Add AIto your product._

Taking an AI MVP to production requires reliable model serving, compute allocation, cost control, data connections, and request security. QDivZero brings models, deployments, routing, and security into one layer so the team can scale the product without building and maintaining the entire AI infrastructure stack separately.

Choose a compatible Hugging Face model and define the region and workload requirements. QDivZero finds suitable GPU compute across providers, prepares the inference environment, and creates an endpoint to serve the model, without requiring you to configure servers, drivers, or runtimes manually.

An AI API provides a model already hosted by a vendor and usually limits your choice to its catalog. An open-weight model gives you control over the architecture and execution environment, but it requires compute and inference infrastructure. QDivZero provides that operational layer while keeping a stable API integration.

Dedicated compute is often useful when traffic is sustained, predictable latency matters, or you need stronger isolation and operational control. Active compute time is prorated by the second and deducted from balance instead of being charged per processed token. In the same account, you can keep balance for Public Models and activate Unlimited Basic with Qwen 3.8 27B for a fixed monthly fee.

Start by measuring cost per task rather than token price alone. Reserve the most capable models for complex requests, route simpler work to efficient models, remove unnecessary context, and compare dedicated compute. LLM Model Routing can distribute requests by cost, latency, quality, and availability.

Yes. QDivZero provides an OpenAI-compatible API, allowing you to keep the familiar request format while changing the model behind the endpoint. This reduces integration work when testing an open-weight LLM, moving a workload to another provider, or combining several models in one product.

LLM Model Routing brings multiple models behind one access layer and decides which one should handle each request. Rules can consider task type, cost, latency, or quality and define fallbacks when a model is unavailable. Each workflow can use the right LLM without creating a separate integration.

A RAG system turns content into embeddings, indexes that information, and retrieves relevant passages before generating an answer. QDivZero lets you combine embedding models, semantic search, and LLMs to query documentation, catalogs, or knowledge bases with company-specific context.

Security policies should run before a request reaches the model. LLM Firewall analyzes prompts and responses for prompt injection, jailbreaks, sensitive data, abuse, and disallowed content. Depending on the policy, a request can continue, be blocked, modified, or recorded for audit.

Yes, when the selected deployment and provider offer the required region. Choosing where a model runs can keep inference close to users and support internal data location, privacy, or residency requirements. Region selection should be evaluated together with the model, compute, and project policies.

Define the model, region, and workload requirements; QDivZero finds compatible compute, prepares the deployment, and keeps the endpoint available. You can add routing, data retrieval, and security as the product needs them, without building an internal MLOps platform for every new capability.

Keep a provider-independent access layer instead of spreading a proprietary API throughout the application. Open-weight models, multi-provider compute, and an OpenAI-compatible API let you test alternatives, move a workload, and configure fallbacks without rebuilding the core product logic.