AI Chat
Multi-model conversations with files, projects, memory and tool calls — local models and premium providers behind one gateway.
Webyne AI Cloud turns a sentence into a running system. Describe the application you want; the platform designs it, writes it, prices the infrastructure, waits for your approval, deploys it — and then keeps watching it.
Chat, agents, code, infrastructure, deployment and operations all point at the same project. What you discussed in chat is the thing that gets built, deployed and watched — no re-explaining it to another tool.
Multi-model conversations with files, projects, memory and tool calls — local models and premium providers behind one gateway.
A supervisor coordinating frontend, backend, database, test, security and DevOps specialists, each with bounded permissions.
Prompt to application: architecture, schema, API, code and pipeline — every change backed by Git.
Build pipelines, preview environments, staging, production and one-step rollback.
Virtual machines, containers, Kubernetes, GPU, databases, storage and networking from one control plane.
Metrics, logs, traces, alerts and incidents, correlated with the deployment that caused them.
Identity, secrets, audit, scanning, firewall and policy — enforced by the platform, not by convention.
Metering, quotas, invoices and usage analytics, emitted as work happens rather than reconstructed later.
Every stage produces something you can inspect: a plan, a commit, a preview URL, a price, an Infrastructure as Code diff. Nothing irreversible happens on its own.
State the application, stack and deployment target in plain language.
Architecture, data model, API surface and repository scaffold come back as an executable plan.
Specialist agents write the code, run the tests and scan the dependencies.
An isolated environment to inspect and approve before production resources are spent.
Approved infrastructure is provisioned, the app rolls out, health is verified.
Metrics, logs and changes are correlated; fixes are proposed with the evidence behind them.
webyne-max; the router handles model versions, health and failover.| Pool | GPUs | Workload |
|---|---|---|
| Inference A | 8 | General & coding replicas |
| Inference B | 8 | General & coding replicas |
| Agent / RAG | 8 | Agents, embeddings, rerank |
| Reserved | 8 | Fine-tuning, dedicated, failover |
Agents never receive root, hypervisor or cloud credentials. They request typed tools, and every request is authorised on the server against the risk of the action.
| Risk | Examples | What happens |
|---|---|---|
| Low | Read metrics, inspect logs, generate code, run tests | Runs automatically |
| Medium | Restart a pod, deploy to preview, renew a certificate | Policy-controlled, tenant configurable |
| High | Production deploy, resize a database, change a firewall rule | Explicit approval |
| Critical | Delete data or a server, restore over production, broad IAM change | Approval + step-up authentication |
Isolation that depends on every query remembering a filter is isolation you cannot audit. Ours is enforced one layer down.
Remediation starts as a recommendation with its evidence attached: the metric window that breached, the log lines inside it, and the change that preceded it. You promote individual actions to automatic once you trust them.
| Restart an unhealthy pod | Recommend → auto |
| Renew a certificate | Recommend → auto |
| Scale stateless replicas | Approve → policy |
| Resize a database | Always approve |
| Delete a resource or data | Approve + step-up |
Bring a project you have been putting off. Describe it, watch the plan and the price come back, and decide whether to approve it.