# Custom AI Model Pricing vs Off-the-Shelf AI: 2026 Guide

> Is custom AI cheaper than off-the-shelf APIs at scale? Sozee breaks down 2026 real costs, hidden fees & the exact break-even point. Compare now.

**Published:** 2026-03-19 | **Updated:** 2026-10-31 | **Author:** AI Growth Agent
**URL:** https://www.sozee.ai/resources/custom-ai-pricing-comparison-2026/
**Type:** post

**Categories:** AI Influencers & Virtual Models, Monetization & Pricing, Tools & Comparisons
**Tags:** pricing

![Custom AI Model Pricing vs Off-the-Shelf AI: 2026 Guide](https://www.sozee.ai/resources/wp-content/uploads/2026/08/1786134996963-864abce7c65e-1024x572.webp)

---

## Content

*Last updated: October 23, 2026*

## Key Takeaways

- Off-the-shelf AI pricing charges per token or per seat with almost no upfront cost. Custom model pricing front-loads build costs, then adds fixed hosting and ongoing MLOps, and the two cross over at a calculable volume.
- The market has three distinct tiers: off-the-shelf API or SaaS, customized fine-tuning or RAG on existing models, and building from scratch. The cost gap between tiers is measured in orders of magnitude.
- At 50,000 queries per month on Anthropic Sonnet 4.6 rates, the raw API bill runs roughly $825 per month. Training costs only $28.80, so serving contracts become the real decision factor.
- Hidden costs include vendor lock-in, per-token compounding, context accumulation, deprecation churn, and a consistency tax that pushes identity-locked content costs to $5.25–$12.50 per finished second.
- Sozee delivers a locked likeness from as few as three photos with no training wait. It replaces per-token unpredictability with a studio workflow of reusable environments, outfits, and objects for creators and agencies.

[Get Started With Sozee Today](https://app.sozee.ai/sign-up?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)

## What Is the Difference Between Custom AI Model Pricing and Off-the-Shelf AI Pricing?

The market sorts into three tiers, not two. Competitors including KrishaWeb, Techvoot, and the current AI Overview collapse “customize a model” and “train a foundation model from scratch” into one bucket, which produces cost ranges that are useless for budgeting. The separation matters because the cost gap between tiers is measured in orders of magnitude.

**Tier 1 — Off-the-Shelf API or SaaS.** You pay per token or per seat. Upfront cost stays near zero and time to value runs from hours to days. You own nothing and control nothing about the model.

**Tier 2 — Customized (Fine-Tune or RAG on an Existing Base Model).** You pay a one-time training or retrieval-pipeline cost, then ongoing serving and hosting. [Fine-tuning costs roughly 1–5% of training from scratch](https://fourfoldai.com/post/the-future-of-ai-fine-tuning-in-enterprise-environments), and [LoRA updates roughly 0.1% or less of trainable parameters (about a 10,000x reduction) while matching or exceeding full fine-tuning on most NLP benchmarks](https://appverticals.com/blog/fine-tuning-llm) (Hu et al., [arXiv 2106.09685](https://arxiv.org/abs/2106.09685)). This is the tier most business applications actually need.

**Tier 3 — Built From Scratch.** Pre-training on 100B+ tokens and thousands of GPU-days requires [$50M–$500M+ in compute](https://netguru.com/blog/ai-development-cost-guide). This tier is justified only for frontier-scale ambitions with genuinely novel data at massive volume.

[KrishaWeb’s own FAQ concedes that fine-tuning or RAG on an existing foundation model is the lowest-cost, fastest path for most business applications](https://krishaweb.com/blog/custom-ai-development-vs-off-the-shelf). The conflation of tiers 2 and 3 in most published guides is a framing error, not a pricing one.

The break-even rule of thumb is simple. Training cost is almost always a rounding error and the serving contract dominates. The key question becomes “how much does it cost to serve the fine-tuned model at my query volume?”

## 2026 Custom AI Development Cost vs Off-the-Shelf API Pricing (OpenAI, Anthropic, AWS)

This section uses current list prices to show how quickly per-token and per-image costs scale.

**OpenAI** ([OpenAI API pricing, accessed October 2026](https://openai.com/api/pricing)): GPT-Image-2.5 Sunburst and Flare at [$8.00 per 1M image-input tokens, $2.00 cached, $30.00 per 1M image-output tokens](https://openai.com/api/pricing). GPT-6 Luna at [$0.10 per 1M input tokens and $0.50 per 1M output tokens](https://openai.com/api/pricing). [Batch API at 50% off standard rates](https://openai.com/api/pricing) for asynchronous jobs. OpenAI bills image usage by tokens rather than publishing a universal per-image rate, so monthly image spend must be calculated as image-token volume multiplied by the token rate. At the $30 rate, 100M image-output tokens cost $3,000 before inputs, caching, or discounts. [OpenAI is retiring gpt-image-1 on October 23, 2026 and gpt-image-1.5 on December 1, 2026](https://developersdigest.tech/blog/image-video-generation-api-pricing-2026), which matters for anyone locking in production pricing.

**Anthropic** ([2026 pricing summary, accessed October 2026](https://g2.com/articles/ai-api-pricing)): Claude Haiku 4.5 at $1/$5 per 1M input/output tokens, Sonnet 4.6 at $3/$15, Opus 4.7 at $5/$25. At 100M input plus 100M output tokens per month, those rates imply approximately $600 for Haiku 4.5, $1,800 for Sonnet 4.6, and $3,000 for Opus 4.7 before caching, batch discounts, or platform fees.

**AWS Bedrock** ([AWS Bedrock pricing, accessed October 2026](https://aws.amazon.com/bedrock/pricing)): per-image billing for image models, for example $0.07 per Stable Image Inpaint operation. Text models bill per token. Batch and Flex tiers run at 50% off Standard, while the Priority tier carries a 75% premium. Provisioned throughput for customized models runs $21.18–$39.60 per hour on committed terms.

Linear scaling creates the core budget risk. A workload that looks affordable at 1M tokens per month becomes a material line item at 100M. Output tokens cost several times more than input tokens, so long-form content generation is especially sensitive to volume growth.

[A ten-turn conversation at 200 tokens per turn means turn 10 carries roughly 1,800 tokens of context, about 5× a 400-token estimate, so if the cost model assumed 400 tokens per call, actual cost at turn 10 is about 5× the estimate.](https://arjunjaggi.com/blog/ai-cost-and-roi)

If you need the full absolute ranges behind these per-token figures, our detailed guides walk through real project budgets.

- [Custom AI Model Costs 2026: $20K–$500K+ Real Pricing Guide](https://sozee.ai/resources/custom-ai-model-cost-2026/?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)
- [Custom AI Model Training Cost for Creators: 2026 Guide](https://sozee.ai/resources/custom-ai-model-training-costs/?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)

[Model Your Studio Costs With Sozee](https://app.sozee.ai/sign-up?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)

## How Much Does It Cost to Fine-Tune an Open-Source Model vs Train From Scratch?

The absolute cost ranges matter less than the ratio between them. Fine-tuning sits several orders of magnitude below from-scratch training, which drives the build-versus-buy decision.

A QLoRA run on a single rented GPU stays inexpensive. [LoRA or QLoRA fine-tuning of a 7B model on a single 24–48GB GPU takes roughly 2–8 GPU hours and costs $5–$30 in compute for a 5,000–10,000 example dataset, based on July 2026 GPU cloud rates.](https://appverticals.com/blog/fine-tuning-llm) A full fine-tune of a 70B model on multi-node clusters runs $5,000 and up. Pre-training a frontier model from scratch costs [$50M–$500M or more](https://netguru.com/blog/ai-development-cost-guide).

The real cost sits in people and process rather than raw compute. [Data preparation consumes 35–50% of a production fine-tuning budget and evaluation another 20–40%.](https://gmware.com/blog/llm-fine-tuning-cost) [Assembling a first high-quality dataset of 500–2,000 input/output pairs typically takes 300–500 person-hours, or $6,000–$25,000 of hidden cost before a single GPU spins up.](https://ai-tldr.dev/learn/fine-tuning/fine-tuning-basics/fine-tuning-cost-breakdown)

RAG build costs add another layer. [Amazon Bedrock Knowledge Bases vector store minimums range from roughly $50 per month (Aurora pgvector) to $350–$700 per month (OpenSearch Serverless OCU floor).](https://builder.aws.com/content/3JRIcyLJXmPLCBZBpvItQKlGSLW) The $350 per month OpenSearch minimum is the most common surprise bill teams encounter.

The QLoRA paper ([arXiv 2305.14314](https://arxiv.org/abs/2305.14314)) demonstrates fine-tuning a 65B parameter model on a single 48GB GPU while preserving full 16-bit task performance. The hardware constraint that once made large-model fine-tuning prohibitive is largely resolved.

## When Does a Custom AI Model Become Cheaper Than an API?

This section walks through a worked break-even example with labeled assumptions using verifiable published inputs only.

**Assumption Set:**

- 50,000 queries per month
- 1,500 input tokens and 800 output tokens per query
- Anthropic Sonnet 4.6 at $3/$15 per 1M input/output tokens ([2026 pricing summary](https://g2.com/articles/ai-api-pricing))
- A tuned endpoint at 1.5× base rate ([Google Vertex AI’s Gemini 3 policy, per Beri, September 2026](https://beri.net/article/fine-tuning-vs-rag-cost-serving-contract-break-even-2026))
- A $28.80 LoRA training run on Together AI at $0.48 per 1M training tokens for 19.2M tokens ([Beri, September 2026](https://beri.net/article/fine-tuning-vs-rag-cost-serving-contract-break-even-2026))

**Off-the-Shelf Monthly Cost:** 50,000 × 1,500 input tokens equals 75M input tokens, which costs $225. 50,000 × 800 output tokens equals 40M output tokens, which costs $600. Total: $825 per month.

**Training Cost:** $28.80, which is a rounding error against monthly serving costs.

**The Conclusion the Arithmetic Forces:** [Serving the same fine-tuned weights costs between $326 and $28,908 per month depending on the platform’s serving contract, roughly an 89× spread, while training the model costs about $30. “Fine-tuning is cheap to do and expensive to keep.”](https://beri.net/article/fine-tuning-vs-rag-cost-serving-contract-break-even-2026)

The serving contract becomes the decision, not the training run. Platform choice determines whether fine-tuning saves money or costs more than the raw API.

Published analyses give clear volume thresholds.

- [Above 100,000 daily queries, LoRA fine-tuning is optimal at roughly $1,800 per month versus RAG’s roughly $5,200.](https://builder.aws.com/content/3JRIcyLJXmPLCBZBpvItQKlGSLW)
- [The economics of LLM inference invert from per-token API pricing toward dedicated instances above roughly 50M tokens per month.](https://netguru.com/blog/ai-development-cost-guide)
- [Self-hosting open-weight models becomes cost-effective when daily token volume exceeds 8 to 30 million tokens.](https://agxntsix.ai/blog/open-weight-models-lower-enterprise-ai-licensing-costs)

[Estimate Your Break-Even With Sozee](https://app.sozee.ai/sign-up?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)

## Hidden Costs of Off-the-Shelf AI at Scale

**Vendor Lock-In and Migration Cost.** [Vendor lock-in, driven by data portability limits, closed APIs, uncapped renewal price increases, migration exit costs, and unclear IP ownership, is a hidden cost that a clean buy-versus-build analysis must price in.](https://appverticals.com/blog/build-vs-buy-software)

**Per-Seat and Per-Token Compounding.** [A tool that costs $50,000 per year at 100 users can easily cost $600,000 at 1,000 users if the inference architecture has not been optimized.](https://arjunjaggi.com/blog/ai-cost-and-roi)

**Context Accumulation.** [The ten-turn example above shows how context accumulation means actual token counts can run several times the per-call estimate.](https://arjunjaggi.com/blog/ai-cost-and-roi)

**Deprecation Churn.** [OpenAI’s retirement of gpt-image-1 and gpt-image-1.5, noted above, is one example.](https://developersdigest.tech/blog/image-video-generation-api-pricing-2026) Every deprecation cycle forces engineering response that a purchased product handles for you.

**The Consistency Tax.** For identity-locked content, the sticker price differs sharply from the real price. [Identity preservation adds a roughly 3× generations-per-usable-shot multiplier, pushing all-in cost for usable identity-locked footage to $5.25–$12.50 per finished second against a $0.15–$0.60 sticker.](https://ishchuk.eu/blog/the-economics-of-character-consistency-in-ai-video-at-scale) Facial drift, pose mismatch, and composition errors force retries on roughly two out of every three generations.

The custom side carries its own hidden costs, and they belong in the same analysis. [Ongoing maintenance runs about 15–25% of the build cost per year.](https://krishaweb.com/blog/generative-ai-for-business-practical-uses) [Most business models need retraining every 1 to 6 months.](https://tensoria.fr/en/blog/custom-model-training-guide) [Self-hosting carries 20–80 hours of one-time setup plus 5–15 hours of monthly maintenance.](https://presenc.ai/research/local-llm-vs-cloud-api-cost-2026)

## For Creators and Agencies, “Custom Model” Means Locked Likeness

Those hidden costs assume an enterprise buyer running a chatbot. Creators and agencies are buying something different, and the same cost logic produces a different answer for them.

A creator buyer needs the same face, the same body, and the same world at volume, without a re-roll, a different purchase from a chatbot with proprietary knowledge. Sozee delivers this as a studio workflow, not a training pipeline. Upload as few as three photos and Sozee reconstructs a likeness with no training and no wait, then holds that identity across every frame and set.

[](https://app.sozee.ai/sign-up?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)**Sozee AI Platform**

Around that locked likeness, you build reusable environments from up to four reference shots and reuse them indefinitely. An outfit library assembles a full look from one piece per category, and each set can include up to four objects. Photo Shoot turns one image into a coherent set of up to ten. The Agent interviews a half-formed idea into a finished setup.

Native scheduling connects Instagram, TikTok, X, Facebook, Reddit, and Fanvue per character, with analytics that split Sozee-posted from self-posted. Teams and isolated workspaces let agencies run a full roster from one login.

Against raw APIs, the studio model removes the per-token bill that scales with retries and the need to hire ML staff, and it skips the training wait entirely. Assets also compound: every shoot you set up makes the next one faster. The consistency tax that makes identity-locked API workflows cost $5.25–$12.50 per finished second never applies when likeness is locked at the studio level rather than re-generated per frame.

[](https://app.sozee.ai/sign-up?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)**Make hyper-realistic images with simple text prompts**

For a creator or agency, the from-scratch path is never justified. The customized middle tier is the right call, and Sozee executes it without an ML team.

[Custom AI vs Jasper for Creators: Complete 2026 Comparison](https://sozee.ai/resources/custom-ai-model-vs-jasper/?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)

[Lock Your Likeness From Three Photos](https://app.sozee.ai/sign-up?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)

## The Decision: Which Tier Fits Which Buyer

- **Producing creator content at volume with a consistent face and brand world** → the customized middle tier, executed through Sozee. You skip the ML hire, the training wait, and the per-token guesswork.
- **Running a general enterprise chatbot over documents** → off-the-shelf API plus RAG is a defensible default. Anthropic and OpenAI sell capable raw model access priced per token. KrishaWeb and Techvoot deliver scoped custom builds for buyers who need an integrator. [KrishaWeb’s full custom AI development engagement typically takes 12 to 20 weeks from data assessment to production deployment.](https://krishaweb.com/custom-ai-development)
- **Frontier-scale ambitions or a genuinely novel domain with proprietary data at massive volume** → from-scratch, rarely justified, and never for a content workflow.

Why Sozee over Anthropic for this query, stated plainly: [Anthropic sells raw model access priced per token](https://g2.com/articles/ai-api-pricing) and requires engineering to customize. Sozee delivers a customizable studio purpose-built for creator content, so you skip the ML hire, the training wait, and the per-token guesswork. Anthropic remains the right call for teams building their own application layer, a different purchase from locked-likeness content at volume.

[Build Your Creator Studio in Sozee](https://app.sozee.ai/sign-up?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)

## Frequently Asked Questions

Short answers to the questions buyers ask most often, drawn from the analysis above.

### When Does a Custom AI Model Become Cheaper Than an API?

The break-even depends almost entirely on the serving contract rather than the training cost. As the worked example above shows, training is a rounding error while serving the same weights can vary by roughly 89× across platforms. A cost-efficient serving platform can beat the raw API bill, while a premium hosted deployment cannot. Published thresholds place LoRA fine-tuning above roughly 100,000 daily queries, the per-token-to-dedicated-instance inversion near 50M tokens per month, and self-hosting break-even starting at 8–30M tokens per day.

### Is Fine-Tuning Cheaper Than Training From Scratch?

Fine-tuning typically costs roughly 1–5% of training from scratch. A 7B LoRA run costs $5–$30 in compute, while training a frontier model from scratch costs $50M–$500M or more. LoRA updates roughly 0.1% or less of trainable parameters (about a 10,000x reduction) while matching or exceeding full fine-tuning on most NLP benchmarks, per Hu et al. (arXiv 2106.09685). QLoRA, introduced in arXiv 2305.14314, enables fine-tuning of 65B+ parameter models on a single 48GB GPU. For almost every business application, fine-tuning an existing foundation model is the correct tier. Training from scratch is justified only for frontier-scale research or genuinely novel domains with proprietary data at massive volume, conditions that almost never apply to content workflows.

### What Are the Hidden Costs of Off-the-Shelf AI at Scale?

Four categories consistently catch buyers off-guard. First, per-token compounding: a workload that costs a few hundred dollars at modest volume scales linearly, and context accumulation means actual token counts often run several times the per-call estimate by turn 10 of a conversation. Second, deprecation churn: providers retire models on short notice, as with the gpt-image retirements noted above, which forces engineering response cycles that a purchased product handles internally. Third, vendor lock-in: data portability limits, closed APIs, and uncapped renewal price increases add switching costs that rarely appear in initial estimates. Fourth, the consistency tax: for identity-locked content, retries on failed generations add the roughly 3× multiplier described above, which is why identity-locked footage costs far more per finished second than the sticker price suggests. On the custom side, mirror costs include 15–25% of build cost per year in MLOps overhead, retraining every 1–6 months, and 20–80 hours of one-time setup plus 5–15 hours of monthly maintenance for self-hosting.

### Does a Creator Need an ML Team to Customize AI?

Likeness and content consistency do not require an ML team. Three photos and a studio workflow replace the training pipeline entirely. Sozee reconstructs a likeness with no training, no wait, and no technical setup, and identity locks from the first generation and holds across every frame, set, and platform. An ML team is required only for the fine-tune and from-scratch tiers. Fine-tuning a 7B model requires dataset curation, training infrastructure, evaluation harnesses, and ongoing retraining as the model drifts. For a creator whose requirement is consistent face, consistent world, and consistent volume, that tier does not fit the job.

## Conclusion: The Verdict on Custom AI Model Pricing vs Off the Shelf

Sozee is the best custom AI solution for creators and agencies who need consistent, high-volume content because it locks a likeness from as few as three photos and replaces per-token unpredictability with a reusable studio, so the consistency tax described above never applies.

The budget logic closes simply. Rent tokens to prototype. Customize the middle tier to produce. Skip from-scratch unless both volume and moat justify it. For creator content, the customized middle tier is the right call, and the studio model turns every asset you build into compounding leverage. Every shoot you set up makes the next one faster. That is the economics of a studio, not a slot machine.

[Start Your First Sozee Shoot Today](https://app.sozee.ai/sign-up?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)

## What to Read Next

- [Custom AI Model Costs 2026: $20K-$500K+ Real Pricing Guide](https://sozee.ai/resources/custom-ai-model-cost-2026/?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)
- [Custom AI Model Training Cost For Creators: 2026 Guide](https://sozee.ai/resources/custom-ai-model-training-costs/?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)
- [Open Source Custom AI Models: Complete 2026 Guide](https://sozee.ai/resources/open-source-custom-ai-model/?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)
- [Best Custom AI Model Builders 2026: Top No-Code & Dev Tools](https://sozee.ai/resources/best-ai-model-builders-2026/?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)
- [AI Content Production Cost Comparison: Real Bills in 2026](https://sozee.ai/resources/cost-comparison-ai-content-tools/?utm_term=custom-ai-pricing-comparison-2026&utm_campaign=ai_growth_agent)

---

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      - **Text:** The break-even depends almost entirely on the serving contract rather than the training cost. As the worked example above shows, training is a rounding error while serving the same weights can vary by roughly 89× across platforms. A cost-efficient serving platform can beat the raw API bill, while a premium hosted deployment cannot. Published thresholds place LoRA fine-tuning above roughly 100,000 daily queries, the per-token-to-dedicated-instance inversion near 50M tokens per month, and self-hosting break-even starting at 8–30M tokens per day.
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    - **Name:** Is Fine-Tuning Cheaper Than Training From Scratch?
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      - **Text:** Fine-tuning typically costs roughly 1–5% of training from scratch. A 7B LoRA run costs $5–$30 in compute, while training a frontier model from scratch costs $50M–$500M or more. LoRA updates roughly 0.1% or less of trainable parameters (about a 10,000x reduction) while matching or exceeding full fine-tuning on most NLP benchmarks, per Hu et al. (arXiv 2106.09685). QLoRA, introduced in arXiv 2305.14314, enables fine-tuning of 65B+ parameter models on a single 48GB GPU. For almost every business application, fine-tuning an existing foundation model is the correct tier. Training from scratch is justified only for frontier-scale research or genuinely novel domains with proprietary data at massive volume, conditions that almost never apply to content workflows.
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      - **Text:** Four categories consistently catch buyers off-guard. First, per-token compounding: a workload that costs a few hundred dollars at modest volume scales linearly, and context accumulation means actual token counts often run several times the per-call estimate by turn 10 of a conversation. Second, deprecation churn: providers retire models on short notice, as with the gpt-image retirements noted above, which forces engineering response cycles that a purchased product handles internally. Third, vendor lock-in: data portability limits, closed APIs, and uncapped renewal price increases add switching costs that rarely appear in initial estimates. Fourth, the consistency tax: for identity-locked content, retries on failed generations add the roughly 3× multiplier described above, which is why identity-locked footage costs far more per finished second than the sticker price suggests. On the custom side, mirror costs include 15–25% of build cost per year in MLOps overhead, retraining every 1–6 months, and 20–80 hours of one-time setup plus 5–15 hours of monthly maintenance for self-hosting.
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      - **Text:** Likeness and content consistency do not require an ML team. Three photos and a studio workflow replace the training pipeline entirely. Sozee reconstructs a likeness with no training, no wait, and no technical setup, and identity locks from the first generation and holds across every frame, set, and platform. An ML team is required only for the fine-tune and from-scratch tiers. Fine-tuning a 7B model requires dataset curation, training infrastructure, evaluation harnesses, and ongoing retraining as the model drifts. For a creator whose requirement is consistent face, consistent world, and consistent volume, that tier does not fit the job.

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---

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## Citations

- [Best Eromify Alternatives for AI Character Consistency](https://www.sozee.ai/resources/best-eromify-alternatives-ai-personas/)
- [Eromify Alternatives For AI Model Creators: 9 Ranked Picks](https://www.sozee.ai/resources/best-eromify-alternatives-ai-models/)
- [RenderNet AI vs MakeInfluencer for AI Creators 2027](https://www.sozee.ai/resources/rendernet-ai-vs-makeinfluencer/)
- [OpenArt vs RenderNet AI for AI Influencers (2027)](https://www.sozee.ai/resources/openart-vs-rendernet-ai-influencers/)
- [Sozee vs Higgsfield For AI Influencer Content: Which Wins?](https://www.sozee.ai/resources/sozee-vs-higgsfield-ai-influencer/)

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