Rearc vs Hakkoda: full comparison for 2026
Quick verdict
Rearc (4.0/5) edges ahead of Hakkoda (3.9/5) overall. Rearc is the better choice for enterprises wanting an engineering-first advisory partner over a slide-deck-heavy strategy firm. Hakkoda is the stronger option for buyers wanting IBM-backed stability for data-and-AI advisory work. The right choice depends on your project size, budget, and required tech stack.
Rearc vs Hakkoda: head-to-head summary
| Criterion | Rearc | Hakkoda |
|---|---|---|
| Founded | 2016 | 2021 |
| HQ | New York, NY, USA | New York, NY, USA |
| Team size | 51-100 | 201-400 |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Best for | Enterprises wanting an engineering-first advisory partner over a slide-deck-heavy strategy firm | Buyers wanting IBM-backed stability for data-and-AI advisory work |
| Pricing model | Dedicated team, T&M | Retainer, fixed project |
| Min. engagement | $30K | $35K |
| Primary tech stack | AWS, OpenAI, LangChain | AWS, Azure, GCP |
| Industries served | Fintech, SaaS, Healthcare | Fintech, Healthcare, Retail |
Rearc vs Hakkoda: overview
Rearc
Rearc was founded in 2016 and is headquartered in New York, with roughly 51-100 employees (74 reported directly) across North America, Asia, and Europe. The firm is an engineering-driven services company specializing in accelerating generative AI, data platform, and cloud platform development for enterprises.
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.
Services and capabilities: Rearc vs Hakkoda
| Capability | Rearc | Hakkoda |
|---|---|---|
| Enterprise automation | ✗ | ✓ |
| Agent orchestration | ✗ | ✗ |
| RAG & knowledge agents | ✗ | ✓ |
| Data & analytics agents | ✓ | ✓ |
| LLM integration | ✓ | ✗ |
| Workflow integration | ✓ | ✗ |
Tech stack comparison: Rearc vs Hakkoda
| Framework / platform | Rearc | Hakkoda |
|---|---|---|
| LangChain | ✓ | N/A |
| LangGraph | N/A | N/A |
| AutoGen | N/A | N/A |
| LlamaIndex | N/A | N/A |
| OpenAI | ✓ | ✓ |
| Anthropic Claude | N/A | N/A |
| Pinecone | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Kubernetes | ✓ | N/A |
Pricing comparison: Rearc vs Hakkoda
| Criterion | Rearc | Hakkoda |
|---|---|---|
| Minimum engagement | $30K | $35K |
| Engagement models | Dedicated team, T&M, Fixed project | Retainer, Fixed project, Staff augmentation |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Rearc vs Hakkoda
| Dimension | Rearc | Hakkoda |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Fintech, SaaS, Healthcare | Fintech, Healthcare, Retail |
| Best use cases | GenAI platform advisory and build, Data platform foundation for agents | Data-platform advisory for AI agents, Cloud-and-AI capability advisory |
| Typical project type | Dedicated team | Retainer |
Rearc vs Hakkoda: pros and cons
| Rearc | |
|---|---|
| + | Engineering-first culture avoids the strategy-only-advisory pitfall of pure consulting firms |
| + | Focused practice areas (GenAI, data, cloud) rather than broad generalist consulting |
| + | US HQ simplifies contracting for North American enterprise buyers |
| - | Smaller team (51-100) limits capacity for very large multi-region programs |
| - | Younger firm (2016) has a shorter track record than legacy advisory houses |
| 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 |
Who should choose Rearc?
Rearc is the right choice for enterprises wanting an engineering-first advisory partner over a slide-deck-heavy strategy firm.
Engineering-driven identity — advisory work is delivered by the same team that builds, not handed off. Minimum engagement starts at $30K. Works best with clients in Fintech, SaaS, Healthcare.
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.
Decision matrix: Rearc vs Hakkoda
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Rearc |
| You need a large dedicated team for an ongoing programme | Rearc |
| Your budget is at the lower end | Rearc |
| You need specialist depth in a specific vertical | Rearc |
| 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: Rearc vs Hakkoda
| Use case | Rearc fit | Hakkoda fit | Winner |
|---|---|---|---|
| GenAI platform advisory and build | Strong | Limited | Rearc |
| Data platform foundation for agents | Strong | Strong | Both equally |
| Data-platform advisory for AI agents | Limited | Strong | Hakkoda |
| Cloud-and-AI capability advisory | Limited | Strong | Hakkoda |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Rearc vs Hakkoda
Rearc (4.0/5) is the stronger overall choice for most AI Agent projects. Engineering-driven identity — advisory work is delivered by the same team that builds, not handed off. It is best for enterprises wanting an engineering-first advisory partner over a slide-deck-heavy strategy firm.
Hakkoda (3.9/5) is the better choice when buyers wanting IBM-backed stability for data-and-AI advisory work. If your situation matches those criteria, Hakkoda is a competitive option.
Related comparisons
Rearc vs Hakkoda FAQ
Is Rearc better than Hakkoda?
Rearc (4.0/5) scores higher overall, but "better" depends on your use case. Rearc is better for enterprises wanting an engineering-first advisory partner over a slide-deck-heavy strategy firm. Hakkoda is better for buyers wanting IBM-backed stability for data-and-AI advisory work.
How do Rearc and Hakkoda differ in pricing?
Rearc uses dedicated team, t&m pricing with a minimum engagement of $30K. Hakkoda uses retainer, fixed project pricing with a minimum engagement of $35K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Rearc or Hakkoda?
Hakkoda 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 Rearc and Hakkoda?
Rearc's primary differentiator is: engineering-driven identity — advisory work is delivered by the same team that builds, not handed off. Hakkoda's primary differentiator is: ibm acquisition (april 2025) adds enterprise backing and cross-sell into ibm's broader ai portfolio. They also differ in team size (51-100 vs 201-400), minimum engagement ($30K vs $35K), and primary industries served (Fintech, SaaS vs Fintech, Healthcare).