Google Cloud Platform
Google Cloud Run
Google Kubernetes Engine
Cloud Functions
Cloud Pub/Sub
Google Cloud API Gateway
Cloud Build
Cloud SQL
Firestore
BigQuery
Cloud Storage
IAM
Cloud Monitoring
Cloud Logging
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Supported AI models on Workik
GPT 5.2, GPT 5.1 Codex, GPT 5.1, GPT 5 Mini, GPT 5, GPT 4.1 Mini
Gemini 3 Pro, Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 2.0 Flash
Claude 4.5 Sonnet, Claude 4.5 Haiku, Claude 4 Sonnet, Claude 3.5 Haiku
Deepseek Reasoner, Deepseek Chat, Deepseek R1(High)
Grok 4.1 Fast, Grok 4, Grok Code Fast 1
Models availability might vary based on your plan on Workik
Features
Define Cloud Architecture
AI generates Terraform and service configurations that align Cloud Run, GKE, IAM bindings, and environment separation correctly.
Deploy Compute Services
Cloud Run services, Cloud Functions, and GKE workloads are built by AI with runtime flags, permissions, and scaling behavior defined.
Integrate GCP Services
AI connects APIs, Pub/Sub topics, data stores, & identity flows to create event-driven Google Cloud apps.
Stabilize Delivery Pipelines
Cloud Build pipelines, deployment configs, and logs are analyzed by AI to fix failures and shorten release cycles.
TESTIMONIALS
Real Stories, Real Results with Workik
"We generated a BigQuery pipeline draft instantly with Workik AI. It now saves us hours of wiring the basics."
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Lea Farthing
Software Architect
"I used Workik AI to spin up Cloud Run APIs for a new project. The AI handled all the setup, and I just refined the logic."
Jonathan Conniff
Cloud Backend Engineer
"We used shared workspaces for our Google Cloud app build. Everyone could generate, test, and deploy in sync."
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Hester Zand
Project Lead
What are the popular use cases of Workik’s Google Cloud Code Generator for developers?
Developers use Workik’s Google Cloud Code Generator for many real-world tasks, including but are not limited to:
* Generating backend APIs for Cloud Run or App Engine
* Automating infrastructure setup for Kubernetes clusters or Cloud Functions
* Creating database logic and queries for Firestore, Cloud SQL, or BigQuery
* Building and deploying full-stack apps with Firebase integrations
* Generating Terraform or Deployment Manager scripts for GCP automation
* Debugging Cloud Function errors and optimizing Cloud Run performance
* Writing serverless workflows that connect Pub/Sub, Cloud Storage, and APIs
* Refactoring or extending existing GCP projects from GitHub or GitLab repos
What context-setting options are available in Workik for Google Cloud projects?
Adding context is not necessary, but adding it helps AI personalize outputs for your specific Google Cloud setup. Developers can add:
* Languages, frameworks, and libraries – define your tech stack for precise code generation
* Database schemas – include Firestore or Cloud SQL structures for accurate query and model creation
* API blueprints – import Postman or Swagger files to refine API logic or endpoint generation
* Repository connections – integrate GitHub, GitLab, or Bitbucket for instant project context and version sync
* Codebase files and common functions – help AI analyze patterns and extend existing functionality.
* Dynamic environment details – share Cloud Run configs, IAM roles, or Terraform templates for infrastructure-aware responses.
How does Workik’s Google Cloud Code Generator help with serverless development?
Workik AI simplifies Google Cloud serverless workflows by generating and configuring Cloud Run or Cloud Functions automatically. For example, you can describe your API or data pipeline in plain text, and AI writes deployable code with triggers and environment variables. It helps developers skip repetitive setup while keeping full control over logic and scaling.
Can I build and deploy full stack applications with the Google Cloud Code Generator?
Yes. Workik AI helps developers build complete Google Cloud applications, from backend APIs to frontend deployment. You can define your frameworks and the AI will generate backend logic for Cloud Run, help to connect it with Firestore or Cloud Storage, and create deployment files ready for execution.
How does AI assist in optimizing performance on Google Cloud?
Workik AI reviews GCP logs and identifies inefficiencies in code or configuration. Developers can receive recommendations for autoscaling thresholds, API latency improvements, or optimized resource allocation in Compute Engine or Kubernetes clusters. This helps ensure efficient performance across workloads without extensive manual tuning.
How can Workik AI help manage and automate Google Cloud infrastructure?
Workik AI automates Google Cloud infrastructure by generating and updating deployment scripts, YAMLs, and Terraform templates. Connect projects from GitHub, GitLab, or Bitbucket, and AI detects existing configurations like IAM roles or network rules, then optimizes and helps scale them across environments.
Can I test and debug my Google Cloud applications with AI assistance?
Yes. Workik AI can simulate test cases for Cloud Functions, generate mock responses, and analyze logs to detect potential issues. You can even ask AI to debug Firestore queries or inspect Cloud Run errors to get clear, actionable solutions faster.
Can I use Workik’s AI code generator for AI and ML projects on Google Cloud?
Yes. Developers working with Vertex AI, TensorFlow, or AI Platform can use Workik to set up ML pipelines, data ingestion scripts, and model deployment configurations. For instance, you can generate preprocessing code for BigQuery datasets or create model-serving endpoints integrated with Vertex AI automatically.
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