Google Vertex AI is one of the most capable AI platforms available — but it’s a cloud platform you build on, which makes it a different kind of thing from osFoundry, a “Hybrid AI Orchestration Platform.” Here’s a factual comparison for a US business, with sources cited. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry.)
At a glance
| osFoundry | Google Vertex AI | |
|---|---|---|
| Core focus | Orchestration: agents, automations, apps | Cloud AI/ML platform (build, tune, deploy) |
| Models | Bring your own, any provider | Gemini + third-party (Model Garden) |
| Cloud lock-in | Cloud-neutral | Locked to Google Cloud (GCP) |
| Pricing | Via dgm: $399 / $3,999/mo | Public, usage-based (tokens/compute) |
| Replaces other SaaS | Designed to consolidate | A platform to build on |
What Vertex AI is
Vertex AI is Google Cloud’s unified, managed machine-learning and generative-AI platform — train and tune models, deploy them, run inference, build agents, and access foundation models in one environment. (As of Google Cloud Next 2026 it’s rebranding toward the “Gemini Enterprise Agent Platform.”) Its Model Garden is the catalog for discovering and deploying Google’s own models (Gemini, Gemma) alongside third-party and open models, and it integrates tightly with the Google Cloud data stack, especially BigQuery.
In short, Vertex is a platform you build on. osFoundry’s center of gravity is different: it’s an orchestration layer for running agents, automations, and apps — with the explicit goal of consolidating overlapping SaaS — rather than a cloud environment where your engineers train and deploy models.
Models
Vertex is genuinely multi-model — Gemini plus third-party models (including Claude, Llama, and others) through Model Garden. But there’s an important boundary: that model choice lives inside Google Cloud. Vertex is cloud-locked to GCP, with deep ties to BigQuery, Cloud Storage, and Google’s MLOps tooling. osFoundry is also model-agnostic — you bring your own models from any provider — but applies that at the orchestration layer across agents and automations, and it isn’t bound to a single cloud. If cloud-neutrality matters to you, that’s the key distinction.
Security and data
Vertex runs within your own Google Cloud project, with GCP’s enterprise security stack — VPC Service Controls, IAM, and compliance certifications — and customer data resides in your GCP environment. (Google publishes data-governance terms; confirm the specific “no training on customer data” commitments in Google’s documentation against your requirements.) With osFoundry, dgm confirms the equivalent controls against your requirements during the integration assessment, so the security review is explicit rather than assumed — and because osFoundry is model-agnostic and cloud-neutral, you retain control over where data and models live.
Pricing
Vertex pricing is public and usage-based, metered across tokens, inference calls, training compute hours, and infrastructure runtime (for example, Gemini models are priced per million input/output tokens). That transparency is a plus, but metering across so many dimensions makes bills genuinely hard to forecast — a common complaint with cloud AI platforms. dgm’s pricing for an osFoundry engagement is fixed and public: $399 assessment and $3,999/month integration, with no per-seat fees — predictable in a way usage-metered cloud billing isn’t.
Build-it-yourself vs orchestrate-and-consolidate
The deeper difference is what you’re signing up to do. Vertex is a build-it-yourself platform: it gives your engineers the full ML lifecycle, which is powerful if you have a team to use it and a GCP footprint to build on. osFoundry is orchestrate-and-consolidate: it’s aimed at running agents and automations across your existing tools and reducing SaaS sprawl, with dgm doing the implementation. One asks you to build; the other delivers a working, consolidated system. They can also be complementary — a GCP-heavy enterprise could use Vertex for custom model work and osFoundry to orchestrate across the broader stack.
Who each is best for
Vertex AI is the stronger choice if you’re a GCP-centric engineering team building and deploying custom models and agents on Google Cloud, comfortable managing usage-based costs and the MLOps lifecycle. osFoundry is the stronger choice if your goal is to orchestrate agents across systems and consolidate SaaS without committing to one cloud, and you want it implemented rather than built in-house.
Which should a US company choose?
If you’re deep in Google Cloud and your team builds custom AI, Vertex AI is excellent. If your goal is to orchestrate agents and consolidate tools while staying cloud- and model-flexible, then osFoundry is the more direct fit. dgm assesses your stack and goals, recommends the right path for a US business, and implements it end to end.