Relevance AI markets a compelling idea — build an “AI workforce” of collaborating agents — and, like osFoundry, it’s model-agnostic. So this comparison is about delivery model and focus more than model philosophy. Here’s a factual look for a US business, with sources cited. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry.)
At a glance
| osFoundry | Relevance AI | |
|---|---|---|
| Core focus | Orchestration: agents, automations, apps | No-code “AI workforce” (multi-agent) |
| Models | Bring your own, any provider | Model-agnostic, incl. self-hosted |
| Data residency | Confirmed in assessment | US / EU / AU (fixed at signup) |
| Pricing | Via dgm: $399 / $3,999/mo | Free → Pro ~$19 → Team ~$234/mo + credits |
| SaaS consolidation | Designed to consolidate | 9,000+ integrations; agent builder |
What Relevance AI is
Relevance AI is a no-code platform for building and managing teams of AI agents — an “AI workforce” — that automate workflows, with agents that can collaborate (one researches, one verifies, one writes). It offers 9,000+ integrations and 400+ agent templates plus a Python SDK, and it’s self-serve from a free tier up. Headquartered in Sydney, Australia, it targets sales, marketing, ops, and support teams from SMB through enterprise.
osFoundry overlaps in running multi-agent work, but its emphasis is broader orchestration plus the explicit goal of consolidating overlapping SaaS — delivered as an implemented system by dgm rather than a self-serve builder.
Models
Both are model-agnostic. Relevance AI is vendor-agnostic, supporting OpenAI, Anthropic, and self-hosted models, and — a nice touch — it passes through model compute (“Vendor Credits”) at provider rates with no markup. osFoundry is likewise model-agnostic at the orchestration layer. So model flexibility is common ground; the difference is what wraps the models.
Security and data
Relevance AI lets you choose a region at signup — US (N. Virginia), EU (London), or Australia (Sydney) — with VPC/private-subnet isolation, TLS and AES-256 encryption, SOC 2 Type II, GDPR, and a commitment not to train on your data. One operational caveat: the region can’t be changed after the organization is created, so choose carefully. With osFoundry, dgm confirms data-residency and the equivalent controls against your requirements during the integration assessment.
Pricing
Relevance AI has a free tier, a Pro plan around $19/month, and a Team plan around $234/month (billed annually), plus pass-through model credits. The dual-meter model — platform “Actions” plus model “Vendor Credits” — is transparent but can make total cost harder to forecast. dgm’s osFoundry engagement pricing is fixed and public instead: $399 assessment and $3,999/month integration, with no per-seat fees.
Self-serve builder vs implemented orchestration
The practical difference is who does the building. Relevance AI is a self-serve, no-code builder — great if you have people who want to assemble an AI workforce themselves. osFoundry, via dgm, is an implemented orchestration layer that also targets SaaS consolidation: dgm scopes, builds, integrates, and trains. If you’d rather have a working, consolidated system delivered than build it in-house — and you value predictable cost — that’s the distinction.
Who each is best for
Relevance AI is the stronger choice if you want a self-serve, no-code “AI workforce” with transparent pass-through model costs and you have the appetite to build agents yourself. osFoundry is the stronger choice if you want implemented orchestration and SaaS consolidation with fixed pricing.
Which should a US company choose?
If a no-code, self-serve AI workforce fits your team, Relevance AI is a strong, model-agnostic option. If you want orchestration plus consolidation delivered for you, then osFoundry is the more direct fit. dgm assesses your goals, recommends the right path for a US business, and implements it end to end.