LLM Development Services: What's Actually Involved

A large language model behind an API key is not a product. LLM development services means building the system around the model: retrieval that feeds it the right context, an evaluation suite that scores its output before a customer sees it, guardrails for what the eval misses, and a deploy pipeline. A Builder Pod doing that work starts at **$5,000 a month**.

**Key numbers**

- Builder Pod: **$5,000/month**, one build track, pod lead plus a two-engineer bench, month-to-month with 30 days' notice ([pricing](/pricing))
- Hiring one senior AI/ML engineer independently: 3-6 months to close, upward of $250,000/year once fully loaded
- A mid-level in-house AI engineer runs roughly $120k-$160k/year base, before benefits and recruiting
- Two HIPAA-aligned production platforms shipped to date, both with signed BAAs and no client data used for training unless requested
- Production code, LLM-generated or not, ships through the same gate: PR review by a named engineer, typed contracts, tests in CI

## What "LLM development services" actually means

If a vendor's pitch is "we wire up your app to GPT-4o," that is API integration, not LLM development. It might be exactly what you need for a weekend prototype. It is not what you need for a system that has to be right the first time, because a raw call to a foundation model has no memory of your data, no way to check its own answer, and no gate that stops a bad output from reaching a patient, a claim adjuster, or a voter file.

Real LLM development services build the scaffolding around the model:

- **Prompt and context design.** How the system decides what goes into the context window: which documents, which conversation history, which tool outputs, in what order and format.
- **Retrieval (RAG).** A pipeline that turns your documents, records, or database into something the model can search and cite, instead of hallucinating from memory.
- **Evaluation.** A repeatable way to score output quality before code ships, not just after a customer complains.
- **Guardrails.** Checks that catch the failure modes an eval alone will not: PII leakage, off-topic responses, jailbreak attempts, cost blowouts from runaway loops.
- **A deploy pipeline.** The same discipline as any production software: pull requests, code review, tests in CI, reviewed migrations for schema changes.

Skip any one of these and you have a demo, not a system. A demo works in the sales call and breaks in week three of production, usually on the exact edge case a compliance officer asks about first.

## The four components a real LLM build actually ships

Ask any vendor claiming LLM development experience to name these four things specifically. If they cannot, they have built a chatbot wrapper, not a production system.

### 1. A RAG ingestion pipeline

Retrieval-augmented generation only works if the retrieval half is engineered, not improvised. A real ingestion pipeline chunks source documents with a strategy that matches the content (clinical notes chunk differently than contract clauses), generates embeddings, indexes them in a vector store, and re-indexes on a schedule or on write. It also handles the boring parts that determine whether retrieval actually works in production: deduplication, versioning when a source document changes, and access control so a query never returns a chunk the requesting user should not see.

```python
def ingest_document(doc: SourceDocument, tenant_id: str) -> IngestionResult:
chunks = chunk_by_structure(doc, max_tokens=512, overlap=64)
embeddings = embed_batch([c.text for c in chunks], model=EMBEDDING_MODEL)
records = [
VectorRecord(
id=f"{doc.id}:{i}",
tenant_id=tenant_id,
vector=emb,
metadata={
"source": doc.source_uri,
"acl": doc.acl_tags,
"version": doc.version,
},
)
for i, emb in enumerate(embeddings)
]
vector_store.upsert(records)
audit_log.write(
event="document_ingested",
doc_id=doc.id,
tenant_id=tenant_id,
chunk_count=len(chunks),
)
return IngestionResult(document_id=doc.id, chunks_indexed=len(chunks))
```

The tenant scoping and audit write in that snippet are not decoration. In regulated builds, a RAG pipeline without row-level access control on retrieval is a compliance incident waiting on a query.

### 2. An eval suite that scores output before it ships

An eval suite is a set of tests for model behavior, run against a fixed collection of representative inputs, scored against a rubric, and gated in CI the same way a unit test suite is gated. It answers the question "did this prompt change make the system better or worse" with a number, not a vibe from someone reading five transcripts.

A working eval suite usually has three layers: a golden set of inputs with known-good outputs or acceptable ranges, an automated scorer (a smaller model grading against a rubric, or exact-match checks for structured output), and a threshold that blocks a deploy if the score regresses. This is the piece most "LLM development companies" skip, because it is unglamorous and it is the part that actually prevents the embarrassing failure in front of a customer.

### 3. An agent loop, where the model calls tools instead of just answering

An agent build gives the model a set of tools (a database query, a calendar API, a document lookup, another model) and lets it decide, per request, whether to answer directly or call a tool and reason over the result. This is a different engineering problem than a single-turn chatbot: it needs a loop with a maximum step count, a way to log every tool call for audit, and a fallback for when the model calls a tool badly or gets stuck. Our [guide to AI agent development services](/blog/ai-agent-development-services) covers the loop architecture, tool-calling patterns, and failure handling in more depth.

### 4. A deploy gate that treats model-generated code like any other code

The model itself is one artifact. The application code around it, including code the model helped write, is another, and it goes through the same gate every other change goes through.

## Does LLM-generated code get a pass on review?

No. This is worth stating directly because it is the question every technical buyer asks and every vendor answers vaguely. AI-assisted code goes through the same gate as any other code: a pull request in the client's own repository, reviewed by the named engineer who owns it, typed contracts, tests in CI, and schema changes shipped as reviewed migrations, the same standard described on our [security page](/security). A model that suggested the diff does not get to skip the reviewer who is accountable for it in production.

This matters more in LLM builds than in ordinary software, not less, because the failure modes are quieter. A hallucinated function signature fails a type check immediately. A subtly wrong retrieval filter, or a prompt template that silently drops a system instruction under certain inputs, can pass every existing test and still produce a wrong answer for a category of user the eval set did not cover. The review discipline is the backstop for exactly that gap.

## What an engagement actually runs through


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Every box in that diagram is a deliverable, not a phase name on a slide. The [how it works](/how-it-works) page walks through the surrounding process: a single working session to scope the build, a free clickable prototype built for approval before anything is billed, a pod that starts within five business days, and weekly shipped work from week one or two onward, all inside daily standups run in your existing channels.

## What it actually costs

The honest comparison is not "our price versus a competitor's price." It is subscription capacity versus the two other ways to get this work done: hiring, or a freelancer.

| Option | Cost | What you get | Time to first output |
|---|---|---|---|
| Builder Pod | **$5,000/month** | 1 build track, pod lead + 2-engineer bench, weekly ship | Working within 5 business days, first ship week 1-2 |
| Growth Pod | **$10,000/month** | 2 build tracks, pod lead + 3-engineer bench, architecture planning | Same onboarding, more parallel work |
| Senior AI/ML hire | $250,000+/year fully loaded | One engineer, full-time, on your payroll | 3-6 months to close a hire |
| Mid-level AI hire | $120k-$160k/year base (estimate, before benefits and recruiting) | One engineer, still needs a lead to direct the work | 3-6 months, the same general hiring-cycle range as most full-time searches |
| Independent freelancer | Varies widely by platform, specialty, and region; no standard published rate | One person, no bench, no built-in review partner | Days to weeks to start, but no coverage if they become unavailable |

A [Builder Pod runs roughly $60,000 a year](/pods), against a single mid-level in-house hire at $120k-$160k base before benefits, payroll tax, and recruiting cost are added. The pod is not one person; it is a lead plus a two-engineer bench, which means the work does not stop when one person is out sick or leaves. For a fuller breakdown of the arithmetic, including what changes at Growth Pod and Enterprise scale, see our [hire-vs-pod cost comparison](/blog/ai-engineer-cost-2026-hire-vs-pod).

Freelance marketplaces solve a different problem: a short, well-defined task with a person who disappears when the contract ends. A pod solves the LLM development problem specifically because RAG pipelines, eval suites, and agent loops are not one-off tasks. They need maintenance as the underlying model versions change, as your data grows, and as the eval set needs new cases added every time a customer finds a gap.

## Hiring an AI engineer vs. renting a pod

"Hire generative AI engineers" is usually two different searches wearing one query. One reader wants a full-time employee. The other wants the work done and does not actually care whether the person doing it is an employee.

If you want the employee: budget $250,000 a year or more fully loaded for someone senior enough to build a production RAG pipeline and eval suite unsupervised, and budget 3-6 months to find, interview, and close them. That is not a knock on recruiting, it is the current market for a scarce skill set.

If you want the work: a pod gives you the same skill set, already assembled, already working together, at $5,000 a month for a Builder Pod or $10,000 a month for a Growth Pod, with no recruiting cycle and no severance risk if the fit turns out wrong. You lose the thing a full-time hire gives you that a pod cannot: a person embedded in your company culture long-term, building institutional knowledge that outlasts any single project. If that is what you actually need, hiring is the right call and no pod replaces it. For the mechanics of that comparison in more detail, see our [practical guide to custom AI development](/blog/custom-ai-development) and the [hire vs. pod cost breakdown](/blog/ai-engineer-cost-2026-hire-vs-pod).

## Data handling: who sees your data and who owns the output

Three questions come up in every regulated-industry sales conversation, and the answers should be short and unambiguous.

**Does the model train on our data?** No, not unless you explicitly ask for it. Default behavior is no training on client data. If a client wants a fine-tuned model trained on their own corpus, that is a specific, requested build, and the resulting model and its weights stay the client's, not licensed back to us.

**Who owns the code, the pipeline, and the fine-tuned artifacts?** The client does, from day one, with no license-back. Everything ships into your own repository and your own cloud account or VPC, so if the engagement ends, the system keeps running. Nothing in it calls a service that only we operate.

**What about HIPAA?** There is no such thing as HIPAA certification, so any vendor claiming it is either confused or overselling. The honest posture is a signed Business Associate Agreement on request and HIPAA-aligned controls built into the architecture: access logging, encryption, row-level tenant isolation, retention policies. We have shipped two HIPAA-aligned production platforms on that basis, a compounding-pharmacy routing system and a Medicare/Medicaid medical-billing audit platform, both with the compliance posture proven in code review and tests, not asserted in a sales deck. Full detail is on the [security page](/security), including how a SOC 2 Type II report is made available under NDA.

## When a pod fits, and when it does not

A subscription pod fits when the problem is well-scoped enough to hand to a team that starts in days, and open-ended enough that hourly billing or a fixed-scope contract would be the wrong shape for it. Concretely:

- You need a RAG system built over an existing document or record set, with an eval suite and a deploy pipeline, not just a prompt tuned in a playground.
- You have a compliance requirement (HIPAA-aligned controls, a signed BAA, an audit trail) and cannot afford a vendor who treats it as an afterthought.
- You want to see working software before committing budget, which is exactly what the free clickable prototype step is for.
- Your team needs the bench, not just one person: coverage when someone is out, code review by a second engineer, and continuity if the project runs longer than expected.

A pod does not fit when you need one specific person embedded in your team long-term, building institutional memory that outlives any single project; that is a hiring decision, not a staffing decision. It also does not fit a task small enough to hand a freelancer for a week, or research work with no defined output (pure model evaluation with no shipping deadline is closer to a research contract than an engineering build).

## A checklist before you sign

1. Ask the vendor to name their eval suite specifically: what golden set, what scorer, what regression threshold blocks a deploy.
2. Ask who reviews AI-assisted code, by name, and confirm it goes through the same PR process as everything else.
3. Confirm the deploy target: your repository and your cloud account, not a shared environment the vendor controls.
4. Get the training-on-data policy in writing: no training by default, fine-tuning only on request, and who owns the resulting weights.
5. If you are regulated, ask for a signed BAA and ask exactly what "HIPAA-aligned" means in their architecture, not just the phrase itself.
6. Compare the monthly number against the fully loaded cost of a hire for the same skill set, not just against another vendor's quote.

## The short version

LLM development services mean building the system around the model, not just calling it: a RAG ingestion pipeline over your own data, an eval suite that scores output before it ships, an agent loop when the model needs to act rather than just answer, and a deploy pipeline where every piece of code, AI-assisted or not, goes through PR review by a named engineer. A Builder Pod starts at $5,000 a month for one build track and a two-engineer bench, against $250,000 a year and a 3-6 month search for a single senior hire. No training on your data unless you ask for it, everything ships into your own repository and cloud account, and if you are regulated, a signed BAA and HIPAA-aligned controls are the honest baseline, not a certification nobody can actually hold.