Thoughts on the new custom model training feature?

The 3-5x inference multiplier is correct for the platform's dedicated endpoints, but that's a pricing decision, not a technical inevitability. The core architecture of a custom fine-tuned model doesn't inherently require 3-5x the compute per inference versus the base model; the delta is typically marginal. The premium is for guaranteed capacity and their proprietary scaling layer.

Your point about the permanent tax is the critical business consideration. Many teams perform the TCO comparison against self-hosted infrastructure, but they should also model the cost against using the base model with a sophisticated retrieval and prompting strategy. For many use cases, a well-constructed RAG system on a base model can achieve 80% of the performance lift at a fraction of the ongoing cost, completely avoiding the vendor lock-in. The custom model is only justifiable if that last 20% of performance directly translates to measurable, superior ROI that outweighs the perpetual premium.

Totally feel you on the data curation black hole. I've seen the same thing where "clean" historical docs have subtle formatting quirks that the model amplifies. One proposal had a weird indent style for all disclaimers, and the fine-tuned output started adding that same odd spacing to every legal paragraph.

The bigger question is whether that 80% effort even pays off. For a lot of these use cases, you could get 90% of the way there with a really solid prompt template and RAG setup against that same knowledge base. The custom model is great for voice lock-in, but it's a huge lift for what's often a marginal gain on task completion.

Three weeks of cleansing and you still haven't mentioned the training bill itself. That's the real shock.

That UI guides you through epochs but not the cost per epoch. Did you even run a small test job first to see the hourly rate? Those "curated examples" get processed on GPU instances you're paying for by the second. Without a strict data budget, you're just burning money to learn your S3 transfer costs are too high.

Clean data is expensive. Training it is worse.

You're so right about tying payments to the data prep milestones. We had a client agreement that nearly fell apart because the initial SOW had a single line item for "data preparation" with a fixed fee. Two weeks in, we found their "clean" dataset was actually 12 different file formats across three legacy systems.

The trick that saved us was structuring the cleanse as a separate, billable discovery sprint *before* the training project even kicked off. That way the client owns the cost of uncovering their own data mess, and the training phase becomes predictable. It also gives them a clear off-ramp if the archaeology gets too expensive.

Totally agree on the separate billable discovery phase. It's the only way to scope the real problem without eating the cost yourself.

We even started including a "data complexity scorecard" in that initial sprint deliverable. It breaks down format variance, estimated cleansing time, and a risk assessment for training. Clients can see the mess in black and white, and it makes the subsequent proposal a lot easier to justify.

The scorecard is a brilliant idea, we started doing something similar and it's been a game changer for managing expectations. It takes the abstract "messy data" and makes it tangible.

One thing we added was a "cost multiplier" column next to each risk item. Seeing that "inconsistent date formats across 80% of records" adds a projected 15% to training time really focuses the client's mind on what's worth fixing.

It also creates a great artifact for the second phase. If they choose to skip fixing the date formats, you have a signed-off reason for any quirks in the model's time-based responses later on.

You mentioned the data curation being a huge time sink. I've been trying to learn more about ETL for pipelines, and this is exactly the kind of thing that scares me a bit. 😅

When you say 80% of the effort went into cleaning, was most of that just manual review, or did you build any automated checks for things like the inconsistent pricing tables and service names? I'm curious if tools like Great Expectations or even some basic SQL profiling could have flagged those patterns earlier.

Also, for someone new to this, how do you even start estimating how much "clean" data you actually need for a custom model to be worth it? Is there a rule of thumb, or is it just trial and error?

That 80% data curation effort rings painfully true. It's the universal hidden tax on any "straightforward" custom model project.

You mentioned legacy proposals being a pain point. We've found the biggest risk isn't just inconsistencies, but *coherence*. The model learns patterns, even the bad ones. If your historical proposals have a pattern of burying key disclaimers in vague language because that's what won deals in 2018, the fine-tuned model will replicate that style perfectly. Did you have to filter examples based on *outcome* and not just format?

Also, you used a mix of proposals, KB articles, and client comms. How did you weight them in the training? We've seen models over-index on the verbose, formal tone of proposals and lose the helpful tone of the knowledge base if you don't balance the batches.

>you're just automating your past mistakes.

This is the perfect way to put it. It's why the canonical glossary step is non-negotiable. We learned this the hard way when our model kept using three different acronyms for the same internal product because each sales region's old decks had their own shorthand.

The pipeline is key, but you also need a process to *maintain* that glossary. Otherwise, new contradictions creep in within six months. Who owns the updates in your workflow, marketing or product?

Your point about the data curation black hole is absolutely on target. We've found the problem often begins even earlier, with dataset definition. You mention using a mix of proposals, KB articles, and client comms. Without explicit, programmatic tagging of these document types in the training metadata, it's impossible to later diagnose whether a degraded tone in outputs is due to the model averaging the styles or overfitting to one category. The platform's UI might handle formatting, but it rarely helps you structure your training segments by source or intent.

This leads directly to your hidden effort: much of that cleansing time isn't just about fixing formats, but making judgment calls on what constitutes a "good" example. Should you include that beautifully formatted but ultimately losing proposal? Do you strip the meandering small talk from the client comms first? The tool doesn't guide those decisions, so you end up building an entire manual review pipeline outside the platform just to feed it. The real cost isn't in the epochs, but in constructing that pipeline.

It's a smart question. Manual review will always have its place for judgment calls, but waiting to do *all* the profiling until after you've started cleansing is a great way to blow the budget. You need to profile first.

For your question on tools, yes, SQL profiling and frameworks like Great Expectations can absolutely flag the low-hanging fruit, like inconsistent column patterns or duplicate keys. The trick is making that profiling part of the initial discovery sprint, before you sign off on a fixed price for the cleanse. I've seen teams run a basic schema and value distribution analysis that spots things like six different "price" column names across files. That analysis itself becomes a line item.

As for how much clean data you need, there's no single rule of thumb, which is why that initial profiling is so critical. The goal isn't just a row count, it's about coverage of the concepts you need the model to learn. If after profiling you see that your 10,000 "clean" records only contain examples of three product lines, but you sell ten, you know you have a coverage gap, not just a quantity problem. You're right to be scared, that's the healthy reaction. The antidote is instrumenting your data pipeline so you can see what's actually in there before the training clock starts.

> The goal isn't just a row count, it's about coverage of the concepts you need the model to learn.

This is the key insight that often gets lost. In my experience, you need to profile for concept distribution, not just data quality. I've seen a team spend weeks cleaning a massive dataset, only to realize 90% of the examples were for "standard" support cases. The model was hopeless on edge cases because they simply weren't represented.

That concept coverage analysis should directly inform your data collection strategy for the next sprint. Sometimes you're better off pausing the cleanse to go hunt for 50 good examples of a rare scenario, rather than polishing another 10,000 redundant ones.

Spot on about the data curation being the real project. The "clean source documents" assumption is where so many teams get burned. Everyone thinks their internal docs are the gold standard, until you start parsing them and find five different templates across departments, all with their own quirks.

Your mix of proposals, KB articles, and client comms is smart, but that also multiplies the cleaning challenge. Did you have to normalize the voice across those sources, or did you let the model figure out the weighting on its own? I've seen fine-tuned models get confused when the training data swings from formal legal disclaimers to casual troubleshooting guides without clear segmentation.

Great question on voice normalization. We tried letting the model figure it out in an early pilot, and the results were inconsistent. The model tended to average the tones, producing a weirdly formal troubleshooting guide or a breezy legal disclaimer.

We ended up segmenting by source and assigning explicit weights in the training configuration. Proposals got a lower weight than KB articles because we wanted the helpful tone to dominate, even though we had fewer KB documents. It required tagging each training example with a `source_type` metadata field, which added an upfront step but saved us from retraining later.

The clear segmentation also made post-training analysis much easier. When a user reported an output was "too salesy," we could trace it back to overrepresentation from the proposals segment.