Document Processing · Enterprise Knowledge Search
Fireworks AI needed a vector database that could supply grounded enterprise context to generative AI models, reduce hallucinations, support customer-controlled deployments, and avoid forcing sensitive data into disconnected third-party vector systems.
Reported impact
Using MongoDB Atlas with Fireworks AI improves model output quality, reduces latency, lowers cost, and gives enterprise customers a practical way to deploy secure RAG, classification, and extraction workloads while keeping data in their own environments when necessary.
Fireworks AI uses MongoDB Atlas as the vector database foundation alongside its inference platform so enterprise customers can add grounded retrieval to generative AI deployments without moving sensitive data into separate systems. Document collections and other enterprise content are ingested into Atlas, embedded and indexed for MongoDB Atlas Vector Search, and then retrieved at query time to supply context to Fireworks AI-hosted or self-deployed models. This architecture supports core workloads such as conversational RAG, semantic classification, image-to-structured-data extraction, and alert intelligence while preserving data sovereignty because deployments can run inside customer environments. By pairing Atlas's native vector retrieval with Fireworks AI's high-throughput model-serving stack, the company offers a faster, lower-latency, and lower-cost path to enterprise-ready AI applications than disconnected inference and data layers.
Enterprise documents and other customer data are ingested into MongoDB Atlas, indexed for vector search, retrieved at query time through MongoDB Atlas Vector Search, and supplied as context to Fireworks AI models running through hosted APIs or inside customer-controlled deployment environments.
"By partnering with MongoDB Atlas, an operational database with native vector search capabilities, Fireworks AI addresses one of AI adoption’s toughest challenges."
"Fireworks AI can process 66 billion tokens daily while achieving four times faster inference speed and eight times higher throughput and scale than competing platforms."
"MongoDB Atlas supports context retrieval from enterprise data, reducing LLM hallucinations while maintaining performance."
"This collaboration enables the secure integration of enterprise data with AI models, facilitating retrieval-augmented generation (RAG) while maintaining data sovereignty."
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Scoring methodology
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Traceability
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The company profile states 'Operating headquarters' in 'Redwood City, CA, US'.
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