# voyage-ai

Published articles for voyage-ai.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## How to Charge for RAG-as-a-Service

DevFeed: [How to Charge for RAG-as-a-Service](<https://devfeed.tech/articles/how-to-charge-for-rag-as-a-service-9721.md>)

Original publisher: [Read original article](<https://dodopayments.com/blogs/charge-rag-as-a-service/>)

Author: Ayush Agarwal

Published: 2026-03-26T00:00:00Z

Content type: tutorial

Language: en

Sources: [Dodo Payments Blog](<https://devfeed.tech/sources/dodo-payments-blog.md>)

Topics: [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [API](<https://devfeed.tech/topics/api.md>), [Database](<https://devfeed.tech/topics/database.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [Qdrant](<https://devfeed.tech/topics/qdrant.md>), [voyage-ai](<https://devfeed.tech/topics/voyage-ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [billing](<https://devfeed.tech/tags/billing.md>), [cost](<https://devfeed.tech/tags/cost.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [openai](<https://devfeed.tech/tags/openai.md>), [payments](<https://devfeed.tech/tags/payments.md>), [rag](<https://devfeed.tech/tags/rag.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [vector-database](<https://devfeed.tech/tags/vector-database.md>), [voyage-ai](<https://devfeed.tech/tags/voyage-ai.md>)

### AI overview

A tutorial on monetizing RAG-as-a-Service platforms. It explains how embedding generation, vector database retrieval, and LLM generation contribute to per-query costs, and discusses usage limits, credit-based pricing, per-query billing, and entitlement management using Dodo Payments.

### Source excerpt

Learn how to monetize Retrieval-Augmented Generation (RAG) services using per-query billing, credit packs, and usage-based models with Dodo Payments.

## Asymmetric Retrieval: Spend on Docs, Embed your Queries for Free

DevFeed: [Asymmetric Retrieval: Spend on Docs, Embed your Queries for Free](<https://devfeed.tech/articles/asymmetric-retrieval-spend-on-docs-embed-your-queries-for-free-12793.md>)

Original publisher: [Read original article](<https://blog.vespa.ai/asymmetric-retrieval-spend-on-docs-queries-for-free/>)

Author: thomas bjorncs

Published: 2026-03-10T00:00:00Z

Content type: article

Language: en

Sources: [Vespa Blog](<https://devfeed.tech/sources/vespa-blog.md>)

Topics: [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [API](<https://devfeed.tech/topics/api.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [container](<https://devfeed.tech/tags/container.md>), [cost](<https://devfeed.tech/tags/cost.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [genai](<https://devfeed.tech/tags/genai.md>), [latency](<https://devfeed.tech/tags/latency.md>), [local](<https://devfeed.tech/tags/local.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [quality](<https://devfeed.tech/tags/quality.md>), [rag](<https://devfeed.tech/tags/rag.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [vector](<https://devfeed.tech/tags/vector.md>), [voyage-ai](<https://devfeed.tech/tags/voyage-ai.md>)

### AI overview

The article presents asymmetric retrieval using Voyage AI models with Vespa. Documents are embedded once with a high-quality, API-based model, while frequently submitted queries are embedded with a small model running locally. Because the models share a compatible vector space, this approach can eliminate recurring query-embedding API costs, reduce latency, and allow query models to be upgraded independently.

### Source excerpt

Documents are embedded once -- worth the spend for maximum quality. Queries hit you on every request. This is what drives your cost at scale. Asymmetric retrieval with Voyage AI and Vespa. Real numbers, real config.

## Grab a Pint With Django-MongoDB-Backend, Voyage AI, and LangChain

DevFeed: [Grab a Pint With Django-MongoDB-Backend, Voyage AI, and LangChain](<https://devfeed.tech/articles/grab-a-pint-with-django-mongodb-backend-voyage-ai-and-langchain-21833.md>)

Original publisher: [Read original article](<https://www.thepolyglotdeveloper.com/blog/2025/04/grab-a-pint-with-django-mongodb-backend-voyage-ai-and-langchain/>)

Author: Anaiya Raisinghani

Published: 2025-04-19T21:55:13Z

Content type: tutorial

Language: en

Sources: [Nic Raboy](<https://devfeed.tech/sources/nic-raboy.md>)

Topics: [Django](<https://devfeed.tech/topics/django.md>), [MongoDB](<https://devfeed.tech/topics/mongodb.md>), [LangChain](<https://devfeed.tech/topics/langchain.md>), [voyage-ai](<https://devfeed.tech/topics/voyage-ai.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Python](<https://devfeed.tech/topics/python.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [Development](<https://devfeed.tech/topics/development.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [dev](<https://devfeed.tech/tags/dev.md>), [django](<https://devfeed.tech/tags/django.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [mongodb](<https://devfeed.tech/tags/mongodb.md>), [python](<https://devfeed.tech/tags/python.md>), [rag](<https://devfeed.tech/tags/rag.md>), [voyage-ai](<https://devfeed.tech/tags/voyage-ai.md>), [voyageai](<https://devfeed.tech/tags/voyageai.md>)

### AI overview

A tutorial on building a full-stack Dublin pub-finder application with Django, MongoDB, Voyage AI embeddings, and LangChain. It explains how these technologies support natural-language queries and generative AI or RAG applications.

### Source excerpt

DjangoCon Europe is just around the corner, and this year, it's in Dublin, Ireland. This means two things: I'm legally required to drink my first Guinness (yes, first ever!). I obviously had to build... The post Grab a Pint With Django-MongoDB-Backend, Voyage AI, and LangChain appeared first on DEV.