AI/ML Consulting Services · Last reviewed
AI infrastructure consulting for production teams.
AI infrastructure consulting is outside engineering help with the systems that run AI in production: model serving, GPUs, retrieval, agents, observability, and cost controls. NavyaAI does this for CTOs and ML platform teams whose pilot works but whose bill, latency, or reliability does not hold at production scale. Every engagement starts with a free written audit of where cost and risk sit.
See Applied AI Development- $47K → $28K
- one client's monthly bill, before and after
- A consulting audit moved a Llama 3 70B workload from 4 GPUs to 2 with quantization and serving changes. See the data
- 2.3x
- more traffic served on half the GPUs you pay for
- The same engagement more than doubled tokens served per second while cutting the GPU count. See the data
- 72%
- of your AI spend sits outside the model call
- Architecture decisions (orchestration, retrieval, evaluation, observability) control most of the real AI budget. See the data
What goes wrong
The pilot works, production does not
The demo had ten users and no retries. At real traffic the same design queues, times out, and has no rollback when a prompt or model change makes answers worse.
The inference bill is unpredictable
Spend grows faster than usage because agent loops, retries, and oversized context multiply tokens per task. Nobody can say what one completed workflow costs.
Nobody can see why it fails
An agent stalls or a RAG answer goes wrong and there is no trace to read. Security and access rules were added after launch, so they are checked by hand.
How this plays out in practice
The layers an AI infrastructure engagement covers
AI infrastructure is a stack, and cost or reliability problems can start at any layer. From the top: the application layer (LLM apps, RAG, and agents), the operations layer (evaluation gates, tracing, release and rollback), model serving (batching, quantization, caching), orchestration and autoscaling, GPU and CPU compute, storage and networking, and the environment it all runs in: cloud, hybrid, or private.
We work across those layers and measure before recommending. For serving and compute, that means GPU sizing against your latency target and utilization data from your own traffic. For the environment decision, it means break-even math between API, rented GPUs, and on-prem or colocation hardware. For operations, it means the release, evaluation, and incident process covered in our MLOps work.
Security and governance sit across every layer: which data may reach which model, who can call it, and what gets logged. Those rules are cheapest to build in at design time, which is one reason the audit comes first.
Why audits come before re-architecture
Most AI consulting engagements start with a proposed solution (migrate providers, buy GPUs, adopt a framework) and work backwards to justify it. We run the sequence the other way: measure where cost and risk actually concentrate, then size the smallest intervention that moves the number.
The reason is empirical. In our audits the expensive problem is rarely where the team expects it: a routing policy nobody wrote, retrieval stuffing context into every call, retry storms hidden behind a provider SDK, or GPUs running at a fraction of utilization. The Llama 3 70B case study is typical: no migration, no new vendor, a 42% cost cut from tuning what was already there.
Build versus buy, with the numbers attached
The recurring strategic question is whether to keep paying API margins, optimize in place, or bring serving private. Each answer is right for some traffic shape, and the consulting work is making your traffic shape visible: token volume and growth, latency and quality floors, privacy constraints, and the engineering capacity available to operate infrastructure.
We bring benchmark data to that decision. Our optimized 70B-class self-hosting measured roughly $0.47 per million tokens, against $4.67 on a mid-tier API such as Claude Sonnet 5.5 and $0.23 on GPT-6 Luna (blended at two input tokens per output token, list prices verified October 3, 2026). We also count the operations cost that most break-even spreadsheets omit. Sometimes the recommendation is to not build: low or volatile volume keeps APIs the right answer, and the cheapest API tier can undercut private serving outright. The engagement says so in writing.
What we check first
The first pass looks for the smallest change that moves the bill: routing, caching, prompt control, serving tuning, or a deeper break-even audit.
- AI application architecture and model route selection
- RAG, agent, and retrieval design review
- LLM serving cost and reliability risks
- Evaluation, observability, and deployment plan
- Build vs buy and vendor lock-in decisions
Consulting engagement map
We focus on decisions that affect production cost, reliability, and delivery risk.
| Area | Risk | First question |
|---|---|---|
| LLM apps | No routing or eval policy | Which tasks need which model? |
| RAG | Retrieval quality hides model waste | Which chunks actually answer users? |
| Agents | Loops and tools inflate spend | What is the stop condition? |
| MLOps | No release path for models/prompts | How are changes evaluated? |
| Model serving | GPUs run far below capacity | What is the cost per workflow? |
| Compute and orchestration | Capacity bought before measurement | How many GPUs does the latency target need? |
| Cloud, hybrid, or private | Deployment chosen before break-even math | Where does the data have to stay? |
| Security and governance | Controls bolted on after launch | Who can call which model with which data? |
What an engagement finding looks like
Findings from the Llama 3 70B audit engagement — each one a measured leak, the lever applied, and the result.
| Finding | Lever applied | Measured result |
|---|---|---|
| FP16 weights on 4 GPUs at ~30% utilization | INT8 (GPTQ) quantization | 45% less VRAM, model fits on half the GPUs |
| Default KV-cache eating batch headroom | KV-cache pruning (H2O, 50% budget) | ~30% cache memory freed for batching |
| Low throughput per GPU-dollar | Continuous batching + serving tuning | 2.3x throughput, $0.82 → $0.47 per M tokens |
| No cost telemetry per workflow | Token and utilization instrumentation | Cost per action visible; regressions now gated |
Full methodology and benchmark data published in the Token Tax post and the Llama 3 70B case study.
How the audit works
Share spend and workload shape
Submit monthly spend range, provider mix, token volume, and what your AI systems actually do: chat, RAG, agents, extraction, or batch jobs. Takes minutes, no production access needed.
Map cost to workflows
We break the invoice into cost per completed user action: prompt and output tokens, retries, retrieval, tool calls, and orchestration overhead — the 72% of AI cost that hides outside the model call.
Rank levers by friction and savings
Each leak gets a lever — routing, caching, prompt compression, retry control, quantization, or a private break-even case — ranked by expected savings against implementation effort.
Get the written read, then decide
You receive the first cost-leak read in writing. If the economics justify deeper work, the next step is a scoped engagement; if not, you keep the findings.
A measured example
$47K → $28K
Case study: a Llama 3 70B production workload moved from 4 GPUs to 2 with INT8 quantization, KV-cache pruning, and serving changes — a 42% monthly cost cut with 2.3x throughput.
Read the full auditStart with your name and work email.
Our AI/ML, HPC and MLOps engineers investigate and reply in writing with a verdict on where to look first. No call required.
Common questions
What is AI infrastructure consulting?
AI infrastructure consulting is advisory and engineering work on the systems that run AI in production. It covers the full stack under an AI product: GPU and CPU compute, storage and networking, orchestration, model serving, the retrieval and agent layer, and the monitoring and cost controls around them. The goal is a system that stays reliable and affordable as usage grows.
What does an AI infrastructure consultant do?
An AI infrastructure consultant measures how your AI system behaves in production, finds where cost and risk concentrate, and designs the fix. In practice that means sizing GPUs against a latency target, choosing between API, hybrid, and private serving, setting up evaluation gates and tracing, and writing down what each workflow costs. At NavyaAI the first deliverable is a written audit, before any paid work.
When do you need an AI infrastructure consultant?
Usually at one of four moments: a pilot is about to take production traffic, the monthly AI bill has outgrown its forecast, an agent or RAG system fails in ways nobody can trace, or you have to decide whether to keep paying API prices or bring serving in-house. If none of those apply yet, you probably do not need one.
What does AI infrastructure consulting include?
AI infrastructure consulting includes architecture review, model route selection, RAG and agent design, GPU sizing and deployment planning across cloud, hybrid, and private environments, observability, access and data-boundary controls, cost controls, and production reliability planning.
Is ML infrastructure consulting different from AI infrastructure consulting?
In practice they describe the same engagement from different starting points. ML infrastructure consulting usually begins with training, pipelines, and model lifecycle. AI infrastructure consulting usually begins with LLM serving, RAG, and agent workloads. Both arrive at the same three questions: what does a workflow cost, where does it break, and what runs it. We take either entry point.
Do you cover LLM operations as well as architecture?
Yes. Architecture no one operates tends to regress within a quarter. LLM operations work covers routing policy, evaluation gates, observability, incident response, and the monthly cost review that catches drift before it reaches the invoice.
Is NavyaAI an AI development company or a consulting team?
NavyaAI does both. We advise on architecture and cost decisions, then help implement production AI systems when the engagement needs engineering delivery.
Who is a good fit for NavyaAI consulting?
The best fit is a team with production AI usage, meaningful monthly AI spend, or a near-term decision about APIs, RAG, agents, GPUs, MLOps, or private deployment.
How much does an AI infrastructure consultant cost?
The entry point is free: the AI inference audit intake produces a written first read on cost and architecture leaks at no charge. Paid engagements are scoped after that and sized to the decision at hand. An architecture review prices very differently from a deployment build-out, so you see the findings before committing budget.
What does an AI infrastructure audit deliver?
A written map of where spend and risk concentrate: cost per workflow, model routing opportunities, RAG and agent overhead, serving utilization, and self-host break-even math where volume justifies it. A recent audit of this type cut a Llama 3 70B serving bill 42%, from $47K to $28K per month.
How long does an AI infrastructure engagement take?
The free audit read arrives within days of intake. Scoped engagements typically run from a focused architecture review measured in weeks to ongoing advisory across a quarter. The audit findings determine which shape fits.