How do I know if I need fine-tuning or if prompt engineering is enough?

The clearest indicator is a measurable quality gap on a specific, well-defined task that persists after you have invested seriously in few-shot examples and retrieval augmentation. If your task requires consistent output formatting, domain-specific jargon comprehension, or behavior that few-shot prompting cannot reliably produce even with 10+ examples, fine-tuning is worth evaluating. Our diagnostic sprint (typically 1–2 weeks) establishes this baseline before you commit to the full investment.

What does LLM fine-tuning cost, and how long does a project take?

Project investment falls in our AI / ML engagement range of $80K to $300K+, depending on data complexity, number of training runs, and serving infrastructure. Compute costs are separate, a typical LoRA fine-tuning run on a 7B–13B parameter model costs $500–$3,000 per run on cloud GPU infrastructure. End-to-end timeline from data audit to production deployment is typically 3 to 6 months. Most of that time is data work, not training.

Which base models do you work with for fine-tuning?

We have fine-tuned Llama 3, Mistral, Phi-3, Gemma, and Qwen families using both full fine-tuning and LoRA/QLoRA adapters. For teams with data-residency or licensing requirements, open-weight models are often the only viable path. For teams that want API-based fine-tuning with less infrastructure overhead, we also work with OpenAI and Anthropic's fine-tuning APIs, though these have more constraints on what you can adapt.

How much labeled data do we need to fine-tune effectively?

For supervised fine-tuning on a narrow task, 500–3,000 high-quality examples is often sufficient with LoRA. More important than raw count is example quality and coverage of your failure modes. We often use synthetic data generation to augment sparse real-world datasets, generating diverse variations of your examples using a frontier model and then filtering for quality. This can substantially reduce the labeled data requirement, though the exact leverage depends on your domain and task.

What happens to our data and the resulting model weights?

Your training data and resulting model weights remain entirely under your control. Fine-tuning runs are executed either in your cloud account or in an isolated environment with no data retention after the engagement. We do not use client data to train shared models, we do not retain weights after handoff, and we provide data processing agreements if your domain is regulated (HIPAA, SOC 2, etc.).

LLM Fine-Tuning Services

When should you fine-tune an LLM instead of prompting it?

Fine-tune an LLM when your domain vocabulary is far outside what general models know, when prompt engineering has hit a quality ceiling, or when you need consistent formatting at cost and latency that rule out frontier models. Codieshub runs a diagnostic sprint to confirm fine-tuning is the right lever, then builds the dataset before training anything.

Last updated November 3, 2026

Methods: SFT, DPO, LoRA, QLoRA
First step: Diagnostic sprint with baseline evals
Data rule: Curated data over volume
Deliverables: Regression-tested model, reproducible training pipeline
Model ownership: Data residency and IP protection built in

Why Codieshub

Built for Teams That Ship

verified

SOC 2 Type II

Enterprise-grade security and compliance built into every engagement.

schedule

Overlapping Hours

Working hours that overlap your US or UK day, so standups, reviews and real-time collaboration happen live.

groups

Vetted Senior Talent

Mid-career to senior engineers, hand-selected and tested before they ever join a client team.

speed

Fast Onboarding

From first call to first commit in 1–2 weeks. No long procurement cycles.

star

4.9 Clutch Rating

Consistently top-rated by verified clients across Clutch, DesignRush, and The Manifest.

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40% Avg. Conversion Lift

The average conversion lift our clients see after a UX redesign.

How we approach LLM Fine Tuning

Fine-tuning a large language model makes sense in a narrow but high-value set of cases: when your domain vocabulary is truly out-of-distribution for a general-purpose model, when prompt engineering has hit a quality ceiling you cannot engineer past, or when you need consistent format adherence at latency and cost targets that exclude large frontier models. Outside those conditions, fine-tuning is an expensive distraction, and knowing which case you are actually in is the first thing Codieshub establishes.

When fine-tuning is the right answer, the outcome depends almost entirely on dataset quality. Our ML engineers have built proprietary data pipelines for synthetic data generation, deduplication, and quality filtering across industries where labeled examples are scarce, from clinical notes to logistics exception reports to legal contract clauses. A model fine-tuned on 2,000 carefully curated examples routinely outperforms one trained on 50,000 noisy ones.

Codieshub has been doing custom model work since before the term 'fine-tuning' entered mainstream product vocabulary. That depth means we can navigate the full decision surface: base model selection, supervised fine-tuning versus RLHF versus DPO, LoRA and QLoRA for cost-efficient adaptation, serving infrastructure, and the regression testing that ensures your fine-tuned model does not silently degrade on capabilities your users depend on.

The challenge

Teams reach for fine-tuning too early, burning months of engineering time and significant compute budget on a technique that better prompt engineering or retrieval augmentation would have solved in a week. Conversely, teams that do need fine-tuning often attempt it without the data infrastructure to get signal from the process, producing models that are worse than the base model on held-out examples.

Our approach

Codieshub begins every fine-tuning engagement with a diagnostic sprint: we baseline your current approach with rigorous evals, identify where it fails, and determine whether fine-tuning is actually the right lever. When it is, we build the data pipeline first (curation, filtering, synthetic augmentation) then select the adaptation method (SFT, DPO, LoRA) against your serving constraints, train on managed infrastructure, and run a full regression eval before any model touches production traffic.

The outcome

A completed fine-tuning engagement delivers a versioned, regression-tested model artifact, a reproducible training pipeline you can retrain when your domain data grows, a serving setup with cost-per-request instrumentation, and clear documentation of where the fine-tuned model outperforms the base and where it does not, because understanding the boundaries is as important as the gains.

Evaluate my fine-tuning case

We'll tell you in 2 weeks whether fine-tuning is the right lever, and what it will cost.

The Work

Shipped systems. Referenceable results.

Archive · 2012 → 2026

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Healthcare

mPATH Health

Healthcare SaaS for mPATH Health

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Engagement Models

Pick the engagement that fits

Four ways to work with us, from surgical staff augmentation to fully managed delivery. All models share the same senior-first talent bench.

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Dedicated Teams

Full-time engineers embedded in your team for long-running engagements.

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Staff Augmentation

Add senior specialists to an existing team: vetted, onboarded, and up to speed in weeks.

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Project Delivery

Managed fixed-scope projects with a committed timeline and deliverables.

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Virtual CTO

Fractional senior technical leadership for architecture, hiring, and strategy.

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Why Codieshub

Six reasons teams stay past the pilot.

The shortlist we get asked about on every call, what actually separates Codieshub from a dev shop.

Data Pipeline Before Training
We build the curation, deduplication, and quality-filtering pipeline before a single training run, because dataset quality determines 80% of fine-tuning outcomes.
Right Adaptation Method
SFT, DPO, LoRA, QLoRA: we select the adaptation technique against your accuracy targets, serving latency budget, and hardware constraints rather than defaulting to the most-hyped approach.
Rigorous Before/After Evals
Every fine-tuned model is validated against a held-out benchmark specific to your use case. We report where it improves, where it regresses, and what trade-offs you are accepting.
Cost-Efficient Serving
LoRA and QLoRA adapters let you run fine-tuned capability on smaller, cheaper base models, often delivering meaningful inference cost savings versus frontier API pricing when quality on your specific task is comparable.
Reproducible Retraining Pipelines
We deliver a versioned training pipeline so your team can retrain as domain data accumulates, without starting from scratch or depending on Codieshub for every model update.
Data Residency and IP Protection
Fine-tuning on sensitive domain data can be run entirely within your cloud account. No proprietary data leaves your environment, and resulting model weights are yours, not ours.

Reviews

Nine CEOs on reference. Three platforms verify the work.

Clutch 4.9
DesignRush 4.9
The Manifest 5.0

Vito Robles

COO · Percensys

“They took feedback seriously, refined the details, and made sure our content and workflows were presented in a way that really works for our learners and admins.”

Percensys case study→

Lisa Dunbar

CEO · Paradigm Labs

“They did an excellent job balancing scientific nuance with a user-friendly experience. It's clear they care about both rigor and design.”

Paradigm Labs case study→

Oliver Dlouhy

CEO · Kiwi

“We move fast and deal with a lot of edge cases. They kept up without cutting corners, which is rare. The team stayed responsive across time zones.”

Kiwi case study→

Farid Huseynov

CEO · Kapital Bank

“Reliability and scalability are critical for us. They approached the engagement with a strong technical foundation and a clear process.”

Kapital Bank case study→

Michael Ou

Founder · CoolBitX

“Security and precision are non-negotiable for us. They demonstrated solid technical judgment, were open to feedback from our engineers, and iterated quickly.”

CoolBitX case study→

John Bradford

CEO · PetScreening

“An external team can be just as committed and driven as our internal one. Their dedication and attention to detail have made them invaluable.”

PetScreening case study→

Ryan Pamplin

CEO · Blendjet

“Managing global scale requires extreme technical precision. Codieshub re-architected our funnels to perform under massive pressure.”

Blendjet case study→

Steve Gebhardt

Founder · RSVLTS

“Our old setup crashed during every major drop until Codieshub built a beast of an engine for us. They handled our traffic spikes perfectly.”

RSVLTS case study→

Davis Rosser

CEO & Co-founder · Elite Amenity

“The digital concierge we co-built is more than tech, it's a paradigm shift in resident experience. Luxury brands can now offer faster services.”

Elite Amenity case study→

Process

How we deliver every sprint.

Our engineers are not freelancers, and we are not a marketplace. Dedicated Codieshub seniors, seated with your team.

Before kickoff

First-touch deep dive.

Pre-kickoff technical and strategic review.

Before a single line of code, we sit with your team to align on stack, constraints, and what success looks like. Our VP Eng, CTO, and senior leads join, not a sales engineer.

Full review of your stack, goals, and constraints before kickoff
Session led by VP Eng, CTO, and the senior leads who'll staff the work
Architecture, tooling, and team shape agreed before the first sprint

Questions

Frequently asked, honestly answered.

The questions we get on every intro call, answered without the marketing gloss.

The clearest indicator is a measurable quality gap on a specific, well-defined task that persists after you have invested seriously in few-shot examples and retrieval augmentation. If your task requires consistent output formatting, domain-specific jargon comprehension, or behavior that few-shot prompting cannot reliably produce even with 10+ examples, fine-tuning is worth evaluating. Our diagnostic sprint (typically 1–2 weeks) establishes this baseline before you commit to the full investment.
Project investment falls in our AI / ML engagement range of $80K to $300K+, depending on data complexity, number of training runs, and serving infrastructure. Compute costs are separate, a typical LoRA fine-tuning run on a 7B–13B parameter model costs $500–$3,000 per run on cloud GPU infrastructure. End-to-end timeline from data audit to production deployment is typically 3 to 6 months. Most of that time is data work, not training.
We have fine-tuned Llama 3, Mistral, Phi-3, Gemma, and Qwen families using both full fine-tuning and LoRA/QLoRA adapters. For teams with data-residency or licensing requirements, open-weight models are often the only viable path. For teams that want API-based fine-tuning with less infrastructure overhead, we also work with OpenAI and Anthropic's fine-tuning APIs, though these have more constraints on what you can adapt.
For supervised fine-tuning on a narrow task, 500–3,000 high-quality examples is often sufficient with LoRA. More important than raw count is example quality and coverage of your failure modes. We often use synthetic data generation to augment sparse real-world datasets, generating diverse variations of your examples using a frontier model and then filtering for quality. This can substantially reduce the labeled data requirement, though the exact leverage depends on your domain and task.

LLM Fine-Tuning Services

When should you fine-tune an LLM instead of prompting it?

Built for Teams That Ship

SOC 2 Type II

Overlapping Hours

Vetted Senior Talent

Fast Onboarding

4.9 Clutch Rating

40% Avg. Conversion Lift

How we approach LLM Fine Tuning

The challenge

Our approach

The outcome

mPATH Health

The metrics that follow from shipping with senior engineers

Pick the engagement that fits

Dedicated Teams

Staff Augmentation

Project Delivery

Virtual CTO

Data Pipeline Before Training

Right Adaptation Method

Rigorous Before/After Evals

Cost-Efficient Serving

Reproducible Retraining Pipelines

Data Residency and IP Protection

Nine CEOs on reference. Three platforms verify the work.

Why Teams Choose Us

SOC 2 Certified

Overlapping Hours

Verified Reviews

First-touch deep dive.

A full team, not a developer.

Daily stand-ups, weekly reviews.

Bench strength on reserve.

Industries we serve

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