AI Infrastructure and Optimization Services
AI infrastructure and optimization for production systems: inference cost, latency, vector database scaling, evaluation pipelines and observability, by senior engineers.
180 ms
p95 latency, from 1,200 ms
$4K
a month, from $12K
99.9%
uptime over six months
200K
queries a day of capacity
AI infrastructure and optimization is the work that keeps an AI feature fast, affordable and available once real users depend on it.
A pilot can run on one API key and a managed vector database. Under production load the same design often times out, costs more than budgeted or fails a data-residency review. Lightrains measures the system you have, finds where the latency and spend go, and changes the parts that matter: serving, retrieval, caching, model choice and monitoring.
What Lightrains improves
Cutting inference cost
Caching, routing simple requests to smaller models, trimming context, and quantized or distilled models where they hold up on the evaluation set. Spend is reported per feature.
AI budgets and cost controlReducing latency
Each step of the request path profiled, then parallel retrieval, response caching and serving changes, measured at p95 and p99 rather than on average.
AI model optimization techniquesScaling vector search
Vector database selection, index tuning and ingestion pipelines that keep the index fresh as the corpus and query volume grow.
Choosing a vector databaseSelf-hosting and data residency
Open-weight models and vector databases in your cloud account or data centre, when data cannot go to a third-party API.
Private and on-device AIFrom symptom to fix
Most systems need a few targeted changes, not a rebuild. Start with the change that addresses the symptom you can measure.
| If | Start with |
|---|---|
| Responses slow down at peak load | Request-path profiling, caching and parallel retrieval |
| The monthly model bill keeps growing | Per-feature cost reporting and model routing |
| Search quality dropped as the corpus grew | Retrieval evaluation and vector index tuning |
| A model or prompt change broke something in production | An evaluation set that runs before every release |
| Data cannot leave your network | Self-hosted open-weight models |
How the work is done
These are part of every optimization engagement. The linked pages explain how the Lightrains team implements them.
Baselines before changes
Latency, cost and error rates are measured per step on real traffic before anything changes, so every improvement is compared with a baseline.
Model routing
Requests are routed by difficulty: small or self-hosted models for simple steps, larger models only where the evaluation set shows they are needed.
Caching
Response and embedding caches with invalidation tied to ingestion, so repeat questions skip the model and answers stay current.
Quantization and distillation
Smaller, faster models for high-volume or latency-critical tasks, adopted only when they pass the same evaluation set as the original.
Evaluation on every change
The evaluation set runs on each model, prompt or retrieval change, so an optimization that costs accuracy is caught before release.
Observability
Traces, dashboards and alerts for latency, cost, error rate and retrieval quality, in Prometheus or the monitoring tools you already run.
A production RAG pipeline, redesigned
180 ms
p95 latency on a fintech compliance-search pipeline, down from 1,200 ms, after moving to a self-hosted Qdrant cluster with a Redis cache.
$12K → $4K
Monthly infrastructure cost for the same pipeline, with capacity raised from 25K to 200K queries a day.
99.9%
Uptime over the first six months in production, up from 97.5% on the previous architecture.
Source: Scaling a RAG pipeline with Qdrant, the engineering write-up of this project.
How an engagement runs
- 01
Step 1
Audit
Instrument the current system and measure latency, cost and quality on real traffic. You get a written report of where the time and money go, ranked by impact.
- 02
Step 2
Optimize
Change the highest-impact parts first. Each change is tested against the evaluation set and the baseline, then released to a share of traffic.
- 03
Step 3
Operate
Dashboards, alerts and cost budgets stay in place. Lightrains can keep running the system, or hand it to your team with documentation.
Can you optimize a system another team built?
Yes. Most optimization work starts on an existing system. The first step is measurement, so changes go where the latency and cost actually are rather than where they are assumed to be.
Will optimization reduce answer quality?
It should not, and the evaluation set is how you know. Smaller models, quantization and caching are adopted only where they pass the same evaluation set as the original setup.
Do we need to change cloud provider or vector database?
Usually not. Caching, routing and tuning what you already run come first. A migration is recommended only when the measurements show the current component cannot meet the target, as with the fintech RAG pipeline that moved to a self-hosted Qdrant cluster.
How much can we save?
It depends on where the spend goes today, and the audit gives an estimate for your system before any optimization work starts. On one fintech RAG pipeline, monthly infrastructure cost fell from $12K to $4K.
Do you work with GPUs and self-hosted models?
Yes. Lightrains sizes and serves open-weight models in your cloud account or on your own hardware, and compares them with hosted APIs on cost, latency and accuracy. Private and on-device AI covers those deployments.
Who owns the code?
You do. All IP belongs to you, access to your systems is VPN-only under NDA, and payments are tied to milestones.
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