Infosys vs Deviniti: full comparison for 2026
Quick verdict
Deviniti (3.2/5) edges ahead of Infosys (3.1/5) overall. Deviniti is the better choice for teams already on Atlassian tooling wanting AI agent services integrated into that ecosystem. Infosys is the stronger option for enterprises wanting a large, named library of pre-built agents backed by a Google Cloud partnership. The right choice depends on your project size, budget, and required tech stack.
Infosys vs Deviniti: head-to-head summary
| Criterion | Infosys | Deviniti |
|---|---|---|
| Founded | 1981 | 2004 |
| HQ | Bangalore, India | Wrocław, Poland |
| Team size | 300000 | 201-250 |
| Rating | 3.1 / 5 | 3.2 / 5 |
| Best for | Enterprises wanting a large, named library of pre-built agents backed by a Google Cloud partnership | Teams already on Atlassian tooling wanting AI agent services integrated into that ecosystem |
| Pricing model | Retainer, dedicated team, T&M | Fixed project, dedicated team |
| Min. engagement | $150K | $15K |
| Primary tech stack | GCP, AWS, Azure | AWS, Azure, Python |
| Industries served | Fintech, Retail, Telecom, Manufacturing | SaaS, Manufacturing, Fintech |
Infosys vs Deviniti: overview
Infosys
Infosys was founded in 1981 by seven co-founders including N.R. Narayana Murthy and is headquartered in Bangalore, India, with over 300,000 employees. The firm launched more than 200 enterprise AI agents through its Infosys Topaz AI offerings in partnership with Google Cloud's Vertex AI Platform, and its Agentic Foundry delivers pre-built agents, open frameworks, and responsible AI tools on retainer or dedicated-team terms.
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 on fixed-project or dedicated-team terms.
Services and capabilities: Infosys vs Deviniti
| Capability | Infosys | Deviniti |
|---|---|---|
| Workflow integration | ✗ | ✓ |
| Enterprise automation | ✓ | ✓ |
| Task automation | ✗ | ✓ |
| Agent orchestration | ✗ | ✗ |
| LLM integration | ✓ | ✗ |
| Customer support agents | ✗ | ✗ |
Tech stack comparison: Infosys vs Deviniti
| Framework / platform | Infosys | Deviniti |
|---|---|---|
| LangChain | N/A | N/A |
| LangGraph | N/A | N/A |
| AutoGen | N/A | 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 | ✓ | ✓ |
| Kubernetes | ✓ | N/A |
Pricing comparison: Infosys vs Deviniti
| Criterion | Infosys | Deviniti |
|---|---|---|
| Minimum engagement | $150K | $15K |
| Engagement models | Retainer, Dedicated team, T&M | Fixed project, Dedicated team, Staff augmentation |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Infosys vs Deviniti
| Dimension | Infosys | Deviniti |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Fintech, Retail, Telecom | SaaS, Manufacturing, Fintech |
| Best use cases | Pre-built enterprise agent deployment (Topaz), Google Cloud Vertex AI agent integration | Atlassian-integrated workflow agent services, Custom AI application delivery |
| Typical project type | Retainer | Fixed project |
Infosys vs Deviniti: pros and cons
| Infosys | |
|---|---|
| + | 45+ years of enterprise IT services history |
| + | 200+ pre-built enterprise agents (Topaz) already launched, backed by a named Google Cloud partnership |
| + | 300,000+ employees support the largest, most complex global service programs |
| - | Very high minimum engagement puts it out of reach for all but the largest enterprise buyers |
| - | Massive scale means minimal boutique-style senior-partner attention on individual engagements |
| Deviniti | |
|---|---|
| + | 20+ years of operating history with a clear founding date and leadership |
| + | Deep Atlassian ecosystem expertise supports agent service 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 services are a newer addition relative to its two-decade core consulting history |
Who should choose Infosys?
Infosys is the right choice for enterprises wanting a large, named library of pre-built agents backed by a Google Cloud partnership.
200+ named enterprise AI agents already launched via Topaz, built on Google Cloud's Vertex AI Platform. Minimum engagement starts at $150K. Works best with clients in Fintech, Retail, Telecom, Manufacturing.
Who should choose Deviniti?
Deviniti is the right choice for teams already on Atlassian tooling wanting AI agent services integrated into that ecosystem.
Atlassian marketplace-app pedigree gives it unusually deep workflow-tool integration service experience. Minimum engagement starts at $15K. Works best with clients in SaaS, Manufacturing, Fintech.
Decision matrix: Infosys vs Deviniti
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Deviniti |
| You need a large dedicated team for an ongoing programme | Infosys |
| Your budget is at the lower end | Deviniti |
| You need specialist depth in a specific vertical | Infosys |
| 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: Infosys vs Deviniti
| Use case | Infosys fit | Deviniti fit | Winner |
|---|---|---|---|
| Pre-built enterprise agent deployment (Topaz) | Strong | Limited | Infosys |
| Google Cloud Vertex AI agent integration | Strong | Limited | Infosys |
| Atlassian-integrated workflow agent services | Limited | Strong | Deviniti |
| Custom AI application delivery | Limited | Strong | Deviniti |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Infosys vs Deviniti
Deviniti (3.2/5) is the stronger overall choice for most AI Agent Development projects. Atlassian marketplace-app pedigree gives it unusually deep workflow-tool integration service experience. It is best for teams already on Atlassian tooling wanting AI agent services integrated into that ecosystem.
Infosys (3.1/5) is the better choice when enterprises wanting a large, named library of pre-built agents backed by a Google Cloud partnership. If your situation matches those criteria, Infosys is a competitive option.
Related comparisons
Infosys vs Deviniti FAQ
Is Infosys better than Deviniti?
Deviniti (3.2/5) scores higher overall, but "better" depends on your use case. Infosys is better for enterprises wanting a large, named library of pre-built agents backed by a Google Cloud partnership. Deviniti is better for teams already on Atlassian tooling wanting AI agent services integrated into that ecosystem.
How do Infosys and Deviniti differ in pricing?
Infosys uses retainer, dedicated team, t&m pricing with a minimum engagement of $150K. 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: Infosys or Deviniti?
Infosys is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each provider before shortlisting.
What are the main differences between Infosys and Deviniti?
Infosys's primary differentiator is: 200+ named enterprise ai agents already launched via topaz, built on google cloud's vertex ai platform. Deviniti's primary differentiator is: atlassian marketplace-app pedigree gives it unusually deep workflow-tool integration service experience. They also differ in team size (300000 vs 201-250), minimum engagement ($150K vs $15K), and primary industries served (Fintech, Retail vs SaaS, Manufacturing).