123RF用Zilliz Cloud省了一半成本
股票媒体平台123RF拥有2亿+视觉资产，从关键词搜索转向语义搜索。迁移至Zilliz Cloud后，成本降低50%，查询延迟从100ms降至30-50ms。Zilliz Cloud支持CLIP生成稠密向量、动态扩缩容、解耦数据写入与在线查询、自定义排序逻辑。该方案帮助平台在保证检索质量的同时控制运营成本。
𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗮 𝘀𝘁𝗼𝗰𝗸 𝗺𝗲𝗱𝗶𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝘄𝗶𝘁𝗵 𝟮𝟬𝟬𝗠+ 𝘃𝗶𝘀𝘂𝗮𝗹 ...
𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗮 𝘀𝘁𝗼𝗰𝗸 𝗺𝗲𝗱𝗶𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝘄𝗶𝘁𝗵 𝟮𝟬𝟬𝗠+ 𝘃𝗶𝘀𝘂𝗮𝗹 𝗮𝘀𝘀𝗲𝘁𝘀 𝗿𝗲𝗱𝘂𝗰𝗲 𝗰𝗼𝘀𝘁𝘀 𝘄𝗵𝗶𝗹𝗲 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗶𝗻𝗴 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗾𝘂𝗮𝗹𝗶𝘁𝘆? Moving from keyword search to semantic search changes the infrastructure requirements behind image retrieval. At a smaller scale, that can be manageable. But for a stock media platform like 𝟭𝟮𝟯𝗥𝗙, the problem changes quickly once the asset library grows into the hundreds of millions. Latency, cost, indexing stability, and operational complexity all become harder to control. After migrating to 𝗭𝗶𝗹𝗹𝗶𝘇 𝗖𝗹𝗼𝘂𝗱, 123RF 𝗰𝘂𝘁 𝗰𝗼𝘀𝘁𝘀 𝗯𝘆 𝟱𝟬% and brought query latency down from around 𝟭𝟬𝟬𝗺𝘀 𝘁𝗼 𝟯𝟬–𝟱𝟬𝗺𝘀. For a stock content platform, several capabilities mattered most: 𝟭. 𝗦𝘁𝗿𝗼𝗻𝗴 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗳𝗼𝗿 𝗱𝗲𝗻𝘀𝗲 𝘃𝗲𝗰𝘁𝗼𝗿𝘀 - Search quality in visual content is not static. It needs to evolve with new content types, modalities, and user intent. - Zilliz Cloud helps 123RF store, index, and retrieve CLIP-generated dense vectors at scale, capturing semantic relationships that keyword search cannot easily represent. 𝟮. 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 - Stock content platforms often see clear traffic peaks and valleys. - With Zilliz Cloud, clusters can scale up or down based on expected query load, controlling costs during quieter periods while keeping response times stable. 𝟯. 𝗗𝗲𝗰𝗼𝘂𝗽𝗹𝗲𝗱 𝗱𝗮𝘁𝗮 𝗶𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗶𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗼𝗻𝗹𝗶𝗻𝗲 𝘀𝗲𝗮𝗿𝗰𝗵 - Content platforms need to keep adding new assets without disrupting live search. - Zilliz Cloud decouples data ingestion from online queries, allowing new assets to be written into the system steadily and quickly without downtime. 𝟰. 𝗖𝘂𝘀𝘁𝗼𝗺 𝗿𝗮𝗻𝗸𝗶𝗻𝗴 𝗹𝗼𝗴𝗶𝗰 - Search for stock media cannot rely on vector similarity alone. Business-specific ranking rules can also affect which results should appear first. - Zilliz Cloud supports custom reranking logic, helping search results better align with platform goals. At this scale, the real challenge of production semantic search is keeping search fast, stable, and easy to keep improving as the system grows. 💬 0 🔄 0 ❤️ 0 👀 9 ⚡