Run a WordPress chatbot on Google Vertex AI
SleekAI talks to Vertex AI through Google’s OpenAI-compatible chat completions endpoint, so the chatbot on your WordPress site can run on gemini-2.5-pro or gemini-2.5-flash inside your own GCP project. Bring the project ID and a service-account-backed key.
Use the GCP project your data already lives in
Vertex AI is the enterprise-grade home for Gemini inside Google Cloud. The models are the same gemini-2.5-pro, gemini-2.5-flash, and Gemini Code variants you would call through the public Generative Language API, but the request lives inside a GCP project, with regional residency, VPC Service Controls, and billing on the same account that already runs the rest of your Google Cloud stack.
Vertex exposes an OpenAI-compatible chat completions endpoint on a per-project URL of the form https://{region}-aiplatform.googleapis.com/v1beta1/projects/{project}/locations/{region}/endpoints/openapi. SleekAI’s OpenAI-compatible provider drops straight into that pattern: paste the URL, an access token minted from a service account with the roles/aiplatform.user role, set the model to google/gemini-2.5-flash, and the chatbot is live.
The traffic flows from WordPress directly to your Vertex endpoint, with no Sleek-hosted relay in the path. For teams already operating on Google Cloud, that single audit trail is often the whole reason to consolidate the WordPress chatbot onto Vertex.
Features
Per-project endpoint
SleekAI signs each call with the access token in the API key field, so the chatbot lands inside the GCP project that already owns your data.
Service-account-friendly
Use a token minted from a service account with the aiplatform.user role. Rotate via gcloud or workload identity federation, paste the current value into SleekAI, and the chatbot keeps answering with no further changes.
Mix Gemini variants
Run gemini-2.5-flash for fast marketing chat and gemini-2.5-pro for deeper documentation answers, both pointing at the same Vertex endpoint with different model strings selected per bot through Multibot.
Where Vertex plus SleekAI fits
Google Cloud enterprises
Companies already on a Google Cloud commitment add a WordPress chatbot without onboarding a separate AI vendor. Spend rolls up on the same invoice, governed by the same IAM and org policies.
Residency-bound workloads
EU, UK, or APAC sites point Vertex at europe-west4, europe-west1, or asia-southeast1 so visitor prompts and Gemini responses stay inside the geography their data agreements require.
Repeat questions
Answer the questions visitors ask every day from the pages and posts where you already answer them.
Wire SleekAI to Vertex AI in four steps
Map your data
Choose the posts, fields, taxonomies or users the bot should know and reference them in the instruction.
Mint an access token
Use gcloud auth print-access-token or workload identity federation in CI to get a short-lived token for the service account.
Configure the provider
Multibot lets you mix Gemini variants on one site.
Set display conditions
Decide which pages, post types, roles or logged-in states show the chatbot.
Why Vertex is the right home for GCP shops
Companies that built on Google Cloud already invested in IAM hierarchies, org policies, VPC Service Controls, billing accounts, and Cloud Monitoring dashboards. Bolting a separate AI vendor onto a marketing site means duplicating all of that for one feature. Vertex AI solves that by giving Gemini a home inside the same GCP project.
The chatbot on the WordPress site can run on gemini-2.5-pro without the security team writing a new third-party risk review and without finance opening a new vendor record, because Vertex is just another Google Cloud service governed by the controls you already operate.
Most WordPress chatbot plugins ignore Vertex entirely and assume you will paste a Google AI Studio key or, worse, an OpenAI key. The chatbot becomes a normal GCP workload instead of a shadow integration.
Frequently asked questions
What is the difference between Vertex AI and Google AI Studio?
AI Studio (api key from aistudio.google.com) is the consumer-grade Gemini API meant for prototyping. Vertex AI is the enterprise-grade Gemini surface inside Google Cloud, with project-level billing, IAM, regional residency, and VPC Service Controls. SleekAI supports both, but Vertex is the right choice for production GCP shops.
What URL do I paste into SleekAI?
Vertex’s OpenAI-compatible endpoint follows the pattern https://{region}-aiplatform.googleapis.com/v1beta1/projects/{project}/locations/{region}/endpoints/openapi. Replace region with us-central1, europe-west4, etc., and project with your GCP project ID.
Which Gemini models work?
Any model Vertex exposes through the OpenAI-compatible endpoint. As of recent updates, that includes google/gemini-2.5-pro, google/gemini-2.5-flash, and related variants.
Does this honor VPC Service Controls?
Yes. If your Vertex endpoint is inside a VPC Service Controls perimeter, the WordPress host needs to reach it from an allowed network. Most teams deploy the WP frontend or an API Gateway in the same VPC, or use Private Service Connect, so the request never crosses the perimeter.
How does billing roll up?
Gemini usage shows up on the same Google Cloud invoice as the rest of your project, broken down by model in Billing reports. SleekAI never marks up tokens or charges per request.
Can I run multiple Gemini models from one WordPress install?
Yes. Multibot lets each chatbot pick its own provider config and model. A common pattern is gemini-2.5-flash on the homepage for speed and gemini-2.5-pro on the docs site for depth, both reaching the same Vertex project under different model strings.
Which AI providers does it support?
OpenAI, Anthropic, Google and OpenRouter, plus custom OpenAI-compatible endpoints. You bring your own API key and the provider bills you directly.
Where does the chatbot get its answers?
From the WordPress data you map into its instruction: post titles and content, post meta, ACF and Meta Box fields, taxonomies, terms, users and user meta, or a WP_Query. Values resolve on every request.
Pricing
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