The moment you decide to build a vector database from scratch, you cross a threshold most developers never see coming. You are not just writing code. You are making a choice that impacts your entire AI infrastructure for years. This article breaks down the hidden complexities and explains why many teams regret this decision.
Why Everyone Thinks Building a Vector Database is Simple
The appeal is magnetic. Search for “vector database tutorial” and you find code snippets that make it look trivial—store embeddings. Compare vectors. Return the most similar ones. A weekend project. A blog post idea. A portfolio piece.
The reality is brutally different. Most tutorials show the happy path and hide simplifications that only work with small data. The moment you scale beyond a few thousand vectors, the entire foundation cracks.
Engineers build an MVP in two weeks that works perfectly on their laptops. Then they try to scale it to real workloads, and suddenly they are debugging memory leaks at 3 AM and questioning every architectural decision they made six months earlier.
The Four Layers You Did Not Know You Needed
Building a vector database is four distinct layers that must work together seamlessly. Fail at any one, and the entire system becomes useless in production.
The first layer is data ingestion and normalisation. Raw embeddings come from different sources with different sizes and formats. OpenAI embeddings have different dimensions from Cohere embeddings. Some vectors are float32, others are int8. You need robust pipelines that handle format conversion, dimension validation, and quality checks. This is where most homemade systems fail silently because you think everything is correct until your similarity search results suddenly stop making sense.
The second layer is the indexing strategy. You cannot compare every query vector against every stored vector if you have millions or billions. You need an index structure like HNSW, IVF, or LSH. Each has different memory footprints, query speeds, and accuracy trade-offs. HNSW is faster but requires more memory. IVF is more memory-efficient but slower. Most teams pick one based on a tutorial, only to discover six months in production that it does not scale with their actual access patterns.
The third layer is distributed storage and replication. Your laptop has one disk and enough RAM for thousands of vectors. Production has millions spread across multiple data centers. You need to shard your data across multiple machines and replicate it for fault tolerance. You need consistency guarantees so a user sees the same results whether they hit server A, B, or C. This is where most homemade systems collapse because building a distributed system requires an entirely different skill set.
The fourth layer is operational visibility. You shipped your database, and it runs in production with real users. You need monitoring to detect when query latency degrades unexpectedly. You need to be alerted when replication falls out of sync. You need debugging tools to understand why a specific search returned unexpected results. You need audit logs for compliance. These are tedious, unglamorous, and absolutely critical to production success.
The Mathematics Nobody Prepares You For
Vector databases live at the intersection of linear algebra, probability theory, and computer science. Most tutorials skip the math because it is hard. But ignoring it costs you dearly later.
Start with similarity metrics. How do you actually compare two vectors? Most developers know Euclidean distance. They do not know why it is often the wrong choice. In high-dimensional spaces with normalised embeddings, cosine similarity dramatically outperforms Euclidean distance. In very high dimensions, distance itself becomes almost meaningless because all vectors end up roughly the same distance from each other. This is the curse of dimensionality, and it catches every team building their first vector database.
Then there is quantisation. If you have a billion vectors with 1536 dimensions each, you need 6 terabytes of memory if you store them as float32. You cannot do this. You need to compress using quantisation techniques that reduce vectors to 8-bit or even 1-bit representations. You lose precision but fit your entire index in RAM.
You need to understand how much accuracy to sacrifice for your use case. Lose too much and your search results become garbage. Lose too little and you are barely compressing at all.
Then there is the approximate nearest neighbour problem. Finding the single closest vector is straightforward. Finding top-K closest vectors in high dimensions is entirely different. Exact algorithms become exponentially slower as dimensions increase. You must use approximate algorithms like HNSW or IVF, but they make different guarantees that affect your results.
The Hidden Performance Cliffs
You build your database and test it thoroughly. It works great. You run load tests, and it handles 1000 queries per second. You deploy to production. Everything crashes under 100 queries per second on real data.
This happens because your load test used random queries against random vectors, which is nothing like real usage. Real queries have patterns. Real vectors have geographic clustering. These patterns destroy your carefully tuned indexes.
The first cliff is memory pressure. Your index requires the entire vector set in memory for fast access. You cannot page to disk without destroying performance. When your memory limit is reached, your system either garbage collects with unpredictable latency or crashes. You need to engineer this carefully.
The second cliff is index rebuild time. After loading millions of vectors, the index becomes stale as new vectors arrive. You need to rebuild periodically. On large datasets, this takes many hours. During rebuild, the index is either locked so no queries run, or in an inconsistent state. Managing this requires sophisticated systems like dual indexing that few homemade databases implement.
The third cliff is batch insertion performance. Inserting vectors one at a time is slow. Inserting millions at once requires an entire reordering of your index, which, paradoxically, can be even slower per vector. You need to batch carefully, balancing latency against throughput.
The fourth cliff is query complexity with filtering. A simple similarity search is easy. Adding metadata filtering changes everything. Your index is optimised for pure similarity. Adding filters requires scanning candidates and applying predicates, which is much slower.
The Operational Burden You Never Expected
Building a vector database is roughly 20 per cent engineering and 80 per cent operations. Once your database is live in production, the real work begins. You must monitor embedding quality because if upstream embedding generation fails, your database becomes useless. Most teams never implement this. They discover the problem when users complain about garbage results.
You need to manage index corruption, which happens more often than you expect. Corruption comes from bit flips on disk, race conditions in memory, and network partitions. Your database must detect and recover automatically. This requires checksums on every index page and version tracking on every vector.
You need to handle index evolution. In three months, you need a different index structure. You have billions of vectors already indexed. Rebuilding takes days and locks the database. Mature databases use index migration systems that transform the index online while queries continue running.
You need to manage resource contention. Index merges compete with query serving for memory. Insert batches compete with background optimisation for CPU. You need sophisticated scheduling to keep everything running smoothly. You also need operational visibility into what is happening. Your customers experience degraded latency. Most homemade systems provide zero visibility into these details.
The Ecosystem Integration Problem
A vector database does not exist in isolation. It sits alongside embedding generation, inference engines, feature stores, and traditional databases. Integration complexity multiplies at every layer.
You need to integrate with your embedding provider. Your embeddings come from OpenAI, Cohere, Anthropic, or local models. Each has different APIs and failure modes. Some return different embedding dimensions for the same input text if called multiple times. Most homemade systems assume embeddings are stable. They break when you handle real embedding generation with all its quirks.
You need to integrate with your application layer. Your application inserts vectors and must handle insertion latency, failure, and the delay before searchability. This creates complex eventual consistency logic. Vector embeddings are derived data. If source data changes, you need to recompute embeddings and update the database. The database is just one piece of a much larger system.
The Actual Path Forward
If you are still convinced you should build from scratch, you need a realistic timeline. Do not expect the timeline from tutorials. Start with a single-machine index handling 10 million vectors in memory.
Use HNSW because it offers the best balance of speed and simplicity. Budget six months for this phase. If you finish in two months, you have not thought through enough edge cases.
What You Should Actually Do
If you made it this far, you probably should not build your own vector database. Use an existing one. Pinecone is a managed service with a generous free tier. Weaviate is open source. Milvus is optimized for high throughput. Quadrant is production-ready. ChromaDB is simple and lightweight.
Build your own only if you have very specific requirements existing databases cannot meet. Consider contributing to open-source instead. The vendors spent thousands of hours thinking through these problems. They discovered the performance cliffs.
They debugged corruption scenarios. They solved all this and wrapped it in a database you can use without becoming an expert in distributed systems.
Use their work. Build your AI application instead. The real value is not in the vector database.
The real value is in how you use it. Focus there.