Cost and Timeline for RAG-Based Product Search Implementation in Medium-to-Large E-commerce

No doubt more and more innovators are trying to adopt the power of LLM to reshape E-commerce, especially for the scenario of product discovery — the gateway to revenue. For many CTO/CIO/CPOs, RAG-based search is the first choice. Before making the decision to deploy this technique, we need to ask: how much and how long does a RAG-based E-commerce search cost? Here I'll walk you through this estimation journey.

Project Goal

As for a Medium-to-Large E-commerce, assume there're at least 10,000 SKUs. The primary objective is to enable users to articulate their product needs and intentions in natural language, receiving a curated list of the most relevant product matches as a result.

Breakdowns:

• User Interface (UI) & User Experience (UX): The solution will be a user-facing chatbot interface integrated into the platform, designed to facilitate a seamless, conversational search experience.

• Input Method: The chatbot will exclusively support text-based communication. Users will interact with the system by typing plain text queries. Voice, image uploads, or other media inputs are out of scope for this project.

• Output: The system's output will be a dynamically generated, ranked list of products from the 10,000 SKU catalog that best align with the user's natural language query.

• Technique adopted: RAG (Retrieval-Augmented Generation).

Project Scope

The scope of a RAG-based e-commerce product search could be seen here: A Practical Guidance for Building RAG-Powered Conversational Search.

Project Cost & Timeline

The cost could be devided into one-time development & set up cost, and the ongoing usage & maintenance cost.

One-time development & set up cost: $200,000 and 3.5 months

1. Data preparation: $30,000 in 2.5 months.

   Successful implementation extends beyond development to encompass robust product data governance. For a catalog of 10,000 SKUs, the timeline and cost depend heavily on existing metadata quality. Based on the assumption that most products have structured metadata requiring updates, with each SKU taking approximately 5 minutes to process (maybe too optimistic to give 5 minutes only), two merchandisers would need roughly 2.5 months to complete the data preparation phase. This translates to an estimated labor cost of $30,000, calculated at $6,000 per merchandiser per month.

2. Development cost: $170,0000 with 3.5 months.

• Project Planning & Design: $20,000 over 1 month

  A minimum of 1 month is required to complete comprehensive planning and design, encompassing product design, solution architecture, and project activity planning. This phase requires 1 product designer and 1 solution architect, with an estimated cost of $20,000 over 1 month (based on $10,000 per month per specialist).

• IT Development: $150,000 over 2.5 months

  The development process consists of two parallel components: basic feature development and RAG system implementation. The feature development requires 3 engineers (1 frontend, 2 backend) working for 1.5 months, while RAG development needs 1 ML/AI engineer for the same duration. Following development, an additional 1-month testing and optimization phase involves 2 dedicated testers. The total development cost is $150,000 over 2.5 months (based on $10,000 per month per team member, with 6 total resources across the timeline).

Since data preparation and IT development can run concurrently, the total timeline is 3.5 months.

Ongoing cost: $5,817 per month.

1. Iterative development cost: $3,542 per month

   Typically, it accounts for 15~25% of the initial development cost as maintenance and iteration cost. For RAG-based search, there is a high-demand for iteration, so let's make it 25% here. Monthly cost is $3,542($170,000*0.25/12).

2. LLM usage cost: roughly $2,275 per month.

   Assume there are 100,000 search queries with each query 100 tokens. Supposing there are 10 products as retrieval from vector database, with each product 500 tokens as product description. The output is 100 token with each product

   For each query:

   • LLM input token: 5100 tokens.(510M tokens per month)

   • LLM output token: 1000 tokens.(100M tokens per month) The processing price for GPT-4o is $2.5 for 1M Input tokens and $10 for 1M Output tokens. So the total monthly LLM expense is $2,275 (510*2.5+100*10)

3. Monthly cost for RAG: Could be negligible (The attached image shows a cost of only $6 from Pinecone, a popular RAG infrastructure provider.)

Conclusion

For a medium-to-large e-commerce platform managing 10,000 SKUs and processing 100,000 monthly search queries, implementing a custom RAG-based product search system requires a substantial but strategically justified investment.

Initial Investment: $200,000 over 3.5 months

• Development and setup: $170,000

• Data preparation and governance: $30,000

Ongoing Operations: $5,817 per month

• Iterative development and maintenance: $3,542

• LLM processing costs: $2,275

• RAG infrastructure: Minimal (under $10)

Ready to Transform Your Product Search?

Building a custom RAG solution requires significant time and resources. Evosmarter offers a mature conversational search solution specifically designed for e-commerce platforms, delivering the same advanced capabilities at just 1/10th the one-time setup cost with lower monthly expenses than building in-house.

Skip the $200,000 development investment and 3.5-month timeline—get enterprise-grade conversational search deployed in days, not months.

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