Inference that scales without surprises.
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.
The part nobody budgets for
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.
The anatomy of a production serving layer.
Every layer is engineered and measured — not a single model call behind a public endpoint.
- Continuous / in-flight batching
- Paged-attention KV-cache
- Request scheduling
- INT8 / INT4 quantization
- Speculative decoding
- Tensor / pipeline parallelism
- Scale-to-load autoscaling
- Queueing & backpressure
- Failover & multi-model routing
- Cost-per-token tracking
- Token & latency telemetry
- Capacity & saturation alerts
Where inference engineering pays off
Inference work earns its keep the moment AI moves from a pilot to something real users hit every day:
- Cutting cost per answer — higher utilization and a right-sized, quantized model turn an unaffordable unit cost into a margin you can plan around.
- Meeting latency SLAs — predictable time-to-first-token and response time even when concurrency climbs, so the product feels fast under real load.
- Surviving traffic spikes — autoscaling, queueing, and backpressure absorb the launch-day surge instead of dropping requests on the floor.
- Serving many models efficiently — multi-model routing and shared capacity so a fleet of models runs without a dedicated cluster per use case.
- Capacity visibility — telemetry that tells you when you are about to run out of headroom, before users feel it.
From scope to production.
Fixed scope, fixed price, twelve weeks from briefing to live deployment.
Common questions.
How do you reduce LLM inference cost?
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.
Can you hit low-latency SLAs at high concurrency?
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.
What is quantization and will it hurt quality?
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.
Explore related capabilities.
Inference management by industry.
How production inference maps to the realities of each regulated vertical we serve.
Ready to make inference reliable and affordable?
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.
Experienced within
Markets served.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: