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Serving production models at scale with low latency, controlled cost, full observability and enterprise governance — from autoscaling REST endpoints to batched high-throughput pipelines.
Inference is where AI delivers value to users, customers and downstream systems — and that value depends entirely on reliability, latency and cost predictability. A model that produces brilliant outputs is worthless if it takes three seconds to respond or costs more per query than the business case allows. NOVACORE inference infrastructure is designed from first principles for production serving: autoscaling pools that match capacity to demand, quantised model serving that reduces hardware cost without degrading quality, dedicated and shared deployment patterns, and full observability into every request — latency, token throughput, error rates and cost attribution. Governance is embedded at the serving layer: approved model registries, guardrail enforcement, content filtering and audit logging ensure that every inference endpoint is compliant, monitored and auditable.
Fast, consistent responses for production workloads. Target sub-100 ms time-to-first-token for interactive applications and streaming token generation with predictable inter-token latency. Model serving infrastructure optimised for real-time user-facing deployments where every millisecond counts.
Capacity automatically matches demand without waste. Inference pools scale based on request queue depth, GPU utilisation and latency targets. Scale up to absorb traffic spikes during business hours or product launches; scale down during quiet periods to control cost — all without manual intervention.
Cost per request monitored, attributed and optimised. Real-time cost analytics show spend by model, endpoint, tenant and time period. Set budget alerts and spending caps to prevent surprise bills. Quantised model serving reduces per-token cost by up to 4x compared to full-precision deployment.
Metrics, logs and traces for every request. Monitor token generation rate, time-to-first-token, inter-token latency, GPU memory utilisation and request error rates across all endpoints. Integrated dashboards with Prometheus and Grafana, plus structured logging exportable to customer SIEM platforms.
Tenants and workloads kept strictly separate. Each inference endpoint runs in its own namespace with dedicated GPU allocation, memory limits and network policies. No shared model caches, no cross-tenant KV-cache leakage, no risk of prompt or response data mixing between customers.
Approved model registries, guardrail enforcement and audit logging at the serving layer. Only authorised models can be deployed to production endpoints. Content filtering, PII detection and output validation rules are enforced before responses reach users. Every inference request is logged with full traceability.
Different inference workloads demand different deployment patterns. NOVACORE supports the full spectrum — from dedicated always-on endpoints for mission-critical applications to cost-optimised shared pools for batch processing and experimentation.
Secure AI and high-performance computing for enterprises, governments and research.