AI Inference Endpoints
Production-grade model serving behind a scalable API — autoscaling, canary rollouts, A/B testing, and p99 latency observability. Models warm to eliminate cold starts and cache answers for repeated requests. One endpoint to take any trained model live.
Overview
A trained model does not earn until it serves a request. AI Inference Endpoints turn trained models into production-grade, low-latency APIs served on NVIDIA GPU infrastructure with automatic scaling, model warm-up to eliminate cold-start latency, and optimised inference runtimes including TensorRT-LLM, vLLM, ONNX Runtime, and Hugging Face TGI. Every endpoint supports FP16, INT8, and INT4 quantisation with KV-cache sharing and continuous batching for transformer models — achieving sub-100ms p99 latency on models up to 70B parameters. The serving infrastructure autoscales from zero GPU replicas to hundreds on request volume with canary deployments, A/B traffic splitting, and automatic rollback on error-rate increase. We make model serving a solved problem so the model proven in the notebook becomes a production API endpoint the same afternoon.
Clevertek scopes every engagement to your environment — capacity, sites, compliance and support model — so you get a tailored plan rather than a fixed SKU. Pricing is quote-only, and our solutions architects will work through your requirements before any proposal.
Our approach
We deploy and operate production-grade model inference endpoints on NVIDIA GPU infrastructure with optimised serving runtimes, elastic auto-scaling, and comprehensive observability. Every endpoint runs on NVIDIA A100, H100, H200, or B200 GPUs with TensorRT-LLM optimisations, vLLM PagedAttention for efficient KV-cache management, and ONNX Runtime for cross-framework model conversion. Endpoints support Open AI-compatible API format for drop-in integration with existing applications, with stable URLs that persist across model version updates. We manage canary deployments with configurable traffic splitting, automatic rollback on latency or error-rate degradation, and per-endpoint monitoring across p50/p95/p99 latency, throughput, error rate, GPU utilisation, and cost per inference. Model warm-up with representative request payloads eliminates cold-start latency on scale-from-zero, and the autoscaler adjusts GPU replica count on request-level metrics with sub-minute response to traffic spikes.
Why work with us
Optimised inference across GPU generations
TensorRT-LLM, vLLM, ONNX Runtime, and TGI with FP16/INT8/INT4 quantisation, FlashAttention, PagedAttention, and continuous batching. Models served on NVIDIA A100, H100, H200, and B200 GPUs.
Sub-100ms p99 latency on scale
Model warm-up eliminates cold-start penalty on zero-to-one scaling. Continuous batching and KV-cache sharing maximise GPU utilisation while maintaining sub-100ms p99 for transformer models up to 70B parameters.
Elastic auto-scaling with zero idle GPU cost
Scale from zero to hundreds of GPU replicas based on request queue depth and latency. No always-on GPU cost for variable-traffic workloads; the autoscaler reacts to traffic changes in under 60 seconds.
Safe deployment with canary and rollback
Canary deployments split traffic gradually between model versions. Automatic rollback triggers on latency increase, error-rate spike, or throughput degradation. Every deployment is reversible without a rebuild.
Key benefits
What this solution delivers for your business.
Sub-100ms p99 inference latency
TensorRT-LLM optimisations, FlashAttention, and continuous batching deliver sub-100ms p99 response times for transformer models up to 70B. Your application feels responsive even under peak load.
Autoscale from zero — no idle GPU cost
Endpoints scale from zero replicas when idle to hundreds under load. Pay only for the GPU hours consumed by inference requests, not for standing capacity waiting for traffic.
Zero-downtime model updates
Canary deployments with gradual traffic shift and automatic rollback on degradation mean model updates happen without service interruption. A bad version is caught on 1% of traffic, not 100%.
Full cost and performance observability
Per-endpoint dashboard showing p50/p95/p99 latency, throughput, error rate, GPU utilisation, and cost per inference. Tune batch size, model precision, and replica count based on real traffic patterns.
What's included
Part of this managed service.
Optimised inference runtimes
TensorRT-LLM, vLLM, ONNX Runtime, and Hugging Face TGI with FP16, INT8, and INT4 quantisation. FlashAttention-2, PagedAttention, and continuous batching for maximum GPU throughput on transformer architectures.
- TensorRT-LLM with FP8 and INT4 quantisation
- vLLM with PagedAttention and prefix caching
- ONNX Runtime for PyTorch and TensorFlow models
- Hugging Face TGI with continuous batching
Elastic GPU auto-scaling
Replica count adjusts automatically on request-level metrics — queue depth, latency, and throughput. Scale from zero to hundreds of GPU replicas with sub-minute response to traffic changes. Model warm-up eliminates cold-start on scale events.
- Zero-to-many scale on request volume
- Model warm-up with representative payloads
- Sub-minute scale-up on traffic spike
- Scale-to-zero when idle
Model versioning and safe deployment
Versioned model registry with canary deployment, A/B traffic splitting, and automatic rollback. Deploy a new model version to 5% of traffic, monitor, and ramp to 100% — or roll back instantly on metric degradation.
- Version registry with metadata
- Canary deployment with configurable split
- A/B testing across model versions
- Auto-rollback on error-rate increase
Production observability
Per-request latency distribution (p50/p95/p99), throughput, error rate, GPU utilisation, memory bandwidth, and cost per inference. All surfaced in a real-time dashboard with configurable alert thresholds.
- Latency percentiles per endpoint
- Throughput and error-rate dashboards
- GPU utilisation and memory metrics
- Cost per inference tracking
Where it helps
Real-world scenarios where this solution delivers measurable outcomes.
LLM-powered chat application
Serve a fine-tuned 70B parameter LLM behind a low-latency API with sub-100ms p99 response time. Autoscaling handles peak chat traffic during business hours and scales to zero overnight, paying only for inference GPU hours consumed.
Batch document processing pipeline
Deploy a document extraction model (layout parsing, OCR, entity recognition) as an inference endpoint. The batch pipeline sends thousands of documents through the API, and the autoscaler provisions GPU replicas to match throughput requirements then scales down when the batch completes.
Questions buyers actually ask
Do I need AI Studio to use Inference Endpoints?
No. Inference Endpoints accept trained models from any source — AI Studio, custom training, third-party platforms. The endpoint API is Open AI-compatible, so existing applications integrate without client-side changes.
How does autoscaling from zero work without cold-start delay?
Model warm-up sends a configurable number of representative inference requests to the freshly provisioned GPU replica before the endpoint accepts production traffic. This pre-loads model weights, KV-cache structures, and CUDA kernels so the first production request sees steady-state latency.
What ML frameworks and model formats are supported?
PyTorch, TensorFlow, JAX, and ONNX models served through TensorRT-LLM, vLLM, ONNX Runtime, or TGI. Custom model architectures supported via custom Docker images with the serving runtime of your choice.
How do you control cost per inference?
Cost per call is tracked and surfaced in the observability dashboard. Optimisations — model precision (FP16, INT8, INT4), batch size tuning, KV-cache management, and scale-to-zero — each reduce cost per inference. We baseline and tune these during endpoint setup.
Ready to scope a solution?
Talk to a Clevertek solutions architect about your requirements — no obligation.