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Transcripts that never leave
Session recordings are transcribed and summarised by a model on the same infrastructure that stores them. No third party ever receives the audio or the text.
Private LLM fine-tuning
The data cannot leave. The model can come to it.
Plenty of AI projects stop at a policy that forbids sending records to a third party. An open weight model running on your own hardware removes the question rather than arguing with it.
- Open weights, hosted by you
- No per token meter running
Your own materialRecords and terms
Prepared into examplesWhere the work is
Weights adjustedAn open model, tuned
Running on your hardwareNo outbound call
What private LLM fine-tuning is
Private LLM fine-tuning adjusts the weights of an open weight model using your own material, then runs the result on infrastructure you control. Because the model sits inside your network or your own cloud account, records reach it without leaving your boundary. The tuning is what makes it fluent in your vocabulary, and the hosting is what makes it private.
The project that stopped at the policy
A hosted endpoint is the right answer for most work and it is genuinely hard to beat. It stops being available the moment somebody decides the records cannot go to a third party. That decision is usually correct and it ends the conversation, unless the model can come to the data instead.
- 01
Legal said no and legal was right
Some material is not ours to send anywhere. Retention settings do not change that. The objection is to the transfer itself.
- 02
The general model mangles your vocabulary
Industry terms and internal shorthand come back wrong. Prompting patches it up, then stops helping.
- 03
The bill scales with success
Per token pricing is cheap while volume is low. It keeps growing, and a server that costs the same every month starts looking sensible.
The architecture of a private deployment
- 01
Preparing the material
Most of the work is turning your material into examples. Quality decides the outcome far more than size.
- 02
Tuning an open weight model
Llama and Mistral are the usual families. Weights adjust to your material, rather than a prompt sitting in front of it describing it.
- 03
Hosted inside your boundary
The result runs in your own cloud account. Nothing calls out, because there is nothing to call.
How the first three weeks run
- Week 1
01We check whether tuning is the answer
Two days on the constraint and the material. Often a hosted endpoint is the honest recommendation.
- End of week 2
02You get a costed plan
The plan prices the hardware, the tuning and the upkeep against what you spend now. You decide whether the move pays.
- From week 3
03Prepared, tuned and compared
The dataset gets built first. It decides everything after it. The tuned model is measured against the hosted one.
Renting an endpoint against owning a model
Most work should rent. This is the comparison for the work that cannot.
| Measure | A hosted endpoint | Your own model |
|---|---|---|
| Where records go | To a third party, retention off | Nowhere. There is no outbound call |
| What it costs | Per token, rising with use | The hardware, flat once running |
| Your vocabulary | Described in a long prompt | Learned by the weights |
| Output you can rely on | Prompted, and checked | Trained toward, and still checked |
| Who keeps it running | The provider does | Somebody has to, and that is a cost |
Where a private model is the only option
Three situations where a hosted endpoint is not available. Each ends with the model inside the boundary.
A financial product
Structured output, trained rather than prompted
A tuned model returns the shape your ledger expects without a long prompt describing it each time. Output still gets validated in code, because trained toward is not the same as guaranteed.
A closed community
Moderation that runs in house
Posts are checked against your own rules, by a model running on your own servers. What members write never leaves the platform.
What owning the model actually means
Private deployment is a trade. Both sides are worth stating. It buys a boundary. It costs capability and upkeep.
- The boundary is architectural
- No outbound call exists. There is no policy to trust.
- The capability is lower
- The largest hosted models still reason better.
- Somebody runs it
- Updates, hardware and monitoring are ongoing work. Usually ours.
What a private deployment is built from
Open weight models, and infrastructure you already know. Nothing here is a requirement, and the work goes ahead against whatever hardware you have.
- Models worth tuning
- LlamaThe usual starting pointMistralSmaller, and often enoughQwenWhere the work is multilingual
- Preparing and tuning
- PythonDataset preparation and evaluationPostgreSQLWhere your material already sitspgvectorRetrieval, where tuning is not needed
- Running it
- DockerSame on your hardware or oursAWSYour own cloud accountSentryKnowing when it stops
What people sayNot our words.
Not our words.
Theirs.
Every quote here is a real Trustpilot review. We did choose which ones to show you. The score beside them is the part we do not control, and it counts all 29 reviews.
Trustpilot4.4out of 5Excellent29 reviews · checked 19 August 2026Read all of them
These reviews are for Appkodes, our software product division.
A Joysale client on the product and the service
He runs a marketplace built on Joysale, our Letgo style product. The clip is his own account of working with us.
A Fantacy client on the build
Fantacy is our Amazon style retail product. He goes through what was built and how the work ran.
An Airfinch client, filmed after his written review
Airfinch is our Airbnb style rentals product. He had already left the same review on GoodFirms before recording this.
A second Joysale client on the same product
Another marketplace running on Joysale. Worth watching beside the first, since the two bought the same thing.
Trustpilot
I've worked with Appkodes for 7 years on 4 different projects. We constantly require support or the implementation of new features, and we have the guarantee that the quality of their work remains the same throughout this time.
Juan VásquezSep 2025Trustpilot
Appkodes exceeded all of our expectations! From the very first contact, the team demonstrated a high level of professionalism, technical expertise, and commitment to quality.
Global SoftwinMay 2025Trustpilot
Appkodes team helped me to launch my healthcare application very quickly. Their software was very close to my requirements and adding some extra features made my project easy.
WassimApr 2025Trustpilot
I so much love your services and I will continue to patronize your company.
Kolawole Alaba JohnsonApr 2025Trustpilot
I worked with AppKodes for a website and mobile app development project, and overall, I'm very satisfied with the results. Their team was responsive and flexible throughout the process, and they delivered a product that met my expectations both in design and functionality.
Яша ФирузApr 2025Trustpilot
It was a good experience working with the team. They understood my ideas clearly and built everything as expected. The team was supportive, quick to respond, and helped me whenever I needed changes. Thank you for your hard work and support!
Prem SharmaApr 2025Trustpilot
I have got a mobile app project going on successfully with the team. Their Support is good. turn around time for any requirement is great. Every detail of my app is meticulously designed. THANK YOU APPKODES.
Fast Fiber NetworksApr 2025Trustpilot
Appkodes is a leader in developing high-quality applications and websites. It was a pleasure working with them, and this certainly won’t be our last collaboration. My experience was exceptional, they developed an outstanding app and website, with smooth and refined interactions.
BaderApr 2025Trustpilot
Overall very good experience. I have been availing services for past 3 years. They are available for discussions and resolving issues whenever we faced any. Mr. Saravana has been looking after our project and I'm very much happy with his timely response. I would definetely recommend.
Vannala RajuApr 2025Trustpilot
Initially, I was hesitant to deal with them, believing their customer service would be poor. However, I was surprised. They act with great responsibility and professional efficiency. My regards to them.
Ahmed NhariApr 2024Trustpilot
You have been supporting me very quickly in every matter, especially in the last 2 months, and this makes me very happy.
Deniz SeçerJan 2024Trustpilot
Very professional. Our project was quite complex and they covered all the aspects. Appkodes did an amazing and professional job developing and creating our Apple and Android apps. I was positively impressed with the communication you can absolutely trust on what they say.
Anu JosephJul 2023Trustpilot
Mani and Saravanan of the Appkodes team are amazing, they have done the best to create and support my project! I give them 10/10 stars for their efforts and work!
Joely CineasJul 2023Trustpilot
It was really great, they are there for me whenever I had a problem or to fix something. Thank you so much Ameer
GomezApr 2023Trustpilot
I've been working with Appkodes for almost a year and i can recommend them to work with as they are so much friendly and professional and you can clearly see it once you start your project right away. They are intact and they are transparent with their communication.
JohnFeb 2023Trustpilot
I have to be honest, sometimes it's hard to find a company or someone abroad to do your project. Not only might you waste your time and money, there is this thing called trust. In business you must trust the person you are dealing with.
Zack GizawDec 202216 of 29 reviews, plus 4 videos
Businesses we have built for
Frequently asked questions
Do we need expensive hardware for this?
Less than people expect. A quantised model gives up some quality for a large drop in what it needs to run. A great many jobs are served well by that trade. The sizing gets costed in week two.
Which open weight models do you fine-tune?
Usually the Llama or Mistral families, picked for what the job needs rather than for what is newest. Model families are named here deliberately and versions are not, because whichever is current when you read this will not be current for long.
How long does fine-tuning take?
The training itself is the short part. Preparing a dataset worth training on is where the time goes, and it depends entirely on what state your material is in. Week one exists to answer that first.
Is custom AI model training better than a long prompt?
Not always, and trying the prompt first is cheaper. Tuning earns its place when prompting has plateaued, or when the prompt has grown long enough to cost real money on every call.
What does on-premise LLM deployment involve day to day?
Somebody keeps it running. Hardware, updates and monitoring do not vanish just because the model now belongs to you. That upkeep is a real cost. We do it after handover unless you would rather not.
Is secure AI model hosting just retention being off?
No, and the difference is the point. Retention off means a promise not to keep it. Hosting it yourself means nothing is sent, so there is no promise to rely on and nothing to audit.
Next stepLet AI do the repetitive
Let AI do the repetitive
half of the job.
Data entry, answering the same tickets, chasing numbers between systems. We automate the parts that repeat. Your team keeps the parts that need judgement.
