For organizations
Give internal teams a governed chat and API service for approved models without sending prompts or data to a public AI provider.
LLMaaS
Available on request
Private AI services on GPUs you control.
WECORE Cloud deploys and operates managed model serving for your organization or customers, with approved open-weight models, a chat interface, and an OpenAI-compatible API on your infrastructure.
Managed model serving
OpenAI-compatible API
Curated open-weight models
Private by design
The product view below shows the private chat and API experience delivered on GPU capacity your organization owns or leases.
LLM workbench preview with sample data; the service, model and conversation are illustrative only.
A managed LLM service for your organization or customers
Commission a private AI service on GPU infrastructure you control, with a clear boundary for prompts, application data, models, and access.
For service providers
Turn your GPU infrastructure into a managed model-serving offer with a curated catalog, tenant access, usage records, and service policies.
What your users can do
Use an approved model through a private chat interface or OpenAI-compatible API and connect organizational applications without changing familiar client libraries.
What your infrastructure team gets
Control model approval, GPU allocation, API access, identity, usage records, data boundaries, monitoring, updates, and service lifecycle.
How WECORE deploys and operates it
We validate GPU and model compatibility, deploy the serving stack on your infrastructure, integrate access and monitoring, and provide the agreed ongoing operations.
Dedicated
One GPU server per service
OpenAI-compatible
Drop-in /v1 chat & completions API
Open models
Verified catalog plus compatible open-weight models
Self-hosted
Runs on your GPUs, data stays put
Workflow
From model to endpoint, without leaving your cloud.
WECORE creates a dedicated LLM service for an open-weight model approved by your organization. The service runs on one GPU server in your organization's project and includes a built-in chat tab and an OpenAI-compatible API: without requiring your team to assemble the serving stack.
1
Pick a model
Choose a curated open-weight model from the deployment catalog for your use case: following user instructions, conversation, or code generation.
2
Choose GPU resources
Select a GPU configuration and initial resource allocation from capacity your organization controls. Checks before launch confirm that the model fits the available GPU memory and quota.
3
Launch in one step
WECORE sets up the model service and web chat for you. The model's pinned weights are downloaded from Hugging Face and served (first start takes minutes for large models) service health is checked, and access is protected by a per-service API key.
4
Chat and integrate
Use the built-in web chat, or point your apps at the OpenAI-compatible endpoint with the service's API key, streaming responses included.
Use cases
What teams run on the service
Each use case includes curated model presets, suitable GPU sizing, and a chat or API interface configured for the task.
Chat
Private chat & assistants
Stand up an internal assistant on a private endpoint: an alternative to public chat services, where prompts and conversations stay inside your deployment.
Qwen2.5-7B
Nemotron Nano 9B
Developer tooling
Code assistance
Serve an approved model behind the OpenAI-compatible API for IDE assistants and code-review workflows, on a dedicated GPU server in your deployment.
Nemotron Nano 30B-A3B
Qwen2.5-7B
Reproducible runs
Research & experimentation
Give research and application teams a private endpoint with pinned model versions and per-service keys, so experiments stay reproducible and access stays scoped.
Nemotron Nano 4B
Qwen2.5-0.5B
Platform
A focused control layer for open-source model serving.
LLMaaS is not another model. It brings open-weight models, managed serving, and a web interface together on GPU capacity you already own, with deployment and access controls built in.
llm-service
Sample record
model: Qwen2.5-7B-Instruct
engine: vLLM
api: openai-compatible /v1
plan: gpu plan, fit checked before creation
access: https + per-service API key
state: Ready
Sample service record; the values are illustrative only.
Curated model catalog
Start with open-weight models approved by your organization, fixed versions, and suitable default settings. The catalog shows the models available for this deployment.
OpenAI-compatible endpoint
A stable /v1 chat and completions API lets existing SDKs, AI agents, and tools connect by using the LLM service's API address and per-service API key.
Compatibility checked before deployment
Each model runs as a managed service on a dedicated server. We verify GPU memory, context limits, and plan eligibility before creating the service.
Published service status
The service address is published only after the model server passes its health checks, and the panel shows the service's state through provisioning, ready, and stopped.
Private by design
Prompts, completions, and weights stay on storage inside your cloud, at inference time, no third-party API calls and no data egress to a model vendor.
Check fit and plan cost before creation
Review GPU-memory fit, project quota, and the configured plan's hourly estimate before creating the service. You can stop and start the service yourself at any time.
Open stack
Open-source model serving, assembled and managed.
LLMaaS packages components that research and platform teams already trust into one self-service deployment flow.
Model serving
Model serving with vLLM
OpenAI-compatible API
Hugging Face model weights
GPU acceleration
Interfaces
Web chat UI
REST + SDKs
Streaming responses
Models
Qwen2.5
NVIDIA Nemotron
Open-weight models per deployment
Platform
Your own GPU capacity
Dedicated server per service
Per-service API keys
Health checks & service state
Why WECORE Cloud
Hosted LLM APIs compared with self-hosted
Dimension
Hosted LLM APIs
WECORE LLMaaS
Where it runs
Vendor SaaS, shared tenancy
GPU capacity you own, inside your cloud
Where data is stored
Prompts and data leave your network
Prompts, weights, and logs stay on your cloud
Model control
Provider's models and versions
Organization-approved open-weight models with fixed versions
Integration
Proprietary endpoints
OpenAI-compatible API: use the service's API address and key
Cost model
Per-token, metered per request
Configured plan's hourly estimate, shown before creation
FAQ
About LLM-as-a-Service
What the service does, what it provides, and how it fits WECORE infrastructure
It is a dedicated LLM service: one open-weight model approved by your organization runs on one GPU server in your OpenStack project. The service includes a built-in chat tab and an OpenAI-compatible API protected by a per-service API key.
Choose from the open-weight models your organization has approved for this deployment. The verified catalog ships the Qwen2.5 and NVIDIA Nemotron families as tested configurations. Beyond it, WECORE can deploy freely released open-weight models (Llama variants, Mistral, and DeepSeek releases among them) wherever the licence permits deployment and the model fits the available GPU configuration and serving environment. Every catalog entry gets a fixed version and suitable defaults, checked against your GPU configuration and context limits.
No; the catalog is curated by your organization from the approved model families. The service serves models: it does not train or fine-tune them, and it does not host customer-supplied weights.
Through an OpenAI-compatible /v1 chat and completions API. Enter the LLM service's API address and per-service API key in your existing SDKs, AI agent frameworks, and tools: no code rewrite needed. A built-in web chat is included for people who don't want to write code.
Nowhere outside your environment. Prompts, completions, model weights, and logs stay on storage assigned to your organization within infrastructure you control. At inference time there are no third-party API calls and no data transfer to a model vendor. At creation, the service downloads the approved model's pinned weights from Hugging Face (the one outbound dependency) and first start takes minutes depending on model size.
On GPU capacity inside infrastructure your organization controls. Each model runs as a managed service on one dedicated server in your organization's project, protected by a per-service API key. WECORE operates the serving layer on that infrastructure. If you don't run a cloud foundation yet, the engagement can include deploying the open-source infrastructure layer the service runs on.
WECORE deploys the service and operates the serving layer. Your infrastructure team owns model approval, GPU allocation, API access, rates, and data boundaries; your users own their services, prompts, and keys. WECORE monitors service health, coordinates upgrades, and works incidents within the responsibility split agreed for your deployment. Panel access is role-based, and administrative actions are recorded in tamper-evident audit records. Hardware and facility stay with the infrastructure owner unless included separately; the pricing page carries the full responsibility and access model.
Yes. Service diagnostics show readiness and health, and the panel shows the service's state through its lifecycle, including explicit stop and start to control cost. Before creation, the panel shows the configured plan's hourly estimate; rating and commercial terms are defined for the deployment.
Those are general-purpose platforms you build on: KaaS for containerized applications, the HPC platform for managed batch and parallel computing. LLMaaS is a finished product rather than a platform: it serves open models behind a chat UI and an OpenAI-compatible API in one step, so your team does not have to assemble the model-serving stack. You don't need either product to use it.
Available on request
Run a private model service on your GPUs
WECORE LLMaaS is for teams that want the convenience of a hosted LLM API without sending prompts and data to a public vendor: open models, an OpenAI-compatible endpoint, and a chat UI managed as one service. Ask for a live walkthrough in your briefing, from picking a catalog model to the first streamed response.