A model that works in a notebook is not a system. We engineer production model serving — throughput/latency engineering, batching, quantization, autoscaling, and cost-per-token observability so inference is reliable and affordable at load.
Every AI project plans for the model and the prompt. Almost none plan for what happens when real traffic arrives. A model that answers instantly for one user in a notebook melts under a hundred concurrent requests — latency balloons, the GPU pins at 100%, and requests start timing out. Or worse, it runs fine and quietly burns money on every single token, turning a successful launch into a line item nobody can defend.
Inference is the layer where AI either becomes operable or becomes a liability. It is where capability meets unit economics: how fast each answer comes back, how many you can serve at once, and what each one costs. We treat that as a first-class engineering problem — serving stack, optimization, scaling, and the observability to prove all three are holding.
Every layer is engineered and measured — not a single model call behind a public endpoint.
Inference work earns its keep the moment AI moves from a pilot to something real users hit every day:
Fixed scope, fixed price, twelve weeks from briefing to live deployment.
Cost per token falls when the GPU stays busy and the model stays small enough to do the job. We combine continuous batching to raise utilization, quantization to shrink the memory and compute footprint, KV-cache reuse to avoid recomputing context, and right-sized model routing so cheap requests never touch an expensive model. Then we track cost per token directly, so every optimization is a measured saving rather than a guess.
Yes — that is the core engineering problem. We tune time-to-first-token and inter-token latency with continuous/in-flight batching, paged-attention KV-cache management, speculative decoding, and tensor/pipeline parallelism, then protect the SLA under load with request scheduling, queueing, and backpressure. We load-test against your real traffic shape so the latency target holds at peak concurrency, not just in a quiet benchmark.
Quantization stores and runs the model at lower numerical precision — INT8 or INT4 instead of 16-bit — which cuts memory use and speeds up inference, often letting a model fit on smaller or fewer GPUs. Done carefully with modern methods, quality loss is minimal to negligible for most workloads. We validate every quantized model against an evaluation set drawn from your real tasks, so the speed and cost gains never come at the expense of an accuracy bar you have not agreed to.
How production inference maps to the realities of each regulated vertical we serve.
Bring the model you are running and the load you expect. In thirty minutes we will show where the throughput, latency, and cost-per-token wins are — and how we will measure them. Response inside 24 hours.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: