Models that stay up under load.
We deploy and serve AI models at production scale, sub second latency, multi region failover, autoscaling that doesn't waste GPU hours, and the SLOs your product team can actually commit to.
Production serving
done right.
A demo on a single GPU is a Tuesday. Serving the same model at 10K RPS, p99 under 500ms, with rolling deploys and rollback, that's the actual job.
Low Latency Inference
Quantization, speculative decoding, KV cache optimization, and token streaming, squeezing every millisecond out of the serving path.
Multi Region Deployment
Active active across regions, with health aware routing and failover that doesn't drop in flight conversations.
Autoscaling Done Right
Predictive autoscaling on real signals, request queue depth, token throughput, GPU utilization, not just CPU.
Smart Model Routing
Dynamic routing across model tiers, frontier model for hard queries, cheaper models for the rest, saving cost without losing quality.
Edge & On Device
For ultra low latency or sovereign use cases, model deployment at the edge, on customer premises, or on device.
Safe Rollouts
Canary deploys, shadow traffic, blue green cutovers, and instant rollback, model updates that don't take down production.
From notebook to battle tested infra.
We treat model serving like any other production system, with load testing, observability, and on call rotations from day one.
Workload Profiling
We characterize your traffic, request size, peakiness, latency budget, so the architecture matches reality, not a hypothesis.
Stack Selection
vLLM, TGI, Triton, SGLang, we pick the serving stack that maps cleanly to your model, hardware, and operational team.
Load & Chaos Testing
Synthetic load that matches your worst case real traffic. We break the system on staging so it doesn't break in production.
Production & SLOs
Cutover with monitoring on latency, availability, and quality SLOs, and on call playbooks your team can run alone.
Questions about
Model Deployment & Serving
vLLM, Triton, TGI, SGLang for LLMs; ONNX Runtime, TorchServe, BentoML for traditional ML. We pick based on the model, hardware, and your team's familiarity.
Yes. Quantization, KV cache reuse, prefix caching, dynamic batching, and speculative decoding can deliver 2/5x throughput improvements without touching weights.
AWS, GCP, Azure, Oracle, and bare metal on prem. NVIDIA H100 / A100 / L40S, plus AMD MI300 and inference optimized CPUs where the workload allows.
99.95% availability and sub 500ms p95 are routine for most LLM workloads. Sub second voice and sub 100ms classification are achievable on right sized hardware.
Shadow traffic, canary, blue green, with eval gates between each stage. Every deploy can be rolled back in seconds, and we test rollback before launch.
Stop experimenting.
Start deploying AI that works.
Book a free discovery call. Share your traffic profile and SLO targets, we'll tell you what's possible and what it costs.
info@croncore.com