Pinecone vs Elasticsearch

How each one stores and serves vectors

Elasticsearch splits data into shards, each of which is a Lucene index, then spreads the shards across nodes and copies them into replicas. That design was built for matching terms, aggregating fields, and searching text at scale. It does those things very well.

Vector search was added on top of that design. Vectors live in the same shards and share the same node capacity as everything else. They also go through the same segment merges, the background process where Lucene combines small index files into larger ones. On a cluster you run, storage and compute stay tied together. If you need more room for vectors, you also pay for more compute. If you need more query throughput, you also pay for more storage.

Pinecone separates the two. Vectors are written to object storage and served by a pool of stateless query servers that Pinecone runs. Growing the corpus and growing query throughput are separate decisions. Neither one requires you to plan a cluster.

What changes on Elastic Cloud Serverless

Everything above applies to a cluster you size: Elastic Cloud Hosted, where Elastic hosts it for you, or a self-managed deployment you run yourself. This is how most production Elasticsearch runs and what most teams compare against. Elastic now sells another option.

Elastic Cloud Serverless is generally available on AWS, GCP, and Azure. Elastic describes a stateless architecture built on object storage that separates compute from storage and the search tier from the indexing tier. It scales automatically with search and indexing load and bills on usage instead of provisioned resources. Elastic's comparison table lists what Elastic manages on Serverless: hardware configuration, cluster size, node count, and versions.

That removes most of the operational difference between the two products. If you are evaluating Serverless, cluster planning is not a reason to pick Pinecone. The same Elastic table lists what you give up: control over cluster topology and version pinning, plus some platform capabilities that Serverless does not carry. Compare the two on retrieval behavior, how filtering works, and how cost grows at your own scale.

What running a cluster involves

Running a self-managed or Elastic Cloud Hosted cluster well means making a set of recurring decisions: how many primary shards an index needs before you create it, when to add nodes, how much filesystem cache the data nodes need for vector data, when to force-merge segments, and where to place replicas for availability. Vector data lives in the filesystem cache, outside the JVM heap. You size that memory separately from the heap.

On Pinecone's on-demand tier, none of those decisions has an equivalent. You work with indexes and namespaces. Pinecone does not expose the infrastructure underneath them.

Elastic has cut how much memory vectors need

The filesystem-cache sizing above assumes vectors have to stay in memory to be fast. That was true of Lucene's HNSW index for a long time. It is no longer the default. Elastic ships Better Binary Quantization, which converts each dimension from a 32-bit float to a bit and keeps 14 bytes of corrective data per vector, roughly a 32x reduction. To recover the accuracy that compression costs, BBQ fetches 3x as many candidates as you asked for (oversampling) and reranks them against the full-precision vectors.

DiskBBQ goes further. It groups vectors into clusters with hierarchical k-means, keeps them on disk, and reads only the clusters nearest the query. Elastic's reference documentation states that it is the default index type for dense_vector fields where the license makes it available. If you are provisioning Elasticsearch today, the memory math is different.

Elastic chose this default on purpose. By making a disk-resident index the default for new indices, Elastic shows that it considers the trade right for the large majority of workloads. HNSW in memory is now something you opt into, for the small set of workloads that need recall above roughly 99 percent. Anyone who still calls DiskBBQ a recall sacrifice is describing an older Elasticsearch.

Elastic has also made filtered search faster. Elasticsearch applies a kNN query's filter during the graph search, which Elastic documents as returning the full number of results you asked for. Without ACORN, that search compares the query against every vector it visits and keeps only the ones that pass the filter. With a restrictive filter, most of that work is wasted. ACORN, the default since 9.1, compares only vectors that pass the filter. To avoid missing parts of the graph, it also looks one step further out, at the neighbors of each node's neighbors. Elastic notes that exceptionally high recall targets may need a larger candidate pool with ACORN.

On Elasticsearch, you still choose the settings yourself. Elastic gives you index types, a quantization level, an oversample factor, and a rerank depth. You set them per field and revisit them as the corpus changes. Even the auto-calibration Elastic added picks an encoding per segment against a recall target you choose. Pinecone picks the quantization for each block of stored data while it re-optimizes the index in the background. There is no setting to change. The gap is smaller than it was a year ago.

Why there is no benchmark on this page

Pinecone has published a head-to-head benchmark against Amazon OpenSearch Service on the OpenSearch comparison page. OpenSearch is a fork of Elasticsearch 7.10. That makes it the closest thing we have to a measured Elasticsearch comparison. We have kept its numbers off this page on purpose.

That benchmark measured OpenSearch's engine. It did not measure Elastic's BBQ and DiskBBQ defaults, the ACORN change to how filtered vector queries walk the graph, or Elastic Cloud Serverless. A benchmark that measured none of those cannot tell you how Elasticsearch performs with them.

The two projects have also been diverging for more than five years, since the 7.10 fork. Numbers measured against OpenSearch describe OpenSearch. Relabeling them as Elasticsearch results would be the same practice we criticize elsewhere on this site.

A few points follow from the architecture and still hold after all of those changes. On a cluster you size, the index sits on the same nodes as everything else. Indexing new data competes with queries for those nodes. You also have to decide capacity before you have a workload to size it against. Elastic Cloud Serverless changes this. If you want numbers, run your own corpus and the filters you use in production against both systems. We will help you set that up.

Multi-tenancy scales differently

An application that serves many customers usually has to keep each customer's data isolated. In Elasticsearch, teams typically give each tenant its own index, or share one index that routes and filters by tenant. Either way, the cluster has to be provisioned for the combined working set of every tenant at once.

Pinecone uses namespaces, which are partitions inside a single index. A query runs against one namespace at a time. Adding tenants does not require you to provision capacity for their combined size in advance.

When to stay on Elasticsearch

If Elasticsearch is already running, already staffed, and the vector workload is small next to the log and analytics work it handles, adding vectors there is reasonable. The same is true when an application depends on aggregations or the surrounding Elastic tooling.

That usually stops holding as scale and the rate of change go up. When retrieval becomes the primary workload, when the corpus grows faster than the cluster plan assumed, or when the number of tenants climbs, the cluster planning that used to be a side task becomes the main cost.