Vertex AI
on Orq.ai
Access models hosted through Google Vertex AI using Orq.ai for reasoning, coding, multimodal, and other AI workloads through one API.
Capabilities:
Chat
Reasoning
Vision
Code
Models Supported:
...
No models available
Provider HQ:
Mountain View, California
Access Google Vertex AI through Orq.ai’s AI Router
Vertex AI is Google Cloud’s managed platform for building and deploying AI applications. It provides access to Google and supported third-party models for reasoning, coding, multimodal applications, embeddings, and other AI workloads within the Google Cloud ecosystem.
Google Vertex AI models available on Orq.ai
Compare the models currently available through Google Vertex AI on Orq.ai, including their supported capabilities, context windows, pricing, and regional availability.
Model
Type
Context
Input / 1M
Output / 1M
No provider models available
Why use Google Vertex AI through Orq.ai?
Using Vertex AI through Orq.ai lets teams combine Google Cloud-hosted models with a wider model stack without maintaining separate routing, evaluation, observability, and cost-control logic for each provider.
Capability | Provider | Direct | Through Orq.ai |
|---|---|---|---|
Chat | Models available through Vertex AI | Call supported models directly through Vertex AI for chat, reasoning, coding, and multimodal workloads. | Use Vertex-hosted models through Orq.ai’s OpenAI-compatible endpoint while applying routing, tracing, evals, budgets, and governance controls around each request. |
Code | Models available through Vertex AI | Use supported models for code generation, debugging, refactoring, and agentic coding workflows. | Route coding workloads through Orq.ai, compare Vertex-hosted models with other providers, and monitor cost, latency, and quality from a shared control layer. |
Embeddings / multimodal | Supported Vertex AI models | Use supported models for embeddings, multimodal inputs, and other specialized workloads. | Route embedding and multimodal workloads alongside other providers while keeping observability, evaluation, and governance in the same platform. |
Pricing
Vertex AI pricing varies by model, modality, usage type, Google Cloud region, deployment configuration, and billing setup.
You can connect supported Google Cloud credentials and Vertex AI configurations to Orq.ai or use other available access options depending on your workspace configuration. Check Orq.ai and your Google Cloud setup for current per-model rates, quotas, and billing details.
Vertex AI may also have costs associated with services outside standard model inference. Check Google Cloud pricing for any additional services used by your deployment.
Compatible frameworks and tools
Orq.ai works with OpenAI-compatible clients and common AI development frameworks. Supported Vertex AI models can be incorporated into Orq.ai workflows depending on the provider, model, and integration configuration.
Check the Orq.ai integration documentation for the latest setup options and compatibility information for Vertex AI.
FAQs
Do I need a separate Google Cloud account to use Vertex AI through Orq.ai?
You can connect supported Google Cloud credentials and Vertex AI configurations to Orq.ai or use other available access options depending on your workspace, plan, and region.
Can I route only some workflows to Vertex AI and others to different providers?
Yes. Orq.ai lets you route different workloads to different providers, so you can use Vertex AI where its Google Cloud integration, regional availability, deployment requirements, or model capabilities fit the workload while routing other requests elsewhere.
Does using Vertex AI through Orq.ai add latency?
Orq.ai adds a routing layer between your application and the model provider. Teams can monitor end-to-end latency and use routing, caching, and provider controls where appropriate to manage performance.
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