last reviewed: August 2026. Verify all details directly with each company before making a decision.