Ertas Builder Plan ($25/mo): Who It's For, What You Get, Honest Review

The Ertas Builder plan is $25 per month. This is a detailed breakdown of what that gets you, who it is right for, when it is not enough, and how the math works against the API costs it typically replaces.

No spin. If Builder is not the right plan for your situation, this review says so.

What's Included in Builder

100 credits per month. Credits are consumed when you run training jobs. A typical fine-tuning run on a 7B model with 500-1,000 training examples costs 8-15 credits. With 100 credits, you can realistically run 7-12 training jobs per month — enough for regular experimentation, iterative improvement, or maintaining 2-3 active projects with monthly retraining.

Full model export. Turn on "Export full model" in Training Config and the run hands back a merged 16-bit safetensors checkpoint, not just the LoRA adapter. This is the single biggest jump from Free, where you get the adapter and have to merge it against the base model yourself before it is usable anywhere. The full checkpoint drops straight into a serving stack, and it leaves quantisation decisions to you rather than baking Q4_K_M in.

Continue training from your own models. The full checkpoint is also what you train again. Pick a model you already trained as the base for the next run via "Continue from your models" instead of starting from the base model every time. When a run gets the tone right but the model still rambles, you build on that run rather than throwing it away and paying for a from-scratch retrain.

Models up to 14B parameters. Free tier caps at models under 5B. Builder unlocks 14B models (Qwen 2.5 14B, Llama 3.1 14B, etc.). For most narrow-task applications, 7B is sufficient. 14B becomes relevant when you need better reasoning or multilingual capability while still running locally on a mid-range VPS.

50 GB storage. Covers your uploaded datasets and exported GGUF files. A fine-tuned 7B GGUF (Q4 quantized) is approximately 4-5 GB. 50 GB comfortably stores 8-10 exported models plus the datasets that produced them.

Data Craft and Prompt Studio. Prompt Studio builds a generation prompt from your own context, which you run in the AI tool you already pay for and import back as JSONL. Useful when you have limited real-world data.

Three inference samples on every run. When a run finishes, Studio puts a small fixed set of prompts through the fresh model so you can see it produces coherent, on-format text before downloading anything. Grading it properly means running your own probe set against the exported model, which the Verifying exports guide covers. A built-in evaluation suite is on the roadmap.

The Credit System In Practice

At 100 credits per month, you can run roughly 6-12 training jobs depending on model size and dataset. For a typical indie developer running one fine-tuned model with monthly retraining, that is substantial headroom.

Who Builder Is Right For

Indie developer replacing an OpenAI API bill. You have a SaaS app currently spending $80-400/month on API calls. You want to fine-tune a model on your domain, export GGUF, run locally. Builder + a $26/month Hetzner VPS replaces that bill at $51/month total — and the savings grow with usage since your inference cost is now zero.

Solo builder with 1-3 active projects. You are experimenting with fine-tuning for different use cases, running training experiments every 1-2 weeks, maintaining 2-3 active models. Builder's 3 projects and 100 credits per month fits this pattern.

Non-technical founder adding AI features. You do not have ML engineers. Ertas's visual interface lets you fine-tune without code. Builder unlocks the credit budget and the model sizes you need to do this seriously, not just once.

Someone validating a fine-tuning use case. You are not sure yet if fine-tuning is right for your application. Builder lets you run multiple experiments, compare results, and evaluate quality before committing to infrastructure investment.

Use Case: Indie Developer Replacing OpenAI API

Here is the math for a concrete scenario.

The situation: You built a writing assistant app using the OpenAI API. At 800 users, you are spending $130/month on API costs (roughly 2M tokens/month). You expect to reach 3,000 users in 6 months, at which point the API bill will be $480/month.

The migration:

• Month 1: Export 600 input/output pairs from your OpenAI API logs as JSONL. Upload to Ertas, fine-tune Qwen 2.5 7B. 8 credits used.
• Month 2: Test the fine-tuned model against your test set. Quality matches GPT-3.5, close to GPT-4 for your specific task. Deploy on Hetzner VPS ($26/month).
• Month 3: Switch API endpoint in your app from OpenAI to local Ollama. Cancel (or reduce) OpenAI subscription.

The economics:

The monthly savings at 3,000 users: $439.50. At 10,000 users: $1,533.50. Builder pays for itself in the first month.

When Builder Is Not Enough

You have more than 3 active client models. If you are doing agency work and managing models for multiple clients simultaneously, you need the Pro plan ($50/month, 200 credits, 100 GB of model storage).

You need models larger than 14B. 14B covers most production fine-tuning use cases. If you are working with 30B-70B models or need frontier-class fine-tuning capabilities, you need Business or Enterprise.

You are deleting models to make room. Builder gives you 50 GB of model artifact storage and 2 GB for datasets. If you are removing exports you still want in order to free space for the next run, Pro takes those to 100 GB and 5 GB.

You have more than 100 credits of monthly training needs. If you are running many experiments or maintaining many models that need frequent retraining, Builder's credits may run short. Track your usage in the first month to assess.

You are waiting on the GPU queue. Builder runs behind Pro and Business when the queue is busy. If queue time is costing you delivery time, Pro's priority scheduling is the fix.

Free vs Builder: Is the Upgrade Worth It?

Free is genuinely useful for learning fine-tuning and testing the workflow. The wall most people hit is the daily refresh, and it is harder than it looks: the 5 credits refresh rather than accumulate, so a run costing more than 5 credits stays out of reach however many days you wait. A 12-credit run on a larger model is permanently unavailable on Free, not merely slow to get to.

Builder hands you 100 credits up front and rolls the unused ones over for a month. Combined with the higher GPU tier, that is what makes bigger models and longer training runs possible at all, rather than just faster.

Since July 2026 there is also a rung between the two. Lite is $10/mo and carries 40 credits, the same 14B model ceiling and the same GPU tier as Builder, with 25 GB of model storage and 1 GB of dataset storage. It exists for the case where Free's daily cap is the only thing blocking you and you are not yet running enough training to need Builder's 100 credits. See Fine-Tuning for Indie Devs: the $10 Lite plan for what it unlocks and where it runs out.

Builder is the right tier once you are training often enough that 40 credits stops being the constraint, or you want the larger 50 GB model and 2 GB dataset allowances to keep several models around at once.

Predictable Monthly Pricing

Builder is $25/month.

• Savings over 12 months: $240
• Savings over 24 months: $480
• Savings over 36 months: $720

You pay monthly, cancel anytime, refund guarantee. The lifetime price lock is the valuable part. Fine-tuning tools are getting more capable, not cheaper; locking in current pricing before launch is the same logic as any infrastructure pre-contract.

How to Know You Are Ready for Builder

You are ready for Builder if:

• You have a specific use case in mind (not "I want to try fine-tuning")
• You have or can generate 300+ training examples in JSONL format
• You are currently spending more than $40/month on API calls for a narrow task
• You have a VPS or are willing to spin one up for local inference

If you are not yet at this stage, use the Free tier until you are, or Lite at $10/mo if the daily credit cap is the only thing in your way. Builder is for when you have a real problem to solve and you are solving it often.

---

Further Reading

• Fine-Tune AI Without Code: How the fine-tuning workflow works from start to finish
• Indie Dev AI Model Costs 2026: The API cost cliff and what it costs at different user counts
• 7B Model Beats API Call: When a fine-tuned small model outperforms a cloud API
• Running AI Models Locally: Setting up Ollama for local inference
• Ertas Pro Plan Review: When to upgrade beyond Builder