The Infrastructure Behind a Billion Voices: How a Leading LLM Provider and Zilliz Are Powering a Multilingual AI Future in India
<300 ms
End-to-end latency at 40-50 QPS across tens of millions of vectors.
3,500+ Dimensions
Tens of millions of high-dimensional vectors served efficiently for multilingual semantic retrieval.
Automatic Resharding
Continuous data and load rebalancing across nodes as the system scales.
Balanced Tradeoffs
A well-balanced outcome across performance, recall, and cost.
About the Company
The customer is a leading generative AI company in India focused on building multilingual language technologies that support the country’s rich linguistic diversity. Their platform spans large language models, speech recognition, speech synthesis, translation, and developer-friendly APIs that power conversational agents and voice-first applications across both public and private sectors. By combining locally trained models with retrieval grounded in trusted knowledge sources, they deliver AI systems capable of producing accurate, culturally aligned responses at a national scale.
As their multilingual RAG system matured, the team encountered growing infrastructure bottlenecks—from high-dimensional embeddings and uneven workload distribution to rising operational overhead. Zilliz Cloud resolved these challenges with automated scaling, hybrid search, and high-performance retrieval that eliminated manual maintenance and restored the accuracy required for large-scale multilingual AI. Today, this partnership serves as a critical foundation for the company’s mission to deliver grounded, trustworthy AI experiences to users nationwide.
The Challenge of Grounding Truth at Scale
Building AI for a linguistically diverse population introduces complexities that most large language models are not designed to handle. While many models perform well in English and a handful of high-resource languages, they often struggle with languages that are less represented in global training datasets. This mismatch can lead to hallucinations, misinterpretations, and inconsistent responses—problems that directly impact user trust, especially when people expect accurate, grounded answers in their local language.
To address this, the customer developed a Wikipedia-powered Retrieval-Augmented Generation (RAG) system that enriches their models with real-time factual context. Instead of relying solely on model weights, every query retrieves the most relevant information from a curated knowledge base before generating a response. This dramatically reduces hallucinations and improves factual accuracy, particularly for culturally specific or regionally nuanced questions.
However, scaling this system quickly revealed deeper infrastructure challenges. The team needed to process the entire Wikipedia corpus, transforming millions of articles into high-dimensional vectors—tens of millions in total, each with thousands of dimensions—that had to be searched in well under 300 milliseconds. The volume, dimensionality, and real-time latency requirements placed enormous pressure on their underlying vector database.
Their initial setup, built on Qdrant and some other open-source tools, worked but came with significant operational overhead. Scaling required manual resharding that could take hours, and workload distribution across nodes was inconsistent—often leaving one machine overloaded while others sat idle. For a fast-moving company building foundational AI infrastructure, these inefficiencies went beyond inconvenience; they directly slowed down their product roadmap and ability to ship reliable AI services.
This made it clear: they needed a far more scalable, resilient vector database that could support rapid growth and production-grade RAG systems without constant operational intervention.
The Journey to Zilliz Cloud Vector Database
As the limitations of their existing vector infrastructure became increasingly apparent, the team began evaluating scalable, reliable alternatives. Their criteria focused on automatic sharding, effortless horizontal scaling, hybrid search capabilities to support multilingual workloads, and enterprise-level reliability. They tested multiple platforms against their production requirements, including 40–50 QPS with sub-300 ms latency for vectors exceeding 3,500 dimensions.
Zilliz Cloud stood out for its ability to distribute workloads evenly, eliminate manual resharding, and support rapid scaling without operational overhead. The platform’s hybrid vector and full-text search capabilities were especially valuable for their multilingual retrieval scenarios, where accuracy and contextual understanding directly impact downstream model performance.
The team noted that Zilliz Cloud not only met their technical benchmarks but also provided the reliability and scalability required to support a fast-growing RAG system serving millions of users. It offered the foundation they needed to accelerate their roadmap while staying focused on building high-quality multilingual AI experiences.
Implementation Details
Zilliz Cloud became the backbone of the customer’s RAG infrastructure, powering multilingual AI through high-performance vector search. Their system operates on embeddings with over 3,500 dimensions—much higher than typical RAG implementations—after extensive experiments showed that lower-dimensional vectors were insufficient for capturing the linguistic complexity required in their use cases.
Their performance requirements were demanding: sustaining 40–50 QPS with end-to-end latency under 300 ms, all while maintaining cost efficiency. The previous setup, based on an open-source vector database, struggled to meet these benchmarks. Despite running a multi-node cluster, only a single node handled most of the workload due to uneven data distribution, creating significant operational bottlenecks and scaling challenges.
With Zilliz Cloud, the improvement was immediate. Data segments are automatically balanced across nodes, eliminating the manual 2–3 hour resharding process that had previously slowed down engineering operations. The platform’s hybrid search capabilities were especially useful, allowing them to blend semantic similarity with keyword-based matching to deliver more accurate retrieval.
Zilliz Cloud also helped them achieve a better balance between performance, recall, and cost. Their earlier solution had relied on aggressive quantization to reduce expenses, but the tradeoff in recall degraded the quality of search results—an unacceptable compromise for their multilingual applications. Zilliz Cloud’s Cardinal search engine restored the recall levels they needed while staying within budgetary limits.
Looking ahead, the team is preparing to adopt Zilliz Cloud’s RaBitQ feature, an advanced 1-bit quantization technology designed to deliver superior performance and cost efficiency without sacrificing recall. This gives them a clear path to scale their RAG workloads even further without repeatedly restructuring their infrastructure.
Most importantly, the move to Zilliz Cloud freed their engineering team from constant database maintenance, allowing them to redirect their efforts toward advancing model quality and system capabilities.
Impact Beyond Technology: Making AI Truly Indian
Today, the customer’s RAG-powered system supports multilingual AI agents that users can access through voice or messaging applications. Their API allows developers to enable or disable factual grounding based on the needs of each use case—balancing speed and accuracy with a simple configuration toggle. When grounding is enabled, users receive responses supported by a trusted knowledge source, improving reliability across a wide range of topics and languages.
This capability has a meaningful real-world impact. People across different regions can ask questions in their preferred language and receive accurate, contextually appropriate answers within seconds. It represents a step toward making AI accessible to communities that have historically been underserved by mainstream models, and it highlights how the right vector database infrastructure can empower AI systems to deliver trustworthy results at scale.
For the customer, Zilliz Cloud has become a core component of this vision—helping them deliver grounded, reliable AI experiences without compromising performance or agility. By removing operational burdens and improving retrieval accuracy, Zilliz Cloud enabled their team to focus on what matters most: building AI systems that truly reflect the linguistic and cultural diversity of their users.