[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"academy-blogs-en-1-1-all-golang-the-series-ep153-intro-to-vector-databases-all--*":3,"academy-blog-translations-kaofe0gfax0kvyb":89},{"data":4,"page":75,"perPage":75,"totalItems":75,"totalPages":75},[5],{"alt":6,"collectionId":7,"collectionName":8,"content":9,"cover_image":10,"cover_image_path":11,"created":12,"created_by":13,"expand":14,"id":83,"keywords":84,"locale":55,"published_at":85,"scheduled_at":71,"school_blog":79,"short_description":86,"status":77,"title":87,"updated":88,"updated_by":13,"slug":80,"views":82},"Cover image for Golang The Series EP.153 titled Vector Databases 101 featuring Pinecone, Weaviate, and Milvus with Go code snippet illustration.","sclblg987654321","school_blog_translations","\u003Cp>Welcome back to EP.153! In our previous episode, we broke down the fundamentals of Vector Embeddings and looked at how to map human language into high-dimensional arrays of floating-point numbers (\u003Ccode>[]float32\u003C\u002Fcode>) using Go.\u003C\u002Fp>\u003Cp>But as you take your applications from prototype to production, you will inevitably hit a bottleneck. Imagine an enterprise Retrieval-Augmented Generation (RAG) system handling hundreds of thousands of document pages. If every page is converted into a vector, relying on a naive \u003Ccode>for\u003C\u002Fcode> loop to calculate similarity scores (like Cosine Similarity) in-memory creates a massive linear scan bottleneck. As your data scales, your CPU will spike, and search performance will tank.\u003C\u002Fp>\u003Cp>This is exactly why the modern AI infrastructure relies on \u003Cstrong>Vector Databases\u003C\u002Fstrong>—engines explicitly architected to store, index, and query high-dimensional vector spaces at scale. Today, we will unpack how they work and compare the top three heavyweights dominating the ecosystem from a Go developer’s perspective.\u003C\u002Fp>\u003Ch2>Why Traditional Databases Fail at Vector Search\u003C\u002Fh2>\u003Cp>Conventional databases like MySQL, PostgreSQL (without specialized extensions), or MongoDB are optimized for \u003Cstrong>Exact Match\u003C\u002Fstrong> or relational queries. They leverage structured B-Tree indices to find a specific ID or evaluate precise string matches.\u003C\u002Fp>\u003Cp>Vector data operates under entirely different rules. It consists of coordinates spanning hundreds or even thousands of dimensions. When querying vectors, we aren't looking for exact equality (\u003Ccode>=\u003C\u002Fcode>). Instead, we are looking for \u003Cstrong>Nearest Neighbors\u003C\u002Fstrong>—mathematically identifying which coordinates in our database are closest to our query vector.\u003C\u002Fp>\u003Cp>To execute these multidimensional calculations in milliseconds across millions of records, you need highly specialized indexing graph algorithms like \u003Cstrong>HNSW\u003C\u002Fstrong> (Hierarchical Navigable Small World) or \u003Cstrong>IVF\u003C\u002Fstrong> (Inverted File Index). Traditional B-Trees simply cannot keep up with this type of workload.\u003C\u002Fp>\u003Ch2>Deep Dive: The Top 3 Vector Databases for Go Devs\u003C\u002Fh2>\u003Cp>While the vector landscape is growing rapidly, these three platforms stand out for their production readiness and robust Go integration:\u003C\u002Fp>\u003Ch3>1. Pinecone (Fully Managed \u002F Cloud-Native)\u003C\u002Fh3>\u003Cp>Pinecone is a fully managed, cloud-native SaaS platform designed for speed and simplicity. It abstracts away the entire infrastructure layer, allowing you to spin up indexes and query them instantly using just an API key.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cp>\u003Cstrong>Pros:\u003C\u002Fstrong> Zero infrastructure overhead; seamless out-of-the-box scaling; ultra-low query latency.\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cstrong>Cons:\u003C\u002Fstrong> Proprietary and closed-source; costs scale directly with data volume and throughput; vendor lock-in with cloud deployment (cannot be run on-premise or in air-gapped environments).\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Ful>\u003Ch3>2. Weaviate (Open-Source \u002F AI-Native)\u003C\u002Fh3>\u003Cp>Weaviate is an open-source, AI-native database that holds a special place in our community: \u003Cstrong>its core engine is written natively in Go!\u003C\u002Fstrong> It uniquely couples vector search capabilities with traditional object storage within a single database architecture.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cp>\u003Cstrong>Pros:\u003C\u002Fstrong> Developer-friendly local setup via Docker; absolute data privacy; native modules for hybrid search and out-of-the-box text tokenization.\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cstrong>Cons:\u003C\u002Fstrong> You bear the responsibility of managing and scaling the infrastructure in production; can be highly memory-intensive (RAM) depending on your graph configuration.\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Ful>\u003Ch3>3. Milvus (Open-Source \u002F Enterprise-Grade)\u003C\u002Fh3>\u003Cp>Milvus is a distributed open-source powerhouse engineered from the ground up to handle massive, billion-scale vector datasets.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cp>\u003Cstrong>Pros:\u003C\u002Fstrong> Highly decoupled, distributed architecture that guarantees exceptional resilience, high availability (HA), and advanced sharding options.\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cstrong>Cons:\u003C\u002Fstrong> A steep learning curve for setup and orchestration; resource-heavy architecture that typically requires a Kubernetes cluster, making it absolute overkill for small to medium-sized projects.\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Feature Comparison Matrix\u003C\u002Fh2>\u003Ctable style=\"min-width: 226px;\">\u003Ccolgroup>\u003Ccol style=\"width: 151px;\">\u003Ccol style=\"min-width: 25px;\">\u003Ccol style=\"min-width: 25px;\">\u003Ccol style=\"min-width: 25px;\">\u003C\u002Fcolgroup>\u003Ctbody>\u003Ctr>\u003Ctd colspan=\"1\" rowspan=\"1\" colwidth=\"151\">\u003Cp>\u003Cstrong>Feature\u003C\u002Fstrong>\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>\u003Cstrong>Pinecone\u003C\u002Fstrong>\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>\u003Cstrong>Weaviate\u003C\u002Fstrong>\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>\u003Cstrong>Milvus\u003C\u002Fstrong>\u003C\u002Fp>\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd colspan=\"1\" rowspan=\"1\" colwidth=\"151\">\u003Cp>\u003Cstrong>License\u003C\u002Fstrong>\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Proprietary (SaaS)\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Open-Source\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Open-Source\u003C\u002Fp>\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd colspan=\"1\" rowspan=\"1\" colwidth=\"151\">\u003Cp>\u003Cstrong>Core Language\u003C\u002Fstrong>\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>C++ \u002F Rust\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>\u003Cstrong>Go (Golang)\u003C\u002Fstrong>\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Go \u002F C++ \u002F Python\u003C\u002Fp>\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd colspan=\"1\" rowspan=\"1\" colwidth=\"151\">\u003Cp>\u003Cstrong>Deployment\u003C\u002Fstrong>\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Managed Cloud\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Docker \u002F K8s \u002F Cloud\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Kubernetes \u002F Docker\u003C\u002Fp>\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd colspan=\"1\" rowspan=\"1\" colwidth=\"151\">\u003Cp>\u003Cstrong>Key Strength\u003C\u002Fstrong>\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Zero-config, rapid deployment\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Blazing fast, written in Go\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Built for billion-scale data\u003C\u002Fp>\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd colspan=\"1\" rowspan=\"1\" colwidth=\"151\">\u003Cp>\u003Cstrong>Best Used For\u003C\u002Fstrong>\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Startups, MVPs, and rapid prototyping\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Strict data privacy & hybrid search\u003C\u002Fp>\u003C\u002Ftd>\u003Ctd colspan=\"1\" rowspan=\"1\">\u003Cp>Enterprise-grade distributed platforms\u003C\u002Fp>\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>🛠️ Code Examples: Quick Start via Go SDK\u003C\u002Fh2>\u003Cp>Let’s look at how seamless it is to initialize a client and connect to these databases using their official Go packages.\u003C\u002Fp>\u003Ch3>Connecting to Weaviate in Go\u003C\u002Fh3>\u003Cp>Go\u003C\u002Fp>\u003Cpre>\u003Ccode>package main\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"log\"\n\n\t\"github.com\u002Fweaviate\u002Fweaviate-go-client\u002Fv4\u002Fweaviate\"\n)\n\nfunc main() {\n\tcfg := weaviate.Config{\n\t\tHost:   \"localhost:8080\",\n\t\tScheme: \"http\",\n\t}\n\n\tclient, err := weaviate.NewClient(cfg)\n\tif err != nil {\n\t\tlog.Fatalf(\"failed to initialize Weaviate client: %v\", err)\n\t}\n\n\t\u002F\u002F Verify the database is ready to accept queries\n\tisReady, err := client.Misc().ReadyChecker().Do(context.Background())\n\tif err != nil || !isReady {\n\t\tlog.Fatalf(\"Weaviate instance is not ready: %v\", err)\n\t}\n\n\tfmt.Println(\"🎉 Successfully connected to Weaviate!\")\n}\n\u003C\u002Fcode>\u003C\u002Fpre>\u003Ch3>Connecting to Pinecone in Go\u003C\u002Fh3>\u003Cp>Go\u003C\u002Fp>\u003Cpre>\u003Ccode>package main\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"log\"\n\n\t\"github.com\u002Fpinecone-io\u002Fpinecone-go-client\u002Fpinecone\"\n)\n\nfunc main() {\n\tctx := context.Background()\n\n\t\u002F\u002F Initialize the Pinecone client\n\tclient, err := pinecone.NewClient(pinecone.NewClientParams{\n\t\tApiKey: \"YOUR_PINECONE_API_KEY\",\n\t})\n\tif err != nil {\n\t\tlog.Fatalf(\"failed to initialize Pinecone client: %v\", err)\n\t}\n\n\t\u002F\u002F Retrieve all active indexes\n\tindexes, err := client.ListIndexes(ctx)\n\tif err != nil {\n\t\tlog.Fatalf(\"failed to fetch indexes: %v\", err)\n\t}\n\n\tfmt.Printf(\"🌲 Connected to Pinecone. Active Index Count: %d\\n\", len(indexes))\n}\n\u003C\u002Fcode>\u003C\u002Fpre>\u003Ch2>⚡ Daily Mission\u003C\u002Fh2>\u003Cp>Most communications with these databases rely heavily on gRPC and RESTful protocols—a distinction we deep-dived into back in EP.143.\u003C\u002Fp>\u003Cp>\u003Cstrong>Your assignment for today:\u003C\u002Fstrong> Head over to Weaviate's documentation, grab their official \u003Ccode>docker-compose.yml\u003C\u002Fcode>, spin it up locally with \u003Ccode>docker compose up -d\u003C\u002Fcode>, and run our Go SDK snippet above. Make sure your application establishes a clean handshake!\u003C\u002Fp>\u003Ch2>💡 FAQ: Frequently Asked Questions\u003C\u002Fh2>\u003Ch3>Can I just use PostgreSQL with \u003Ccode>pgvector\u003C\u002Fcode> instead of a dedicated Vector DB?\u003C\u002Fh3>\u003Cp>\u003Cstrong>Yes, you absolutely can.\u003C\u002Fstrong> Extensions like \u003Ccode>pgvector\u003C\u002Fcode> are fantastic for MVPs, small-scale production apps, or datasets under a few hundred thousand records. However, when you cross into millions of vectors or require complex real-time AI pipelines, dedicated vector databases deliver significantly better latency, throughput, and specialized scaling tools.\u003C\u002Fp>\u003Ch3>Should I use REST or gRPC endpoints when interacting with Vector DBs in Go?\u003C\u002Fh3>\u003Cp>Always default to \u003Cstrong>gRPC\u003C\u002Fstrong>. Embedding vectors are essentially large arrays of floating-point numbers (e.g., 1,536 dimensions). Parsing these via standard JSON over REST introduces massive CPU and bandwidth serialization overhead. The official Go SDKs for Pinecone, Weaviate, and Milvus leverage gRPC under the hood for data-heavy operations.\u003C\u002Fp>\u003Ch3>Since Weaviate is written in Go, does it automatically mean it's the fastest option for Go apps?\u003C\u002Fh3>\u003Cp>Not necessarily. Weaviate being built in Go is a massive win for \u003Cstrong>Gophers because we can easily dig into its source code, debug its architecture, or contribute to it\u003C\u002Fstrong>. However, actual search latency depends primarily on underlying graph index mechanics (like HNSW) and memory layouts. Competitors written in C++ or Rust offer equally blistering sub-millisecond query profiles.\u003C\u002Fp>\u003Ch3>What triggers \"Index Out of Memory\" errors in Vector DBs, and how do we prevent it?\u003C\u002Fh3>\u003Cp>Graph algorithms like HNSW require loading the entire graph index into RAM to ensure lightning-fast lookups. If your dataset scales rapidly, your memory footprint will explode, causing OOM crashes.\u003C\u002Fp>\u003Cp>\u003Cstrong>How to mitigate this:\u003C\u002Fstrong>\u003C\u002Fp>\u003Col>\u003Cli>\u003Cp>Switch to an \u003Cstrong>IVF (Inverted File)\u003C\u002Fstrong> index or enable \u003Cstrong>PQ (Product Quantization)\u003C\u002Fstrong> to compress vector dimensions.\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Adopt a vector engine that supports on-disk caching or decoupled storage layers (like Qdrant or Milvus).\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Fol>\u003Cdiv data-type=\"horizontalRule\">\u003Chr>\u003C\u002Fdiv>\u003Ch2>🎯 Summary: Choosing Your Weapon\u003C\u002Fh2>\u003Cul>\u003Cli>\u003Cp>If you need to \u003Cstrong>ship fast\u003C\u002Fstrong> and want to offload all infrastructure management ➡️ Go with \u003Cstrong>Pinecone\u003C\u002Fstrong>.\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>If you have strict \u003Cstrong>data privacy constraints\u003C\u002Fstrong>, want to self-host, or prefer an engine built natively in Go ➡️ Go with \u003Cstrong>Weaviate\u003C\u002Fstrong>.\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>If you are designing a \u003Cstrong>massively distributed enterprise system\u003C\u002Fstrong> handling billions of records ➡️ Go with \u003Cstrong>Milvus\u003C\u002Fstrong>.\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Ful>\u003Cp>\u003Cstrong>Up Next in EP.154: \u003C\u002Fstrong>Beyond the big three, there is a rising star in the vector space written in Rust that is capturing a lot of attention for its raw performance: Qdrant. In our next episode, \u003Cstrong>\"Go & Qdrant: High-Performance Vector Management,\"\u003C\u002Fstrong> we will get our hands dirty writing code to upsert and query raw vectors. Keep your Docker engines running, and I'll see you in the next one!\u003C\u002Fp>\u003Cp>\u003Cstrong>Follow Superdev Academy on all platforms:\u003C\u002Fstrong>\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cp>\u003Cstrong>🔵 Facebook: \u003C\u002Fstrong>\u003Ca target=\"_blank\" rel=\"noopener\" class=\"ng-star-inserted\" href=\"https:\u002F\u002Fwww.facebook.com\u002Fsuperdev.academy.th\">\u003Cstrong>Superdev Academy Thailand\u003C\u002Fstrong>\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cstrong>🎬 YouTube: \u003C\u002Fstrong>\u003Ca target=\"_blank\" rel=\"noopener\" class=\"ng-star-inserted\" href=\"https:\u002F\u002Fwww.youtube.com\u002F@SuperdevAcademy\">\u003Cstrong>Superdev Academy Channel\u003C\u002Fstrong>\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cstrong>📸 Instagram: \u003C\u002Fstrong>\u003Ca target=\"_blank\" rel=\"noopener\" class=\"ng-star-inserted\" href=\"https:\u002F\u002Fwww.instagram.com\u002Fsuperdevacademy\u002F\">\u003Cstrong>@superdevacademy\u003C\u002Fstrong>\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cstrong>🎬 TikTok: \u003C\u002Fstrong>\u003Ca target=\"_blank\" rel=\"noopener\" class=\"ng-star-inserted\" href=\"https:\u002F\u002Fwww.tiktok.com\u002F@superdevacademy?lang=th-TH\">\u003Cstrong>@superdevacademy\u003C\u002Fstrong>\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>\u003Cstrong>🌐 Website: \u003C\u002Fstrong>\u003Ca rel=\"noopener noreferrer\" href=\"https:\u002F\u002Fsuperdevacademy.com\">\u003Cstrong>superdevacademy.com\u003C\u002Fstrong>\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Ful>\u003Cp>\u003C\u002Fp>","26wex28wp7l4_59wg9092rf.png","https:\u002F\u002Ftwsme-r2.tumwebsme.com\u002Fsclblg987654321\u002Fv42u1edm27a0qmw\u002F26wex28wp7l4_59wg9092rf.png","2026-06-16 06:00:49.979Z","76qprkevbgfdps8",{"keywords":15,"locale":49,"school_blog":59},[16,23,27,32,36,40,45],{"collectionId":17,"collectionName":18,"created":19,"created_by":13,"id":20,"name":21,"updated":22,"updated_by":13},"sclkey987654321","school_keywords","2026-05-11 04:12:24.718Z","zo53ndb3rj4jxci","Vector Database","2026-06-07 06:49:11.382Z",{"collectionId":17,"collectionName":18,"created":24,"created_by":13,"id":25,"name":26,"updated":24,"updated_by":13},"2026-06-16 05:04:35.792Z","x748wj5xdkj9ovx","Pinecone Go",{"collectionId":17,"collectionName":18,"created":28,"created_by":13,"id":29,"name":30,"updated":31,"updated_by":13},"2026-03-04 08:20:14.253Z","ah6lvy4x8qe08l5","Golang","2026-06-07 06:45:08.193Z",{"collectionId":17,"collectionName":18,"created":33,"created_by":13,"id":34,"name":35,"updated":33,"updated_by":13},"2026-06-16 06:01:40.840Z","q64rf2b557boiw3","Weaviate",{"collectionId":17,"collectionName":18,"created":37,"created_by":13,"id":38,"name":39,"updated":37,"updated_by":13},"2026-06-16 06:01:47.271Z","un1w6rev8brv950","Milvus",{"collectionId":17,"collectionName":18,"created":41,"created_by":13,"id":42,"name":43,"updated":44,"updated_by":13},"2026-05-11 06:33:48.022Z","9kb92fayji137ra","Go SDK","2026-06-07 06:49:12.389Z",{"collectionId":17,"collectionName":18,"created":46,"created_by":13,"id":47,"name":48,"updated":46,"updated_by":13},"2026-06-16 06:01:56.832Z","unpr089rjmhpw6q","AI Backend",{"code":50,"collectionId":51,"collectionName":52,"created":53,"flag":54,"id":55,"is_default":56,"label":57,"updated":58},"en","pbc_1989393366","locales","2026-01-22 11:00:02.726Z","twemoji:flag-united-states","qv9c1llfov2d88z",false,"English","2026-04-10 15:42:46.825Z",{"category":60,"collectionId":61,"collectionName":62,"created":63,"expand":64,"id":79,"slug":80,"updated":81,"views":82},"wqxt7ag2gn7xcmk","pbc_2105096300","school_blogs","2026-06-16 05:54:47.852Z",{"category":65},{"blogIds":66,"collectionId":67,"collectionName":68,"created":69,"created_by":13,"id":60,"image":70,"image_alt":71,"image_path":72,"label":73,"name":74,"priority":75,"publish_at":76,"scheduled_at":71,"status":77,"updated":78,"updated_by":13},[],"sclcatblg987654321","school_category_blogs","2026-03-04 08:33:53.210Z","59ty92ns80w_15oc1implw.png","","https:\u002F\u002Ftwsme-r2.tumwebsme.com\u002Fsclcatblg987654321\u002Fwqxt7ag2gn7xcmk\u002F59ty92ns80w_15oc1implw.png",{"en":74,"th":74},"Golang The Series",1,"2026-03-16 04:39:38.440Z","published","2026-06-07 06:45:03.856Z","kaofe0gfax0kvyb","golang-the-series-ep153-intro-to-vector-databases","2026-06-22 05:13:07.596Z",111,"v42u1edm27a0qmw",[20,25,29,34,38,42,47],"2026-06-22 02:19:52.931Z","Why fall short? Discover Vector Databases—the next-generation storage solution—and take a deep dive into the top 3 heavyweights for backend developers: Pinecone, Weaviate, and Milvus.","Golang The Series EP.153: Vector Databases 101 - Getting to Know Pinecone, Weaviate, and Milvus","2026-06-22 02:59:05.697Z",{"th":80,"en":80}]