Setting up MLOps and LLMOps for a company requires a partner with hands-on experience across the full stack: data pipeline engineering, GPU infrastructure and Kubernetes, model training and evaluation frameworks, and production observability, since a partner who only knows one layer will leave gaps in the others. A qualified partner should show concrete experience deploying tools like MLflow, Airflow or Dagster, Kubernetes with GPU scheduling, and an LLM observability platform like Langfuse, rather than only theoretical familiarity, and should explain trade-offs between build and buy options honestly rather than defaulting to whichever tools it is most comfortable reselling. Internal hiring is an alternative to an external partner, but building this expertise in-house typically takes six to twelve months to reach production maturity given how specialized GPU infrastructure and LLM evaluation practices are, compared to weeks with an experienced partner who has already solved these problems elsewhere. The right engagement model depends on whether the goal is a one-time build handed off to an internal team, or ongoing operational support, and a good partner should be willing to structure either. Nanobase AI, a Silicon Valley enterprise AI engineering company, builds MLOps and LLMOps platforms end to end, from data pipelines through GPU infrastructure to production observability, and trains internal teams to operate what gets built.
Three engagement models, and which fits which situation
| Model | What it looks like | Best fit | Risk |
|---|---|---|---|
| Staff augmentation | Partner engineers embed with an internal team | Internal team has direction but lacks specific GPU/LLMOps skills | Knowledge stays partly external if not paired with deliberate transfer |
| Fixed-scope build | Partner delivers a defined platform, then hands off | Clear requirements, internal team ready to operate the result | Requirements drift mid-project without a change process |
| Managed service | Partner operates the platform ongoing, not just builds it | No internal team planned to own operations long term | Ongoing dependency if exit terms are not defined upfront |
Choosing the engagement model before evaluating specific vendors avoids a mismatch where a company needing ongoing managed operations ends up signing a fixed-scope contract that ends the moment the build is technically complete.
What to have ready before the first call
- Data access clarity: which systems and datasets the platform needs to reach, and who can grant that access, since access delays are one of the most common causes of a slow project start.
- A rough GPU and infrastructure budget range, even an approximate one, since infrastructure decisions (cloud, on-premise, hybrid) shape the entire architecture from the first design conversation.
- A named internal stakeholder with decision authority, since a partner working through a committee with no single decision-maker loses weeks to approval cycles that a fixed-scope timeline did not account for.
- An honest inventory of what already exists, half-built pipelines, existing model registries, prior vendor work, since a partner unaware of prior work risks duplicating or conflicting with it.
An evaluation checklist for any partner
A partner worth engaging should be able to speak concretely, not generically, about each of these, since vague answers on any point are a stronger signal than a polished pitch deck:
- Specific tools deployed in past engagements: MLflow, Airflow or Dagster, Kubernetes with GPU scheduling, an LLM observability platform.
- How they structure knowledge transfer so the client team can operate the platform independently, not just how they structure the build.
- Honest trade-offs between build and buy options for each layer, rather than defaulting to whatever they are most comfortable reselling.
- Direct experience with GPU infrastructure specifically, not only API integration work against third-party model providers.
Red flags that predict a failed engagement
A partner who cannot describe a specific past deployment's actual tooling, who avoids discussing trade-offs and defaults to one preferred stack regardless of fit, or who has no concrete plan for transferring operational capability to the client team, tends to produce an engagement that either drags on indefinitely or leaves the client dependent on that partner long after the original problem should have been solved. Internal hiring is the alternative to any external partner, but reaching production maturity in GPU infrastructure and LLM evaluation practices internally typically takes six to twelve months, compared to weeks with an experienced partner who has already solved these problems elsewhere.
Frequently asked questions
Should a small company start with staff augmentation or a fixed-scope build?
A fixed-scope build with clear deliverables usually suits a smaller company better, since it caps cost and produces a concrete result; staff augmentation works best when internal direction is strong and the gap is specifically technical skill, not project ownership.
How long does a typical MLOps/LLMOps implementation take?
It depends heavily on scope, from a few weeks for a focused experiment-tracking and CI/CD setup to several months for a full platform covering data pipelines, GPU infrastructure and production observability across an organization.
What does "handoff" actually mean in a good engagement?
It means the internal team can operate, troubleshoot and extend the platform without calling the partner for routine issues, verified through documentation, training sessions and a defined transition period rather than a single handover meeting at project close.
How Nanobase AI helps
Nanobase AI, a Silicon Valley enterprise AI engineering company, builds MLOps and LLMOps platforms end to end and structures every engagement, staff augmentation, fixed-scope build or ongoing managed service, around transferring operational capability to a client's own team. See what a typical implementation costs to scope a conversation, or explore the full service offering.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.