Custom LLM Development Services
There are four ways to put a large language model to work, and they differ by a factor of a hundred in cost and months in time. You can prompt a commercial API. You can ground the model in your own content. You can fine-tune it on your material. You can train one from scratch. Picking the wrong one is the most expensive mistake in this category, and it is usually made before anyone writes code.
Cabot provides LLM development services that begin with that choice and prove it before the budget follows. In most cases grounding beats training on both accuracy and cost, and we will tell you when that is true for you even though the smaller engagement is the one we are recommending.Then we build it: the retrieval, the evaluation, the guardrails, and the cost controls that keep it viable in production.
Grounding · Fine-tuning · Integration · Evaluation · Cost control | Model-neutral and security led
No obligation. Your details stay private.
150x
Spread in inference cost between small and top-tier models, roughly $0.10 to $15 for the same unit of work, which is why model choice drives the running bill
6 to 24 months
Time to build an enterprise-class model in house, at roughly $1,000 to $50,000+ per month in cloud infrastructure alone
700+ projects
Delivered for 140+ clients since 2010, across the systems these models have to integrate with
What is LLM development, and which version of it do you actually need?
LLM development is the work of turning a large language model into a system that does a specific job for your business, by choosing how the model gets your knowledge and then engineering everything around it. The confusion in this market comes from four very different activities sharing one name. Prompting a commercial API is configuration. Grounding a model in your documents is retrieval engineering. Fine-tuning teaches a model your format and tone on your examples. Training from scratch builds a model from raw data and is out of reach for all but a handful of organizations. They differ enormously in cost, time, and risk, and the honest answer for most businesses is a combination of the first two. This page is about making that call well, then building it properly, which sits within our wider AI engineering services.
Why these projects overrun, and what it costs when they do
The wrong path was chosen first. Teams reach for fine-tuning because it sounds serious, then spend months solving a problem retrieval would have handled in weeks.
The model was never grounded. Without access to your documents it answers from general knowledge, which is where confident and wrong comes from.
Nobody measured quality. With no evaluation set there is no pass mark, so the release decision comes down to whoever is most confident in the room.
Cost per query went unwatched. Token spend behaves nothing like a server bill, and a design that is fine in testing becomes indefensible at real volume.
The build assumed one provider. Prompts and behavior tuned to a single vendor's quirks turn a routine model swap into a re-engineering project.
Sensitive material went to a public endpoint. It works until legal, security, or a regulator asks where the data went, and then the project stops.
What we build with large language models
Path selection and feasibility
We test your use case against the four approaches and recommend one, with the cost and timeline of each spelled out. If an off-the-shelf product already does the job, we say so.
LLM application development
We build the product around the model: the interface, the orchestration and prompting, the fallbacks, and the integration with the systems your teams already use.
Retrieval and knowledge grounding
We connect the model to your documents and databases so answers come from your material with sources attached. This is where we start for most engagements. See our RAG implementation work.
Fine-tuning and domain adaptation
Where a model has to learn your format, tone, or a specialist vocabulary that prompting cannot reach, we build the training set and tune it, and we measure whether it beat the cheaper option.
Integration and deployment
We put the model where the work happens, inside your applications and workflows, and deploy it in your own cloud environment where your data requires that.
Evaluation, guardrails, and cost control
We build the test suite that proves quality, the controls that contain behavior, and the routing and caching that keep cost per query defensible. See our LLMOps practice.
What do LLM development services cost, and what do they cost to run?
There are two numbers here and most quotes only cover the first. Build cost tracks with the path you choose and the data engineering it needs. Running cost is cost per query multiplied by real volume, and it is the one that surprises people. We scope both up front, so you are never signing a blank check. Get a quick figure in minutes, then talk to an engineer about your specifics.
How we decide what to build before we build it
Start from the job, not the technique
We write down what a correct answer looks like for your use case before discussing architecture. Most path decisions become obvious once that is on paper, and some use cases turn out not to need a model at all.
Try the cheap path first
We test grounding and prompting against your real content early. If that clears your quality bar, the project is smaller and faster, and we will tell you so rather than scope the larger build.
Tune only where it earns its cost
Fine-tuning is right when a model must learn a format, a tone, or a specialist vocabulary that context cannot supply. We measure it against the grounded version, so the decision rests on numbers.
Design for the running bill
We profile cost per query during the build, then use model routing, caching, and right-sizing to hold it down. A design that only works at test volume is not finished.
The stack behind the systems we ship
The stack behind the systems we ship
Models and orchestration
Retrieval and tuning
Deployment and operations
What happens at each stage
How we choose and govern the tooling
Which path is right for your use case?
Built for the way your industry is judged
Healthcare
We know a clinical answer carries consequences a marketing draft does not, so we ground output in approved sources, keep protected health information inside your environment, and put a clinician in the loop where it belongs.
Financial services
We understand an auditor will ask why the system said what it said, so we keep retrieval traceable and every interaction logged and reviewable.
SaaS and ISVs
We know an AI feature is judged against the best your users have seen elsewhere, and that its margin depends on cost per query, so we engineer for both from the first release.
Legal and professional services
We know a fabricated citation is a professional liability, so we ground answers in your document set and surface the source alongside every claim.
Enterprise operations
We understand the knowledge you need sits in a decade of documents across systems that do not speak to each other, and that connecting them is most of the work.
Manufacturing and field services
We know your manuals and service histories are the asset, and that a technician needs a precise answer with the page it came from, not a paragraph of plausible text.
Industries we already understand
Healthcare
Ecommerce
Fintech
Travel and Tourism
Security
Automobile
Stocks and Insurance
Restaurant
Built to pass review from users, auditors, and security teams
A capable system is worth nothing if it cannot pass review. We build current security controls into the work rather than bolting them on afterward, so what we deliver can face users, auditors, and security teams without another round of rework.
Which standards apply depends on the market you operate in. The security practices below apply to every engagement. The regulatory items apply where your system handles the data they govern, and we scope that with you during assessment.
Governing what these systems may see and do, and proving it later, is its own discipline. See our data governance for AI practice. For systems that handle health data, we build to HIPAA standards.
How we get you from a use case to a system in production
Our LLM development services follow a structured path from candidate use case to a running system, with a decision point at every step and something you can judge at the end of each.
1. Frame the use case
We agree what job the model does, who it serves, and what a good answer looks like, so you decide what goes forward on evidence rather than enthusiasm.
2. Assess the path
We test the cheaper approaches against your real content and recommend one of the four paths, with the cost and timeline of each on the table.
3. Prove it on your material
We build a working version on your own content and test it against real questions, so you decide whether to fund the full build on results, not a slide.
4. Set the quality and cost bar
Together we define the evaluation set, the pass mark, and the cost per query the system has to hold, and you approve both numbers.
5. Build, harden, and integrate
We build it into your workflow, add guardrails and access control, and review with you in short cycles rather than one long silence.
6. Release and operate
We roll it out to a controlled group first, watch quality and spend, then widen it and keep improving on what real usage reveals.
The team that owns whether this works
These projects go wrong when the people who understand models and the people who understand production are different teams with nobody accountable for the join. On our engagements that line does not exist. An AI engineer owns the system design, the retrieval strategy, and the prompting and orchestration. A data engineer owns the pipelines that keep your content fresh and correctly permissioned. A QA lead owns the evaluation set and the pass mark, and has the authority to hold a release. A solutions architect owns how the system meets your existing software and your security model.
Our depth shows in four specific places. We are strong at retrieval over messy enterprise content that was never written to be machine-read. We are strong at judging honestly when tuning is worth its cost and when it is not. We are strong at integrating these systems into software people already use, rather than beside it. And we are strong at holding cost per query down as volume grows. We do not claim to be equally deep in everything, and we will tell you where a research group fits better than an engineering one.
We work as an extension of your team, not a black box down the hall. You see the evaluation results, the decision points, and the cost per query, and you own the code and the IP from the first commit. When you need more hands as the work scales, you can add forward-deployed engineers to the same team rather than starting over with a new one.
Why enterprise leaders choose Cabot for custom LLM development
We recommend the smaller build when it is right
If grounding clears your quality bar, we say so, even though fine-tuning would bill more. Advice you cannot trust is worth nothing on a decision this expensive.
Model-neutral by design
We match the model to the job and keep that layer swappable, so a better or cheaper option next quarter is a change you can make rather than a rebuild you have to justify.
Cost per query is a design constraint
We profile spend during the build and use routing, caching, and right-sizing to hold it down, so the economics still work at real volume.
Security and compliance built in
Access control, audit logging, and prompt-injection defense are part of the build, and regulated data stays in your environment. Where a standard applies, such as HIPAA in healthcare or GDPR for EU users, we build to it.
Quality is a number, not an opinion
Every engagement has an evaluation set and a pass mark agreed with you, so the release decision rests on measurement rather than on whoever is most confident.
A full engineering practice behind it
Retrieval, agents, and operations each have a deeper practice within our AI engineering services, so the work does not stop at the edge of one specialty.
Where to go next, depending on what you need
You are earlier than the model decision
If you are still shaping what to build with AI at all, start with our generative AI development services.
You know it has to cite your documents
If grounding and traceable sources are the whole point, go straight to RAG implementation.
You need it to act, not just answer
If the system must complete tasks across your tools, look at AI agent development.
Our Clients
LLM development is the work of turning a large language model into a system that does a specific job for your business. It covers choosing how the model gets your knowledge, whether through prompting, retrieval, or tuning, then building the application, evaluation, guardrails, and operations around it. The model is a component you can swap; the engineering around it is what makes the result dependable.
There are two numbers. Build cost tracks with the path you choose, the data engineering it needs, and the reliability bar it has to clear. Running cost is cost per query multiplied by your real volume, and it varies enormously with model choice, roughly $0.10 to $15 for the same unit of work between small and top-tier models. We scope both up front and profile spend during the build. For a quick figure, try our Cost Calculator.
For most business use cases, retrieval first. Grounding the model in your documents usually beats tuning on both accuracy and cost, it keeps answers current without retraining, and it lets you cite sources. Fine-tuning is the right call when the model has to learn a format, a tone, or a specialist vocabulary that context alone cannot supply. We test the cheaper path against your real content before recommending the larger one, and the two are often combined.
Rarely, and we will usually advise against it. Training a model from raw data takes 6 to 24 months and heavy ongoing infrastructure, and it only makes sense for organizations with a genuine data moat, deep research staff, and a strategic reason to own the model itself. For almost everyone else, grounding or tuning an existing model reaches the same business outcome far faster and for far less.
Two ways, used together. We ground answers in your own content so the system retrieves real material rather than reasoning from general knowledge, and we cite the source alongside the answer. Then we measure it: an evaluation set of real questions with known good answers, scored on every release, so accuracy is a number you can hold us to rather than a hope.
A grounded use case usually reaches a working version on your real content within a few weeks, and a first production release inside a quarter, depending on the state of your data. Fine-tuning adds weeks to months. We agree the scope and the quality bar during the path assessment, before the build starts.
We keep regulated or sensitive data inside your environment rather than sending it to public endpoints, and we do not use your data to train third-party models. Where your requirements demand it, we deploy open-weight models inside your own cloud. Access control, audit logging, and prompt-injection defense are built into the system rather than added later.
We are model-neutral and match the choice to your product on quality, latency, and cost, working across the major providers and open-weight options that can run in your own environment. The model layer stays swappable by design, so you are not tied to one vendor as the field moves, and switching does not mean rebuilding.