An LLM development company designs, builds, and operates systems that use large language models (LLMs), including the prompts, retrieval, evaluation, and monitoring that keep answers correct. It builds the system around the model, and the model itself comes from a provider.
Compare vendors on the same eight-criterion scorecard: use-case fit, evaluation practice, architecture choice, data security, integration, team seniority, post-launch support, and cost model. Weight evaluation practice heaviest, because without an eval set and regression checks, quality drops silently after any model or prompt change. Ask for an eval report from a recent project, and settle ownership of prompts, pipelines, and fine-tuned weights before signing.
A vendor demo looks like proof that a system will hold up in production, and it rarely is. Teams comparing LLM development companies sit through four or five of them in a week and come away unable to tell one from another, because the model underneath each demo is the same model every vendor can call.
What breaks after the demo usually has little to do with that model. MIT Media Lab’s Project NANDA found that 95% of enterprise generative AI pilots produced no measurable impact on profit and loss, and the report traces those failures to how the systems met real workflows, which puts model quality well down the list of causes.
The comparison, therefore, has to happen on the ground, which a demo cannot cover. Here, we start with a shortlist and the choice among API, RAG, and fine-tuning, then move to a weighted scorecard, the cost drivers behind any quote, and the questions worth asking on a first call. SPD Technology publishes this list and is scored on the same criteria as every company on it.
Top LLM Development Companies in 2026: Comparison Table
Large language models (LLMs) are general-purpose text models trained on broad data, and the same handful of them is available to every company on this list. Every company below offers LLM or generative AI development as a named service, publishes at least one production LLM case, discloses its stack, and holds a Clutch rating of 4.7 or higher. SPD Technology appears first as the publisher of this list, and the rest sit in no ranked order.
Search results for this term tend to blur the line between two categories of artificial intelligence companies that behave very differently. OpenAI, Anthropic, Google, Mistral, and Meta supply the models themselves, and their AI research sets what those models can do.
An LLM development company builds the working system on top of that model, which is a separate job with separate skills. Buyers weighing an AI partner beyond language work will find a wider field in our roundup of top AI development companies.
The table below sets out where each company is based, what it charges, and the kind of LLM work it is built for.
Company | HQ | Founded | Rate & Clutch Rating | Best Fit |
|---|---|---|---|---|
SPD Technology | London, UK | 2006 | $50–9/hr · 4.8 | Production LLM systems with RAG and evals; fintech and data-heavy products |
InData Labs | Vilnius, Lithuania | 2014 | $50–99/hr · 4.9 | Data science-led LLM projects |
Addepto | Warsaw, Poland | 2017 | $50–99/hr · 4.9 | Data and AI consulting combined |
Master of Code Global | Winnipeg, Canada | 2004 | $50–99/hr · 4.7 | Conversational AI and customer-facing assistants |
EffectiveSoft | San Diego, US | 2003 | $50–99/hr · 4.9 | Full-cycle software with LLM features |
LeewayHertz | Gurugram, India | 2007 | $50–99/hr · 4.7 | Domain-specific customization |
Azumo | San Francisco, US | 2016 | $25–49/hr · 4.9 | RAG delivery with nearshore teams |
SoluLab | Ahmedabad, India | 2014 | $25–49/hr · 4.9 | Broad generative AI builds |
Company
SPD Technology
InData Labs
Addepto
Master of Code Global
EffectiveSoft
LeewayHertz
Azumo
SoluLab
HQ
London, UK
Vilnius, Lithuania
Warsaw, Poland
Winnipeg, Canada
San Diego, US
Gurugram, India
San Francisco, US
Ahmedabad, India
Founded
2006
2014
2017
2004
2003
2007
2016
2014
Rate & Clutch Rating
$50–9/hr · 4.8
$50–99/hr · 4.9
$50–99/hr · 4.9
$50–99/hr · 4.7
$50–99/hr · 4.9
$50–99/hr · 4.7
$25–49/hr · 4.9
$25–49/hr · 4.9
Best Fit
Production LLM systems with RAG and evals; fintech and data-heavy products
Data science-led LLM projects
Data and AI consulting combined
Conversational AI and customer-facing assistants
Full-cycle software with LLM features
Domain-specific customization
RAG delivery with nearshore teams
Broad generative AI builds
Rates and ratings as published on Clutch, October 2026.
LLM Development Companies to Shortlist: Profiles
Each profile below covers what the company builds with large language models, one signal a buyer can check independently, and the kind of work it suits. Nothing in them goes beyond what the table already discloses. Buyers weighing something broader than a single LLM feature usually start with AI consulting companies, then narrow back to a build partner once the scope is clear.
SPD Technology
London, UK · founded 2006 · $50–99/hr · 4.8 on Clutch
SPD Technology is a custom software development company with 20 years of delivery behind it and more than 460 completed projects, including long-running work for PitchBook, Morningstar, and Blackhawk Network. Its LLM practice sits inside that wider engineering base, so retrieval, guardrails, evals, and scalable infrastructure are part of the same design pass as the product itself.
A high-load support chatbot for a French B2C fashion retailer answered 99% of queries in under 10 seconds and held 100% uptime at 30 requests per second through peak traffic, on a RAG architecture built with LangChain and Mistral on AWS.
Where an API model is the wrong shape for the problem, the generative AI development services extend to training one. That is what a custom LLM for a kids’ education product called for, at a parameter count the client can self-host. Production LLM systems with RAG and evals are a natural fit for fintech and data-heavy products, particularly AI-powered applications that already carry live users.
InData Labs
Vilnius, Lithuania · founded 2014 · $50–99/hr · 4.9 on Clutch
LLM work at InData Labs grows out of a data science practice, so engagements tend to open with the data and the modeling question. Teams that already hold a dataset and a defined quality bar will recognize that order. Starting there also tends to surface data analysis problems, such as gaps in labeling or coverage, early enough to fix them cheaply. Buyers who arrive with that groundwork already done get the most out of the arrangement.
Addepto
Warsaw, Poland · founded 2017 · $50–99/hr · 4.9 on Clutch
Consulting and delivery sit under one roof at Addepto, which suits engagements that begin before the use case is settled. That combination matters most when the data platform and the surrounding AI technologies are being decided together, since each decision constrains the other. That early stage is exactly what the discovery criterion on the scorecard further down is built to test.
Master of Code Global
Winnipeg, Canada · founded 2004 · $50–99/hr · 4.7 on Clutch
Two decades of conversational AI sit behind the customer-facing assistants Master of Code Global builds. For a support or sales assistant that end users talk to directly, that history is the signal to weigh, because tone, escalation paths, and fallback behavior are where consumer-facing AI apps most often come apart. Worth confirming early: how the team handles the handover from assistant to human agent.
EffectiveSoft
San Diego, US · founded 2003 · $50–99/hr · 4.9 on Clutch
Full-cycle software development forms the base at EffectiveSoft, with LLM features added inside larger product builds. Buyers who need the surrounding application built alongside the model work have the clearest case, since one team then owns both the AI capabilities and the product they sit in. For teams that want a single vendor across the whole product, the eval work left behind at sign-off is a trade-off worth accepting.
LeewayHertz
Gurugram, India · founded 2007 · $50–99/hr · 4.7 on Clutch
Domain-specific customization is the stated focus at LeewayHertz, so the work points at narrow vertical behavior where generic output falls short. Industry terminology and strict output formats are the usual reasons to go this route, and the same pressure often extends to knowledge management, where answers must come from a controlled internal corpus. RAG covers much of that ground at lower cost, so customization earns its place once retrieval has been exhausted.
Azumo
San Francisco, US · founded 2016 · $25–49/hr · 4.9 on Clutch
RAG delivery runs through nearshore teams at Azumo, which places the rate band below most of this list. A retrieval build with a defined scope and a constrained budget is a natural first stop here, and the lower rate band puts more weight on how you staff evaluation work. Teams that fix their AI tools and frameworks early get the most out of a nearshore model, since the stack stops moving in the first few weeks.
SoluLab
Ahmedabad, India · founded 2014 · $25–49/hr · 4.9 on Clutch
Generative AI work at SoluLab spans a broad set of build types at the lower rate band. Early-stage teams still deciding what to build often start with that breadth, treating a first engagement as a way to narrow the field of generative AI solutions down to one. Narrowing early matters, because breadth in a vendor stays useful only while the use case is still open.
What Does an LLM Development Company Actually Build with Large Language Models?
An LLM development company designs, builds, and operates software that uses large language models, including the prompts, retrieval, integrations, evaluation, and monitoring that make the model’s output reliable. It builds the system, while the model itself comes from a provider. A good deal of that system is ordinary natural language processing, handling the parsing, classification, and extraction steps that surround the model.
Because 88% of organizations already use AI in at least one business function, the scope is wider than most first briefs assume. Assistants and copilots are the part buyers can see, and the part that overlaps with conversational AI services. The four rows beneath them carry most of the delivery risk.
What Gets Built | What It Is | What Makes It Hard |
|---|---|---|
Assistants and copilots | Chat or in-product help that answers and drafts | Keeping answers correct and on-topic |
RAG over private data | The model answers from your documents and databases | Retrieval quality, data freshness, access control |
Agents and workflows | The model plans steps and calls tools or APIs | Permissions, error handling, verifying each step |
Fine-tuned or custom models | A model adapted to your domain, style, or format | Training data, cost, re-training when things change |
Evaluation and monitoring | Eval sets, quality scoring, cost and drift tracking | Building and maintaining the datasets |
What Gets Built
Assistants and copilots
RAG over private data
Agents and workflows
Fine-tuned or custom models
Evaluation and monitoring
What It Is
Chat or in-product help that answers and drafts
The model answers from your documents and databases
The model plans steps and calls tools or APIs
A model adapted to your domain, style, or format
Eval sets, quality scoring, cost and drift tracking
What Makes It Hard
Keeping answers correct and on-topic
Retrieval quality, data freshness, access control
Permissions, error handling, verifying each step
Training data, cost, re-training when things change
Building and maintaining the datasets
Most engagements combine two or three of these rows. An assistant answers, while an agent plans and acts through tools, which is the step up into agentic AI development services. Model Context Protocol (MCP) is emerging as the standard way to connect a model to those tools and data, and it is quickly becoming the plumbing behind AI automation inside existing products.
API, RAG or Fine-Tuning: The Decision a Good LLM Development Company Helps You Make
Most projects should start with the simplest option that meets the quality bar, and a good partner says so even when a larger build would earn it more. OpenAI’s guidance on optimizing LLM accuracy separates the two problems behind this choice. Retrieval addresses missing or out-of-date context, while fine-tuning addresses inconsistencies in behavior, format, and tone, and that guidance recommends exhausting simpler methods before resorting to either.
Teams still at the validation stage will recognize the same trade-offs in our guide to AI PoC development, where over-building shows up as a cost before it shows up as a delay.
These options combine in practice. RAG with an API model is the most common production setup, with fine-tuning layers added once the remaining problem is behavior. Set against each other, the four approaches differ most in what they risk and what a buyer must verify before committing.
Approach | When It Fits | Main Risk | What to Verify |
|---|---|---|---|
API-based integration | General tasks; fast start; no private knowledge needed | Vendor dependence; data leaving your environment | Data retention terms; fallback to another model |
RAG | Answers must come from your own, changing data | Poor retrieval gives confident wrong answers | How retrieval quality is measured; access control |
Fine-tuning | A consistent style, format, or narrow domain behavior | Cost, stale training data, harder to update | Whether RAG was tried first; who owns the weights |
Custom or self-hosted model | Strict data residency, very high volume, or special requirements | High build and operating cost | Total cost of ownership; in-house skills to run it |
Approach
API-based integration
RAG
Fine-tuning
Custom or self-hosted model
When It Fits
General tasks; fast start; no private knowledge needed
Answers must come from your own, changing data
A consistent style, format, or narrow domain behavior
Strict data residency, very high volume, or special requirements
Main Risk
Vendor dependence; data leaving your environment
Poor retrieval gives confident wrong answers
Cost, stale training data, harder to update
High build and operating cost
What to Verify
Data retention terms; fallback to another model
How retrieval quality is measured; access control
Whether RAG was tried first; who owns the weights
Total cost of ownership; in-house skills to run it
How to Evaluate an LLM Development Company: A Scorecard
Deloitte’s 2026 survey of more than 3,000 director-to-C-suite leaders found that only 25% of respondents have moved 40% or more of their AI pilots into production, while 54% expect to reach that level within three to six months, which places most buyers on the near side of that line today. Our AI production-ready checklist lays out what must be true before a pilot crosses that line.
Using the scorecard, you can rank each vendor from 1 to 5 on every criterion, multiply by the matching weight, and compare the totals. The weights favor what keeps the system reliable after launch, which is why evaluation practice carries twice the weight of post-launch support and four times the weight of references. The gap between a pilot that demos well and a system that runs under load is wide and well documented.
Each criterion below pairs a question worth asking with the answer that should slow a decision down. Taken together, they say more about how a vendor works with AI models than about which ones it picks.
Criterion | Weight | What to Ask | Red Flag |
|---|---|---|---|
Evaluation practice | 20% | How do you evaluate outputs, and what happens when the model or prompt changes? | “We tested it”; no eval set |
Use-case fit and discovery | 15% | How will you decide whether an LLM is the right tool for this? | A solution proposed before the problem is understood |
Architecture and model choices | 15% | Why API, RAG, or fine-tuning for our case? | One answer for every client; model lock-in |
Data security and IP | 15% | Where does our data go, and who owns prompts and weights? | Vague data handling |
Integration and operations | 10% | How does this connect to our systems and get monitored? | No monitoring plan |
Team seniority | 10% | Who will work on this, and what have they shipped? | An unnamed team |
Post-launch support | 10% | Who maintains evals and handles model updates? | No plan after delivery |
References | 5% | Can we speak to a client running your system in production? | A demo-only portfolio |
Criterion
Evaluation practice
Use-case fit and discovery
Architecture and model choices
Data security and IP
Integration and operations
Team seniority
Post-launch support
References
Weight
20%
15%
15%
15%
10%
10%
10%
5%
What to Ask
How do you evaluate outputs, and what happens when the model or prompt changes?
How will you decide whether an LLM is the right tool for this?
Why API, RAG, or fine-tuning for our case?
Where does our data go, and who owns prompts and weights?
How does this connect to our systems and get monitored?
Who will work on this, and what have they shipped?
Who maintains evals and handles model updates?
Can we speak to a client running your system in production?
Red Flag
“We tested it”; no eval set
A solution proposed before the problem is understood
One answer for every client; model lock-in
Vague data handling
No monitoring plan
An unnamed team
No plan after delivery
A demo-only portfolio
#1. Ask for an Eval Report Before Anything Ships
Tests and evals answer different questions. A test checks deterministic code, where the same input returns the same result every time, so a pass means the code behaves. An eval scores non-deterministic output against real cases and a written rubric, and writing that rubric is most of the work in eval engineering for AI products.
The practice runs as a loop. An eval set is built from real cases, regression checks re-run it whenever the prompt, the data, or the model changes, and high-stakes answers pass through human-in-the-loop review before release. Quality monitoring in production then feeds real failures back into the eval set.
Ask any vendor for an eval report from a recent project. Buyers should expect concrete metrics in that report, not just a claim that the system performed well. A consumer health-scoring iOS app that combined computer vision, retrieval-augmented generation, and LLMs built by our AI engineering specialists reached 95%+ model accuracy, a quality bar arrived at by measurement.
SPD Technology holds its own delivery to the same standard. Under its Verified Velocity framework, 80% of changes ship with a complete automated evidence trail, which means the tests, evals, and review records behind a release can be reconstructed afterward without chasing anyone for context.
#2. Weigh the System Around the Model Before the Model Itself
An agent is a model plus a harness, and the harness includes everything around the model: instructions and context, retrieval, tools, middleware, guardrails, and orchestration. Context engineering decides what instructions, knowledge, examples, and tools the model receives, and when.
LangChain’s engineering blog on harness engineering held the model constant to isolate that effect. With gpt-5.2-codex fixed and only the system prompt, the tools, and the middleware changed, their coding agent moved from 52.8 to 66.5 on Terminal Bench 2.0, a gain of 13.7 points that carried it from outside the top 30 into the top 5. The same model inside a different system produced a different result, so the system is what a buyer is actually purchasing.
Serhii Leleko
AI & ML Engineer at SPD Technology
“Most ‘the model is wrong’ tickets we investigate turn out to be context problems. The retrieval step returned a passage that looked relevant and wasn’t, or the instruction the model needed was never written down anywhere. Swapping the model hides that for a week. Fixing what the model receives holds.”
That pattern is why harness work pays off more reliably than a model swap, and why a vendor able to describe its context pipeline in detail is usually the one whose system holds up under load.
Ready to go deeper on what surrounds the model? Explore agent harness engineering in our dedicated guide.
#3. Check Data Security, Privacy, and IP Ownership
Security answers turn vague in the same places on almost every call. Work through the list below early, because each item lands in the contract, where it gets expensive to fix.
- Where your data is processed decides which regulators have a claim on it.
- The data you actually send to third-party APIs marks the real boundary of what stays private, and the architecture diagram often draws that line somewhere else, especially when it includes proprietary data.
- Retention terms and training rights decide how long your inputs live after the session ends and who gets value from them.
- Access control inside the application keeps one user’s retrieval results from turning up in another user’s answers.
- Ownership of the prompts, the pipelines, and any fine-tuned weights sets how much you would have to rebuild if you changed vendors.
- What you receive at handover separates owning a system from renting one.
Classifying healthcare product eligibility within a HIPAA and SOC 2 environment meant the compliance boundary had to be designed into the system before the model touched anything, and the FSA/HSA payments platform achieved over 80% eligibility-classification accuracy across more than 200,000 products kept in sync.
LLM Development Cost and Timeline: What Drives Them
Quotes for the same brief routinely differ by an order of magnitude, because AI solutions scoped from a one-page spec carry very different assumptions about data and evaluation depth that nobody wrote down. The spread widens at the top of the table, where enterprise AI platforms absorb integration and governance work that smaller builds never touch.
Project Type | Typical Cost |
|---|---|
Simple LLM integration | $15,000–50,000 |
Production-ready LLM application | $100,000–500,000 |
Enterprise LLM platform | $500,000+ |
Project Type
Simple LLM integration
Production-ready LLM application
Enterprise LLM platform
Typical Cost
$15,000–50,000
$100,000–500,000
$500,000+
Published third-party market ranges for LLM development, not SPD Technology pricing. Mid-market engagements are reported at $75,000–$300,000 over 8–20 weeks.
Data readiness moves the needle most, because documents that need cleaning, chunking, and permission mapping before retrieval work turn into data engineering services long before any prompt gets written. Each integration adds an auth model, a rate limit, and a failure mode to handle, which is where enterprise AI projects quietly absorb weeks.
Evaluation depth scales with the stakes, since a rubric for internal drafting costs less to build than one for regulated advice. Inference volume determines both the running costs and the cloud infrastructure required, which is where AI applications with consumer-scale traffic diverge sharply from internal tools. Compliance work adds review cycles that affect the schedule more than the build. Those review cycles are what stretch a custom AI/ML project timeline past the engineering estimate.
Token spend grows with usage, so ask any vendor for a cost-per-request estimate and for how requests are routed between larger and smaller models.
Red Flags When Hiring an LLM Development Company
These patterns show up across LLM development companies of every size, and any one of them earns a direct follow-up question on the same call.
- Without an eval plan, quality is whatever the team says it is, and that falls apart the first time a model updates.
- A demo-only portfolio shows what got built and says nothing about what survived production traffic.
- Vague answers about data handling usually mean the precise answer is uncomfortable, and it gets no better once it reaches the contract.
- Locking into one model or vendor turns an upstream pricing change into your problem.
- No monitoring plan means your customers find the failures before your team does.
- Promises of guaranteed accuracy, or of a system that never hallucinates, describe something nobody can deliver, and our guide to guardrails for AI engineering covers the controls that genuinely reduce bad output.
Questions to Ask Top LLM Development Companies Before You Sign
Bring this list to the first call and keep the answers in writing. Top LLM development companies answer these in specifics, with a project name and a number attached, and the vague responses tend to cluster in the same two or three rows.
✓ | Question | What a Good Answer Confirms |
|---|---|---|
⃣ | How do you evaluate outputs before release, and can we see an eval report? | Quality is measured, not assumed |
⃣ | What happens when the model, prompt, or data changes? | Regressions are caught before users see them |
⃣ | Why this approach (API, RAG, fine-tuning) for our case? | The architecture fits the problem |
⃣ | Where is our data processed, and what goes to third-party APIs? | Data handling is known and documented |
⃣ | Who owns the prompts, pipelines, and any fine-tuned weights? | You can leave without starting over |
⃣ | What will a request cost, and how do you control it? | Running cost is planned |
⃣ | What do you monitor in production? | Problems are found by the team, not by customers |
✓
⃣
⃣
⃣
⃣
⃣
⃣
⃣
Question
How do you evaluate outputs before release, and can we see an eval report?
What happens when the model, prompt, or data changes?
Why this approach (API, RAG, fine-tuning) for our case?
Where is our data processed, and what goes to third-party APIs?
Who owns the prompts, pipelines, and any fine-tuned weights?
What will a request cost, and how do you control it?
What do you monitor in production?
What a Good Answer Confirms
Quality is measured, not assumed
Regressions are caught before users see them
The architecture fits the problem
Data handling is known and documented
You can leave without starting over
Running cost is planned
Problems are found by the team, not by customers
Specific answers to five or more put a vendor on the shortlist, and a vague answer to the first two is enough reason to keep looking.
How SPD Technology Approaches LLM Projects
SPD Technology builds LLM systems for production and engineers the retrieval, guardrails, evals, and infrastructure that keep answers reliable under real traffic. Twenty years of custom software work and more than 460 delivered projects sit behind that, including 13 years with PitchBook and a partnership with Poynt spanning more than five years.
Document processing ran 3x faster at 5x lower cost for a financial data and publishing company working across France, the UK, and the USA, after the extraction pipeline scaled from 25 source structures to more than 50. That pipeline paired natural language processing services with YOLO, Camelot, AWS Textract, and a GPT stage, which enabled it to absorb layouts that kept changing.
A US EdTech company needed behavior tuned to children aged 9 to 12 without the running costs and control trade-offs of a frontier API. A lightweight domain-specific model of roughly 3 billion parameters answered that, at a size the client can self-host. Where an API model does fit, a RAG architecture on AWS built with LangChain and Mistral carries catalog and policy questions at retail peak volumes for a French fashion retailer.
One team owns both architecture and execution, so nothing is handed off between the people who design a system and those who run it. Human-in-the-Loop sits inside that design as a control with defined thresholds, and compliance-by-design means the regulatory boundary, whether HIPAA, SOC 2, or the EU AI Act, shapes the architecture before the model touches production data.
The same discipline governs delivery itself: under our Verified Velocity framework, 80% of changes ship with a complete automated evidence trail, so any release can be audited after the fact. For a buyer, that is the difference between a vendor describing its process and a vendor able to show it.
The fit is software and fintech companies that need an LLM feature to work reliably inside an existing product. A one-off demo, or training a foundation model from scratch, is work for someone else.
Key Takeaways
- The same models are available to every vendor, so the engineering around the model, meaning context, retrieval, tools, and guardrails, is what separates a reliable LLM system from a demo.
- Tests confirm that code works, but only evals scored against real cases and a rubric show whether an LLM feature’s answers are good, which is why “we tested it” isn’t enough.
- An LLM feature that passed last month can fail after a model or prompt update, so a vendor without regression evals can’t tell you when quality drops.
- RAG is usually the right way to give a model access to your company’s knowledge, because fine-tuning bakes in data that can go stale and is costly to update.
- A partner that recommends fine-tuning or a custom model before trying an API or RAG is adding cost and risk the use case may not need.
- If the contract doesn’t assign you the prompts, pipelines, and fine-tuned weights, changing vendors means rebuilding the system.
- Token spend grows with usage, so a vendor that doesn’t provide a cost-per-request estimate leaves the highest running cost unmanaged.
In short: the model is the commodity, and a vendor’s evaluation practice, architecture judgment, and data handling are what you are actually buying.
FAQ
How do I choose an LLM development company?
Choosing an LLM development company means scoring every candidate on the same eight criteria and comparing totals, since AI solutions that look alike in a demo differ sharply in how they are evaluated and maintained.
Weight evaluation practice most heavily, because it determines whether answers stay correct after launch, followed by use-case fit, architectural judgment, and data handling. Ask how each vendor evaluates outputs, what happens when the model or prompt changes, and who owns the prompts and weights. A development partner that answers with a project name and a number belongs on the shortlist.
How much does LLM development cost and how long does it take?
LLM development costs anywhere from $15,000 to $500,000 or more, and a typical engagement runs 8 to 20 weeks. Published third-party market ranges put a simple LLM integration at $15,000 to $50,000, a production-ready LLM application at $100,000 to $500,000, and an enterprise LLM platform above $500,000. Mid-market engagements are reported at $75,000 to $300,000. These are market figures and not SPD Technology prices, so ask for a cost-per-request estimate alongside the build price, since token spend keeps growing long after delivery.
Should we fine-tune a model, use RAG, or call an API?
Start with an API call. Add RAG once answers have to come from your own changing data, and keep fine-tuning in reserve until the remaining problem is behavior, format, or tone. Retrieval fixes what the model does not know, while fine-tuning fixes how it writes. A custom or self-hosted model earns its place only under strict data residency, very high volume, or unusual requirements, and prompt engineering with a better retrieval setup clears most quality complaints before any change to model architecture is justified.
How do LLM development companies evaluate quality and prevent hallucinations?
LLM development companies measure quality with evals, which score output against real cases and a written rubric, then re-run that set as regression checks whenever the prompt, data, or model changes. Hallucinations are reduced, never eliminated. Grounding answers in retrieved documents, citing sources, constraining output formats, and routing high-stakes answers to human review all cut the rate. Production monitoring catches what slips through, and those real failures go back into the eval set.
Who owns the model, prompts, and data after the project?
Settle ownership in the contract before signing. Name the prompts, the retrieval pipelines, the eval sets, and any fine-tuned weights explicitly, because default vendor terms often leave them with the vendor. Your source data should stay yours, with written limits on retention and on any use for training. Base model weights from a provider stay with that provider under its license. Without these clauses, changing vendors means rebuilding the system from scratch.
What should an LLM development company deliver besides the code?
Expect the eval set and its rubric, the prompts and retrieval configuration, monitoring dashboards and alerts, and documentation of the data flows and access control. A runbook for model updates matters just as much, because it tells your team what to re-run the day a provider ships a new version. Anything built on AI agents should also hand over the tool permissions and the error-handling logic. Whatever is missing from that list becomes your team’s work on day one.