Agent builders
Bonito vs Relevance AI
No-code AI workforce vs. agents on your own cloud accounts
Relevance AI is a no-code and low-code platform for building AI workforces: agents, tools and multi-agent teams on a visual canvas, sold mostly to go-to-market and operations teams. It is SaaS only. Bonito serves platform teams who need agents plus a shared gateway: an OpenAI-compatible endpoint with cross-provider failover, cost routing and per-agent dollar budgets on their own provider accounts.
Relevance AI pricing
Pro $19/mo annual (2,500 Actions + $20 Vendor Credits); Team $234/mo annual; Enterprise custom. Top-ups $80 per 1,000 Actions.
Model costs
Vendor Credits billed at wholesale with no markup, or bring your own keys on paid plans. The platform fee is the Actions meter.
What Relevance AI does well
- A visual multi-agent Workforce canvas with handoffs and sub-agents, plus text-to-agent building
- Autonomy controls: step limits and per-tool or conditional approval rules
- Managed RAG with reranking and auto-sync, plus long-term project and user memory
- 1,000+ app integrations and cron schedule triggers
- Per-task cost visibility and full agent tracing with OpenTelemetry export
Where Bonito goes further
- An OpenAI-compatible gateway your own apps can call, not only the agents inside the platform
- Automatic failover to an equivalent model on another provider for every call, beyond a per-step fallback model
- Cost routing to cheaper models for simple work
- Dollar budgets per agent
- Canada as a residency option alongside the US and EU, switchable as an org setting
- One bonito.yaml deployed with the CLI, for teams that keep agent config in version control
Where Relevance AI is ahead of Bonito
- Connector breadth (1,000+ apps)
- No-code building for business users
- An Australia data region
Capability by capability
| Capability | Bonito | Relevance AI |
|---|---|---|
| Agents | ||
| Build and run agents | Yes | Yes |
| Approval queue and scheduled runs | Yes | Yes |
| Dollar budgets per agent | Yes | Partialstep limits and per-task cost; no dollar budget found |
| MCP support | Yes | Yes |
| Routing | ||
| OpenAI-compatible gateway | Yes | Nonot found in their docs (Sep 2026); models are used inside agents |
| Automatic failover across providers | Yes | Partialper-step fallback model |
| Cheaper models for simple work (cost routing) | Yes | Nonot found in their docs (Sep 2026) |
| Bedrock, Vertex, Azure and direct providers in one place | Yes | Yes |
| Semantic caching | No | Nonot found in their docs (Sep 2026) |
| Governance | ||
| SSO, RBAC and audit log | Yes | YesSSO, RBAC and audit log on Enterprise |
| Data residency pinning | Yes | YesUS, EU or AU, chosen at signup |
| Observability and knowledge | ||
| Agent traces with cost per request and agent | Yes | Yescost per task and model |
| Knowledge bases (RAG) | Yes | Yes |
| How you run it | ||
| Whole stack from one config file | Yes | PartialAPI, SDK and CLI; no declarative deploy found |
| Self-hosted or open source | Partial | NoSaaS only per their docs |
| No markup on inference | Yes | Yesplatform fee is the Actions meter |
Checked against Relevance AI's own documentation on 2026-09-27. Products change quickly; if something here is out of date, tell us at hello@trybonito.com and we will fix it.
Sources
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