Valfort Tech
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LLMOps consulting for reliable, affordable LLM features
LLMOps consulting is the work of making large language model features safe to change and affordable to run in production. Valfort Tech's senior engineers add evals that gate every deploy, tracing and cost dashboards, guardrails and PII redaction, and efficient inference, all as code in your repositories. Inference costs typically fall 40–70%.
When teams call us about LLMOps
A prompt change quietly breaks other answers.
- A prompt change fixes one answer and quietly breaks others.
- Your LLM bill grows faster than usage and nobody can say which feature drives it.
- When a user reports a bad answer, you cannot trace the prompt, context and model that produced it.
- Security or a customer has asked how you keep personal data out of prompts and logs.
- You want to move part of your traffic to a self-hosted model but have no safe way to compare it.
What we deliver
Everything ships as code in your repositories, with runbooks and a recorded handover.
Evals that gate deploys
An eval suite built from your real use cases runs in CI on every prompt, model or retrieval change. A change that lowers quality does not ship.
Tracing
Every request traced in Langfuse with OpenTelemetry, from user input through retrieval and tool calls to the final answer.
Cost dashboards
Token and GPU spend broken down by feature, customer and model, with alerts when a change moves the numbers.
Guardrails and PII redaction
Input and output checks, plus PII redaction before data reaches a model provider or your logs.
Inference cost reduction
Caching, model routing, prompt trimming and, where it pays off, self-hosted models on vLLM and KServe, tested against your evals first.
Runbooks and handover
Written runbooks for model swaps, incidents and cost reviews, plus a recorded handover so your team runs it without us.
Typical results
Caching, routing and right-sized models typically cut inference spend by 40–70% without lowering eval scores.
With evals gating every deploy, quality drops are typically caught in CI instead of by users.
Teams can typically trace a reported bad answer to its prompt, context and model in minutes rather than days.
Typical ranges, not promises. We measure your own baseline in the first week.
How a LLMOps engagement runs
The first two weeks run on read-only access. Every change after that goes through your pull requests, with one named engineer and a written update every Friday.
- Week 1
- Read-only reviewWe review your prompts, retrieval, model providers, logs and spend on read-only access, and find where quality and cost leak.
- Week 2
- Roadmap and first fixYou get a scored report and a ranked 90-day roadmap, and we ship one fix to production through your pull requests.
- Weeks 3–5
- Evals and tracingWe build the eval suite from your real cases, wire it into CI, and add tracing and cost dashboards.
- Weeks 6–8
- Guardrails, costs, handoverWe add guardrails and PII redaction, ship the cost changes that pass your evals, then hand over runbooks and a recording.
Tools we work in
We work in the tools you already run, and only add new ones when they earn their place.
- Langfuse
- OpenTelemetry
- vLLM
- KServe
- Kubernetes
- Terraform
- AWS
- GCP
- Argo CD
- Ray
LLMOps questions
What is the difference between MLOps and LLMOps?
LLMOps is MLOps adapted to large language models, where you often change prompts and providers rather than retrain models. The hard problems shift to evaluating free-text answers, tracing multi-step requests, controlling token and GPU costs, and keeping personal data out of prompts. Valfort Tech handles both, and many teams need a mix.
How much does LLMOps consulting cost?
Engagements start with a 10-day readiness sprint at a fixed $3,500. You get a scored report, a ranked 90-day roadmap and one fix shipped to production. If we do not find at least three times the fee in savings or avoided risk, the sprint is free. Follow-on work is scoped after the sprint, and the sprint fee is credited to it.
How long does it take to add evals and tracing to an LLM app?
A first eval suite and tracing are typically running within 3 to 5 weeks after the readiness sprint. Guardrails, PII redaction and cost changes usually follow over the next few weeks. The first two weeks run on read-only access, and every change after that goes through your own pull requests.
How can we reduce LLM inference costs?
Most teams cut LLM inference costs by caching repeated requests, routing simple tasks to smaller models, trimming prompts and context, and self-hosting models where volume justifies it. Typical savings are 40–70%. The key is to test every change against an eval suite first, so you save money without lowering answer quality.
Do you work with hosted model APIs and self-hosted models?
Yes. We work with hosted model APIs and with self-hosted open models served on vLLM or KServe, often both in the same system. Tracing, evals and cost dashboards are set up to compare providers on your own use cases, so model choices are based on your data rather than public benchmarks.
Not sure where to start?
Book a 30-minute call. We'll tell you whether the readiness sprint, a single service or nothing at all is the right next step.