Every pitch for a dedicated vector database opens with the same chart: the dedicated engine wins on recall and latency. What the chart crops out is the sync overhead of a second system, the extra round trip, stale results, and separate governance, security and billing surface. AWS reckons that second database meant at least $700 a month regardless of usage.
That tax is why vector search is going native. DynamoDB, PostgreSQL via pgvector, and Oracle 26ai now store embeddings beside the records they describe, on the same HNSW, IVF and DiskANN indexes dedicated engines use, so under a few million vectors the gap closes. In short, native vector search stores embeddings as plain columns in the database you already run.
Native vector search vs a dedicated vector database: how do the approaches differ?
Native vector search runs similarity queries inside the database you already operate, DynamoDB’s native index, PostgreSQL via the pgvector extension, or Oracle 26ai’s VECTOR type, storing vectors beside the data they describe. A dedicated vector database such as Milvus/Zilliz, Qdrant, Weaviate or Chroma is a separate, purpose-built system tuned for peak recall and scale.
There are three tiers: a native index inside an operational database (DynamoDB), a vector-capable extension (pgvector on PostgreSQL), and a standalone store, from local-first Chroma up to billion-scale Milvus and managed Zilliz Cloud.
Native predates this year’s wave. Oracle 26ai ships its VECTOR type and Select AI with AI Vector Search at no extra charge, a direct shot at vector database vendors. The comparison pairs people actually search map the same way:
- DynamoDB vs pgvector: operational-native against the extension you already run.
- Oracle 26ai’s VECTOR type vs a standalone store: legacy-schema native against a dedicated engine.
- A dedicated store vs an extension: peak performance against co-located simplicity.
- A vector lakebase vs a dedicated store: lakehouse search against a specialist.
Both sides use the same index structures, so raw ANN performance is close to a commodity; the differentiators are architecture and operations. Native and extension tiers sell the hybrid, multi-model pitch, one platform for everything, where vectors inherit transactions, joins, backups and access control. The pgvector GitHub repository and its documentation are the primary sources for the extension tier, and the broader native search picture fills in context. That architecture-versus-operations split is what the fit test in the next section measures.
How do I evaluate whether native vector search fits my existing workload?
Assess native vector search against five fit criteria: whether your operational data already lives in that database, whether your write and read patterns tolerate it, whether index configuration is fixed once set, whether quotas hold, and whether the index fits in memory under a sizing calculation.
First, the easy win: if your operational data already sits in DynamoDB or Postgres, native search removes the sync pipeline.
Second, behaviour. Native indexes accept writes beside normal traffic, but every insert updates a graph, so write-heavy workloads add latency, and deletes are tombstoned and reclaimed later.
Third, the one most people miss. On DynamoDB, dimensions, distance function, projection and partition key are set at index creation and can’t be changed afterward, so swapping embedding models later means rebuilding from scratch.
Fourth, quotas. A DynamoDB table holds up to five vector indexes by default, and throughput is subject to per-partition-key limits, so a partition key buys a separate quota.
Fifth, where the arithmetic decides. A 768-dimension float32 vector is about 3 KB before graph overhead, and a few million of them can consume 10+ GB of RAM on your production database while it is running.
Then the questions to ask before committing: what does lock-in and reversibility cost when you outgrow it? Does operational burden versus engineering time favour native? Does governance and audit parity hold? What happens when the workload outgrows native? The cost and lock-in angle is worth a read. Fit decides which tier you choose; the recall-versus-latency dial decides how you tune it, whichever tier you land on.
How should we weigh recall versus latency before choosing an approach?
Recall and latency share one dial, and every approach, native, extension or dedicated, exposes it differently. Push toward recall and you spend memory and milliseconds; push toward speed and you accept missed neighbours. Where you set that dial is the decision every architecture has to make.
HNSW’s ef_search and IVF’s nprobe raise recall at a latency cost, and Qdrant’s filterable HNSW builds filtering straight into the index. Quantization trades accuracy for memory: int8 scalar quantization cuts memory by roughly 75%, and binary quantization reaches a 32x reduction. Matryoshka dimension reduction truncates a 1,536-dimension vector to 256 or 512 dimensions before you even reach for quantization.
Vendor-authored results dominate VectorDBBench, the public leaderboard, and independent, apples-to-apples native-versus-dedicated numbers are scarce. Test against your own data and filters, and watch tail latency, because the p99 matters more than the median.
A single HNSW index becomes impractical around 50 to 100 million vectors, because the graph must largely fit in RAM. That’s where sharding enters and a distributed store like Milvus, Zilliz, Qdrant or Weaviate earns its keep. Below that line the choice is about fit; above it, the decision flips back. Unsure you need vector search at all? When files and agents are enough has that answer.
So that’s the map. Native wins when your operational data is already in that database, your index fits in memory, and your recall and latency requirements sit within one dial-turn of each other. Leave the shootout behind and ask the question worth asking: what does it cost to re-embed and migrate when you outgrow this tier? For the full story, native vector search in general-purpose databases is the thread to pull next.
Frequently Asked Questions
Does native vector search actually match a dedicated vector database on recall?
For most workloads, yes. Both native and dedicated systems rely on the same approximate-nearest-neighbour indexes, HNSW, IVF or DiskANN, so raw recall at a given latency is close when the index fits in memory. The gap opens at scale or under heavy filtering, where a distributed store can spread shards and dedicate more RAM per vector.
Is pgvector good enough for production, or is it only for prototypes?
pgvector runs in production for many teams, provided you manage the operational details. PostgreSQL’s extension gives you HNSW and IVF indexes with transactions and backups, but index builds are memory-hungry and updates can bloat the table until vacuum catches up. Under a few million vectors on a well-sized box, it is a sound production choice rather than a demo.
Can native vector search keep up with real-time writes, updates and deletes?
It can, with caveats. Native indexes accept writes beside your operational traffic, but each insert updates a graph or cluster, so high-velocity ingestion adds latency and rebuild overhead. Deletes are often tombstoned and reclaimed later. If your workload is write-heavy and search-heavy at once, test both, because a dedicated store can isolate indexing from transactional load.
Can native vector search handle filtered and hybrid search well?
Yes, and this is often where native shines. Because vectors sit beside your relational or document data, you can combine similarity search with metadata filters, joins and full-text ranking in one query, without a second round trip or a sync pipeline. Dedicated stores support filtering too, but you must keep their filterable fields in step with your operational tables.
What is the difference between HNSW, IVF and DiskANN, and does my choice of index matter?
They are different approximate-nearest-neighbour structures. HNSW builds a layered graph for high recall and fast queries but holds the index in memory; IVF clusters vectors and probes a subset, trading recall for memory; DiskANN keeps the graph on SSD for very large sets. The choice matters more for memory and scale than for headline speed, since all three are mature.
Does adding a vector index slow down my existing operational queries?
It can, because the index competes for the same memory, CPU and I/O as your transactional workload. An HNSW index that must sit in RAM reduces the buffer cache available for ordinary queries, and index builds consume significant memory. Sizing the box for both workloads, or moving search to a read replica, keeps latency predictable.
What happens when my dataset outgrows a single machine’s memory?
This is the point where the decision flips. A single HNSW index becomes impractical somewhere around 50 million to 100 million vectors, because the graph must largely fit in RAM. Past that, you either shard the native index across machines or move to a distributed dedicated store such as Milvus, Zilliz, Qdrant or Weaviate, which is built to shard and replicate from the start.
How do I benchmark native vector search against a dedicated store fairly?
Run both against your own data, queries and filters on matched hardware. Public leaderboards like VectorDBBench are useful for orientation, but vendor-authored charts dominate the space and neutral, apples-to-apples native-versus-dedicated numbers are scarce. Measure recall at your target latency, then watch tail latency under concurrency, since that is where shared-box contention shows up.
How much does a dedicated vector database cost compared with going native?
A dedicated vector database adds a metered bill plus the engineering time to run a sync pipeline, so its true cost is higher than the headline price. Native search reuses infrastructure you already pay for, which is why it usually wins on total cost for smaller workloads. The break-even arrives when scale or recall demands outgrow what one box can deliver.
Will native vector search lock me into a single cloud provider?
Not inherently, but the extension and native tiers differ. Open source pgvector runs on PostgreSQL anywhere, including on-premises, so it stays portable. Fully managed native indexes such as DynamoDB’s tie the feature to that provider. The real lock-in to weigh is the re-embedding and migration cost when you outgrow the tier, not the vendor logo.
Does native vector search work for image and multimodal embeddings?
Yes, provided your database stores vectors of the expected dimension. Native support is about where the vector lives, not what produced it, so CLIP-style image embeddings or combined text-and-image vectors work the same as text embeddings. The practical limits are storage and memory, since image and multimodal embeddings are often higher-dimensional and therefore larger.
