AI changes ship without eval gates
Prompt, model, retrieval, and data changes can degrade quality or cost without a release system.
MLOps and AI DevOps
Last reviewed MLOps consulting for AI systems that need to keep running.
NavyaAI helps teams build the operational layer for production AI: model and prompt release flows, eval gates, observability, incident response, infrastructure automation, and cost monitoring across LLM and ML systems.
72%
of AI spend hides outside inference
~30%
GPU utilization found in a recent audit
42%
cost cut once measured
Incidents lack useful signals
Latency, token spend, hallucination risk, retries, and provider failures need first-class telemetry.
Infrastructure scales before it is measured
GPU and API spend grows faster when utilization and cost per workflow are not tracked.
Deep Dive
The invoice shows totals; telemetry shows causes
Every provider invoice answers one question — how much — and none of the questions that matter operationally: which feature, which prompt change, which retry storm, which team. The gap between those two views is where AI budgets quietly erode. Our cost report found 72% of production AI spend sitting outside the model invoice, in orchestration, retrieval, and operations that no provider dashboard itemizes.
MLOps consulting closes that gap with workflow-level telemetry: every model call tagged with feature and team metadata, token and retry logging, cache hit rates, and utilization counters — aggregated to cost per completed action. Once that number exists per feature, cost regressions become visible the day they ship instead of the month the invoice lands.
Eval gates are cost controls, not just quality controls
Prompt edits, model upgrades, and retrieval changes ship constantly in LLM systems, and each one can silently change token consumption: a longer system prompt multiplies across every call, a new model changes output verbosity, a retrieval tweak doubles context size. Without release gates, these regressions compound unnoticed.
The release system we help teams build treats cost as a first-class eval metric alongside quality: golden-set runs before rollout report both answer quality and tokens per action, and a change that degrades either blocks by default. Teams with this gate in place stop discovering cost regressions from the invoice.
Audit Focus
What we inspect before prescribing a platform change.
The first pass is designed to identify the smallest useful intervention: routing, caching, prompt control, serving tuning, or a deeper break-even audit.
Model, prompt, and retrieval release workflow
Eval gates before production rollout
Observability for latency, cost, retries, and quality
AI infra CI/CD and deployment automation
Cost and capacity monitoring for APIs and GPUs
MLOps operating map — see the full map
Production AI needs release controls and cost telemetry, not only model code.
| Layer | Common failure | Audit question |
|---|---|---|
| Release | Prompt/model changes ship manually | What blocks a bad rollout? |
| Evaluation | Tests do not match real workflows | Which cases define quality? |
| Observability | Only provider errors are monitored | Can you see cost per workflow? |
| Infrastructure | GPU/API capacity is overprovisioned | What is current utilization? |
| Governance | No owner for model behavior | Who approves risk changes? |
Worked Example
Invoice view versus telemetry view
The same monthly spend, seen two ways — the gap between them is what MLOps instrumentation exposes.
| Question | Invoice view | Telemetry view |
|---|---|---|
| What did AI cost this month? | One aggregate number | Cost per feature, team, and completed action |
| Why did spend jump 18%? | Unknown — investigate manually | Prompt change in release 41 raised tokens per call |
| Are GPUs earning their cost? | Invisible | Utilization per node; idle capacity flagged |
| Which retries are waste? | Invisible | Retry rate by cause: timeout, eval fail, tool error |
| Is the new model cheaper? | Wait for next invoice | A/B token and quality metrics before rollout |
The audit reviews your current observability against this telemetry baseline and sequences the gaps by exposed savings.
How It Works
How the audit works
Step 1
Share spend and workload shape
Submit monthly spend range, provider mix, token volume, and what your AI operations actually do: chat, RAG, agents, extraction, or batch jobs. Takes minutes, no production access needed.
Step 2
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.
Step 3
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.
Step 4
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.
Start with spend, provider, and workload shape.
The audit form routes teams below $20K/month toward self-serve estimators and routes qualified spend into follow-up.
$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 auditFAQ
Common questions
What is MLOps consulting?
MLOps consulting helps teams design the systems that deploy, monitor, evaluate, and operate ML and LLM workloads in production.
Does MLOps apply to LLM and RAG systems?
Yes. LLM, RAG, and agent systems need eval gates, prompt and model release controls, retrieval monitoring, cost telemetry, and incident response.
Can MLOps reduce AI infrastructure cost?
MLOps can reduce cost by exposing utilization, retries, routing mistakes, prompt growth, and deployment patterns that waste GPU or API spend.
What does MLOps consulting cost?
The entry point is the free audit: share your release, evaluation, and observability setup alongside spend shape, and you get a written read on the operational gaps. Paid engagements are scoped from those findings — typically starting with the telemetry and eval-gate work that pays for itself in exposed waste.
How do I track LLM costs per feature?
Instrument at the workflow layer, not the invoice: tag every model call with feature and team metadata, log prompt and output tokens plus retries and tool calls, and aggregate to cost per completed action. Provider dashboards cannot do this for you — the attribution has to live in your application telemetry.
What metrics should an LLM platform monitor?
Beyond uptime: cost per workflow, token volume by feature, retry and fallback rates, cache hit rates, GPU or PTU utilization, p95 latency per route, and eval scores on golden sets. Those seven surface nearly every cost regression and quality incident we see in audits.