Deploying Claude Haiku 5.5 on Google Cloud: A Hands‑On Guide

When I first got my hands on Claude Haiku 5.5, the biggest question I asked myself was how do I run it reliably on Google Cloud without blowing my budget? In this post I’ll walk through the exact steps I used—setting up the GCP project, provisioning the model with Terraform, and calling it from a tiny Python script. By the end you’ll have a production‑ready deployment and a concrete way to keep the cost under control.

Why this matters: If you’re building any feature that relies on a high‑performance LLM, the cloud contract you sign determines both latency and your monthly spend.

#Preparing a Google Cloud project for Claude Haiku 5.5

Before any code touches the model, you need a clean GCP project with the right APIs enabled.

1. Create a new project in the Google Cloud console.
2. Enable the Vertex AI API and Cloud Storage API.
3. Set up a service account with roles/aiplatform.user and roles/storage.objectAdmin.

gcloud projects create my-haiku-demo --name="Claude Haiku Demo"
gcloud services enable aiplatform.googleapis.com storage.googleapis.com --project=my-haiku-demo
gcloud iam service-accounts create haiku-deployer \
    --display-name="Haiku Deployer" \
    --project=my-haiku-demo
gcloud projects add-iam-policy-binding my-haiku-demo \
    --member="serviceAccount:haiku-deployer@my-haiku-demo.iam.gserviceaccount.com" \
    --role="roles/aiplatform.user"

Tip: Store the service‑account key securely; I keep it in a secret manager and reference it via GOOGLE_APPLICATION_CREDENTIALS.

#Deploying the model with Terraform

Terraform makes the infrastructure reproducible. Below is a minimal configuration that creates a Vertex AI endpoint and deploys Claude Haiku 5.5.

provider "google" {
  project = var.project_id
  region  = var.region
}

resource "google_vertex_ai_endpoint" "haiku_endpoint" {
  display_name = "claude-haiku-5-5-endpoint"
  location     = var.region
}

resource "google_vertex_ai_model_deployment" "haiku_deployment" {
  endpoint   = google_vertex_ai_endpoint.haiku_endpoint.id
  model      = var.model_id               # e.g. "claude-haiku-5-5"
  deployed_model {
    display_name = "claude-haiku-5-5"
    automatic_resources {
      min_replica_count = 1
      max_replica_count = 2
    }
  }
}

#Configuring the service account

The google_vertex_ai_model_deployment resource needs the service account you created earlier. Add this block to the Terraform file:

resource "google_service_account_iam_member" "deployer" {
  service_account_id = "haiku-deployer@${var.project_id}.iam.gserviceaccount.com"
  role               = "roles/aiplatform.user"
  member             = "serviceAccount:${google_service_account_haiku_deployer.email}"
}

Run terraform init && terraform apply and watch the endpoint appear in the Cloud console.

Note: Terraform will prompt you to confirm the creation of resources that could incur charges. Double‑check the min_replica_count if you’re experimenting.

#Running inference with the Python client

Once the endpoint is live, a few lines of Python are enough to generate text.

from google.cloud import aiplatform

def generate(prompt: str) -> str:
    client = aiplatform.gapic.PredictionServiceClient()
    endpoint = f"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{ENDPOINT_ID}"
    response = client.predict(
        endpoint=endpoint,
        instances=[{"prompt": prompt}],
        parameters={"temperature": 0.7, "max_output_tokens": 256},
    )
    return response.predictions[0]["content"]

print(generate("Explain quantum computing in two sentences."))

Replace PROJECT_ID, REGION, and ENDPOINT_ID with the values Terraform printed after the apply. The call returns a JSON payload with the model’s completion.

Warning: The default quota for Vertex AI can be low for new projects. If you hit a quota error, request an increase via the GCP console.

#Estimating and controlling your cloud spend

Running an LLM on Vertex AI can be pricey, especially when you scale up replicas. Before you spin up a full‑size deployment, I like to get a quick cost estimate.

Tip: If you want to avoid surprise bills, I’ve been using Estimate Website Cost to get a fast AI‑powered estimate of my GCP spend for the model. It helped me decide on a safe replica count before the first terraform apply.

You can also run a cost check on Estimate Website Cost before scaling. Knowing the per‑hour price of the underlying TPU or GPU lets you budget weekly or monthly caps in the GCP billing console.

#Quick budgeting checklist

• Model compute cost: check Vertex AI pricing tables.
• Storage: factor in the size of any persisted datasets.
• Network egress: estimate traffic from your end‑users.

For the latest pricing, see the official Google Cloud Vertex AI pricing page.

#Wrapping up

Deploying Claude Haiku 5.5 on Google Cloud is surprisingly straightforward once you have the right scaffolding: a clean project, a Terraform definition, and a tiny Python client. The biggest hidden cost is the compute you allocate, so a quick estimate with a tool like Estimate Website Cost can save you headaches later. Give the steps above a try, tweak the replica settings to match your traffic, and you’ll have a robust LLM service ready for production. Happy coding!