Hakkoda vs Xebia: full comparison for 2026
Quick verdict
Hakkoda (3.9/5) edges ahead of Xebia (3.9/5) overall. Hakkoda is the better choice for buyers wanting IBM-backed stability for data-and-AI advisory work. Xebia is the stronger option for enterprises wanting AI advisory paired with a dedicated internal training practice for upskilling teams. The right choice depends on your project size, budget, and required tech stack.
Hakkoda vs Xebia: head-to-head summary
| Criterion | Hakkoda | Xebia |
|---|---|---|
| Founded | 2021 | 2001 |
| HQ | New York, NY, USA | Atlanta, GA, USA |
| Team size | 201-400 | 4501-6735 |
| Rating | 3.9 / 5 | 3.9 / 5 |
| Best for | Buyers wanting IBM-backed stability for data-and-AI advisory work | Enterprises wanting AI advisory paired with a dedicated internal training practice for upskilling teams |
| Pricing model | Retainer, fixed project | Retainer, dedicated team |
| Min. engagement | $35K | $50K |
| Primary tech stack | AWS, Azure, GCP | AWS, Azure, GCP |
| Industries served | Fintech, Healthcare, Retail | Fintech, Retail, Manufacturing |
Hakkoda vs Xebia: overview
Hakkoda
Hakkoda was founded in 2021 and is headquartered in New York City, with 371 employees. The firm is a modern data consultancy helping companies harness cloud platforms and AI capabilities, and was acquired by IBM in April 2025 — now operating as Hakkōda, an IBM Company, which buyers should factor into long-term roadmap and pricing expectations.
Xebia
Xebia began in the Netherlands in 2001 and moved its global headquarters to Atlanta, Georgia in 2023, with employee counts reported between roughly 4,500 and 6,735 depending on source. The firm is an AI-first consulting, software engineering, and training company helping organizations translate AI strategy into production-ready solutions.
Services and capabilities: Hakkoda vs Xebia
| Capability | Hakkoda | Xebia |
|---|---|---|
| Enterprise automation | ✓ | ✓ |
| Agent orchestration | ✗ | ✓ |
| RAG & knowledge agents | ✓ | ✗ |
| Data & analytics agents | ✓ | ✗ |
| LLM integration | ✗ | ✗ |
| Workflow integration | ✗ | ✓ |
Tech stack comparison: Hakkoda vs Xebia
| Framework / platform | Hakkoda | Xebia |
|---|---|---|
| LangChain | N/A | N/A |
| LangGraph | N/A | N/A |
| AutoGen | N/A | N/A |
| LlamaIndex | N/A | N/A |
| OpenAI | ✓ | N/A |
| Anthropic Claude | N/A | N/A |
| Pinecone | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Kubernetes | N/A | ✓ |
Pricing comparison: Hakkoda vs Xebia
| Criterion | Hakkoda | Xebia |
|---|---|---|
| Minimum engagement | $35K | $50K |
| Engagement models | Retainer, Fixed project, Staff augmentation | Retainer, Dedicated team, T&M |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Hakkoda vs Xebia
| Dimension | Hakkoda | Xebia |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Fintech, Healthcare, Retail | Fintech, Retail, Manufacturing |
| Best use cases | Data-platform advisory for AI agents, Cloud-and-AI capability advisory | AI-first transformation advisory, Internal AI capability training |
| Typical project type | Retainer | Retainer |
Hakkoda vs Xebia: pros and cons
| Hakkoda | |
|---|---|
| + | IBM backing (since April 2025) adds financial stability and enterprise credibility |
| + | Data-platform-first advisory approach suits agents that need reliable data foundations |
| + | Internal AI agent (per Hakkoda Labs) demonstrates applied capability beyond advisory |
| - | 2025 acquisition by IBM changes ownership structure and may shift pricing/positioning over time |
| - | Post-acquisition integration into IBM's broader practice could affect team continuity |
| Xebia | |
|---|---|
| + | 23+ years of engineering-consulting history, with an explicit AI-first repositioning |
| + | Dedicated training practice supports internal capability building, not just external delivery |
| + | Large, multi-thousand-person bench supports substantial enterprise programs |
| - | Reported employee counts vary meaningfully across sources (4,500 to 6,735) — confirm scope directly |
| - | Large-firm structure means less boutique-style senior-partner attention on smaller engagements |
Who should choose Hakkoda?
Hakkoda is the right choice for buyers wanting IBM-backed stability for data-and-AI advisory work.
IBM acquisition (April 2025) adds enterprise backing and cross-sell into IBM's broader AI portfolio. Minimum engagement starts at $35K. Works best with clients in Fintech, Healthcare, Retail.
Who should choose Xebia?
Xebia is the right choice for enterprises wanting AI advisory paired with a dedicated internal training practice for upskilling teams.
Combines AI-first advisory with a formal training arm, useful for buyers who need internal capability building alongside delivery. Minimum engagement starts at $50K. Works best with clients in Fintech, Retail, Manufacturing.
Decision matrix: Hakkoda vs Xebia
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Hakkoda |
| You need a large dedicated team for an ongoing programme | Xebia |
| Your budget is at the lower end | Hakkoda |
| You need specialist depth in a specific vertical | Hakkoda |
| 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: Hakkoda vs Xebia
| Use case | Hakkoda fit | Xebia fit | Winner |
|---|---|---|---|
| Data-platform advisory for AI agents | Strong | Limited | Hakkoda |
| Cloud-and-AI capability advisory | Strong | Limited | Hakkoda |
| AI-first transformation advisory | Limited | Strong | Xebia |
| Internal AI capability training | Limited | Strong | Xebia |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Hakkoda vs Xebia
Hakkoda (3.9/5) is the stronger overall choice for most AI Agent projects. IBM acquisition (April 2025) adds enterprise backing and cross-sell into IBM's broader AI portfolio. It is best for buyers wanting IBM-backed stability for data-and-AI advisory work.
Xebia (3.9/5) is the better choice when enterprises wanting AI advisory paired with a dedicated internal training practice for upskilling teams. If your situation matches those criteria, Xebia is a competitive option.
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Hakkoda vs Xebia FAQ
Is Hakkoda better than Xebia?
Hakkoda (3.9/5) scores higher overall, but "better" depends on your use case. Hakkoda is better for buyers wanting IBM-backed stability for data-and-AI advisory work. Xebia is better for enterprises wanting AI advisory paired with a dedicated internal training practice for upskilling teams.
How do Hakkoda and Xebia differ in pricing?
Hakkoda uses retainer, fixed project pricing with a minimum engagement of $35K. Xebia uses retainer, dedicated team pricing with a minimum engagement of $50K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Hakkoda or Xebia?
Xebia is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each consultancy before shortlisting.
What are the main differences between Hakkoda and Xebia?
Hakkoda's primary differentiator is: ibm acquisition (april 2025) adds enterprise backing and cross-sell into ibm's broader ai portfolio. Xebia's primary differentiator is: combines ai-first advisory with a formal training arm, useful for buyers who need internal capability building alongside delivery. They also differ in team size (201-400 vs 4501-6735), minimum engagement ($35K vs $50K), and primary industries served (Fintech, Healthcare vs Fintech, Retail).