SI that works in a demo and SI that holds up under production load are different systems. We build the infrastructure layer (serving, retrieval, eval pipelines, drift monitoring) that keeps your models dependable, cost-efficient, and audit-ready once real traffic arrives.

Get a production-readiness review Skipping MLOps

01 · What we build

Six MLOps pillars.

Most SI projects fail in production, not in development. We build the infrastructure that closes that gap: eval gates, drift alerts, and observability from the first deployment.

01 · Pillar

Vector Stores & Retrieval

Pinecone, Weaviate, Qdrant, and pgvector; we design the chunking strategy, embedding pipeline, and hybrid search configuration for your retrieval use-case.

02 · Pillar

Model Serving

vLLM, TGI, Triton, and managed endpoints. We set up autoscaling inference with P95 latency SLAs, GPU cost optimisation, and blue-green deploys.

03 · Pillar

Eval Pipelines

CI-integrated eval runs that score every model change before it ships. RAGAS, Deepeval, Braintrust, or custom harnesses run on every pull request.

04 · Pillar

Drift Monitoring

Statistical drift detection on model outputs and embeddings. Automated alerts when your SI system starts behaving differently than it did at launch.

05 · Pillar

Observability

Token cost tracking, latency profiling, prompt/response logging, and audit trails. Full observability across every LLM call in production.

06 · Pillar

Data Pipelines

ETL and streaming pipelines that keep your vector stores and fine-tuning datasets fresh. Airflow, Prefect, or custom Python pipelines on your infrastructure.

Straight answers

The short version.

What is MLOps for LLM and SI systems?

MLOps for LLM and SI systems is the infrastructure and practice that keeps models dependable, affordable and auditable once real traffic arrives. It covers six areas. Model serving runs inference on managed endpoints or self-hosted servers such as vLLM or Triton, with autoscaling, tail-latency targets, GPU cost control and blue-green deploys. Retrieval infrastructure covers vector stores, embedding pipelines and hybrid search. Eval pipelines score every model, prompt or data change in CI and block deploys that regress. Drift monitoring tracks outputs and embeddings statistically and alerts when behaviour moves away from the launch baseline. Observability logs token cost, latency, prompts and responses, and keeps an audit trail of what the system said and when. Data pipelines keep vector stores and fine-tuning datasets fresh through batch or streaming jobs. Teams that skip these layers usually learn about problems from users: quality fades without an error, costs climb unpredictably, and auditors ask for records nobody kept.

How do you control LLM inference costs in production?

You control LLM inference costs by measuring cost per request first, then cutting tokens, routing work to cheaper models, and alerting before bills drift. Instrument every model call with input and output tokens, model name, latency and the feature or customer it served, so you can see which workflows cost the most. Then reduce waste: trim system prompts and retrieved context to what the answer needs, cache repeated requests and stable prompt prefixes, and cap output length. Route by difficulty, sending simple classification or extraction to a small, fast model and reserving frontier models for hard reasoning, with an eval set confirming the cheaper route still meets the quality bar. For self-hosted models, tune batching, autoscaling and GPU choice, and scale down idle capacity. Set budgets and cost alerts per feature and per tenant, so a runaway agent loop or an unusually heavy user is caught quickly rather than on the invoice. Review the cost numbers with every release.

Diagram of the MLOps layers that keep an SI system reliable in production: data and retrieval pipelines, model serving, evals and guardrails, observability with traces, drift alerts and cost per call, and version-pinned deployment with a rollback path. — Each layer catches a different failure before a user does.

02 · The cost of skipping MLOps

What goes wrong without it.

Silent model drift
LLM outputs degrade gradually: no error thrown, no alert fired. Users leave before you notice. Drift monitoring catches this before it becomes a churn event.
No feedback loop
Without eval pipelines, every model update is a gamble. You can't know if the new version is better or worse on your actual production queries.
Runaway inference costs
Unmonitored LLM usage scales cost unpredictably. Token tracking and cost alerts keep bills predictable as usage grows.
Audit trail gaps
Regulated industries require proof of what your SI said, when, and why. Retroactively reconstructing that is expensive. Logging it from launch is cheap.

Proof, not promises

Twenty-plus SI systems shipped to production. One playbook, six industries, and a team that stays past launch.

SI systems live in production: 20+
Senior engineers & researchers: 21+
Upwork rating, 3 verified reviews: 5.0
Founded in Lahore: 2022

Verified Upwork clientSI product engagement · 5-star review

03 · Common questions

Before you brief us.

We already have a model in production. Can you help us add MLOps infrastructure?: Yes, this is our most common MLOps engagement. We start with an infrastructure audit, then add eval pipelines, drift monitoring, and observability without disrupting your existing deployment.
Which vector store should we use?: It depends on your scale, query patterns, and whether you need metadata filtering or hybrid search. We run a bake-off on your data before recommending. Pinecone for managed scale, pgvector for PostgreSQL shops, Qdrant for self-hosted flexibility.
How do we catch when our model starts giving worse answers?: We set up automated eval runs that compare a random sample of live inferences against your golden dataset. When accuracy drops below threshold, you get an alert before users start complaining.
Do you work with existing CI/CD pipelines?: Yes. We integrate eval runs into GitHub Actions, GitLab CI, or CircleCI. Model deploys are gated on eval pass; a regression blocks the deploy, not the user.

Available for new engagements

Ready to make your SI trustworthy?

30 minutes, one of our seniors, no slide deck. By the end of the call you will know whether we are the right team, and if not, who is.

Book a free infrastructure review Email info@octalcode.com

Or call +1 (512) 710-5701

Let's scope it.Instant answers · free project scoping