Pinecone vs Vertex AI Vector Search
Vertex AI Vector Search is Google Cloud's approximate nearest neighbor service, built on the ScaNN algorithm that powers Google Search and YouTube. The two products differ less in the search algorithm than in how you run and pay for them. On Vertex you deploy an index onto nodes you choose, then size those nodes and pay for them. Google now also sells Agent Retrieval, a newer product where you do not configure VMs or replicas.
Pinecone is the better fit when you want cost to follow the queries you run instead of the nodes you provision, or when you need the same API on AWS, Azure, and Google Cloud. Vertex AI Vector Search is the better fit when you are committed to Google Cloud and want retrieval in the same project, under the same IAM policies, and on the same bill.
The short version
Vertex AI Vector Search is a strong choice when your team is committed to Google Cloud and already uses Google embedding models. Retrieval stays in the same Google Cloud project, under the same IAM policies, and on the same bill. One vendor for the whole stack is an advantage in itself.
Pinecone is the better fit when retrieval is your main workload and you would rather not operate it. Vertex serves an index from nodes you select and scale. Its own pricing documentation computes serving cost as replicas, multiplied by shards, multiplied by an hourly node rate. Builds and updates to the index are billed separately by data volume. Google also notes that heavy use of filters increases memory and creates more shards. Pinecone has no node to size and no rebuild to schedule. In Pinecone a filter narrows the work a query does and does not require more capacity.
The other difference is where each one can run. Pinecone runs on AWS, Azure, and Google Cloud, including inside your own cloud account. Vertex AI runs on Google Cloud.
When to pick which
Each system is the better choice for some workloads. These lists say which.
Choose Pinecone when
- Retrieval is your main workload and you want capacity to track it without sizing nodes.
- You filter heavily and do not want the number of filters you use to raise your shard count and your bill.
- Your data changes continuously and you do not want to plan around index rebuilds.
- You need to run on AWS or Azure, across more than one cloud, or inside your own cloud account.
- You serve many isolated tenants and do not want to provision capacity for all of them combined.
Choose Vertex AI Vector Search when
- You are committed to Google Cloud and want retrieval in the same project, under the same IAM policies, and on the same bill.
- You want one vendor for the whole stack because you already use Google embedding models and Gemini.
- Your committed Google Cloud spend brings the price you actually pay well below list.
- Your updates arrive in batches and you rebuild the index on a predictable schedule.
- Your corpus and query rate are steady enough that sizing nodes is simple.
Feature by feature
How Pinecone and Vertex AI Vector Search differ on the points that usually decide it.
| Capability | Pinecone | Vertex AI Vector Search |
|---|---|---|
| Deployment model | PineconeYou create an index and write to it. Data sits in object storage, Pinecone runs the servers that answer queries, and you never choose an instance size, a node count, or a cluster layout. | Vertex AI Vector SearchYou deploy an index to an endpoint that runs on VM nodes. You choose the machine type and the number of nodes. |
| Scaling model | PineconeAutomatic on the on-demand tier, where you never pick a shard or replica count. On Dedicated Read Nodes, which are capacity you provision, shards and replicas are set by hand today. There is no machine type to pick at either tier. | Vertex AI Vector SearchYou pick the machine type and node count, then add shards as the index grows and replicas as query load grows. On Agent Retrieval, Google's newer product, the system tunes this for you. |
| Effect of filtering on cost | PineconeFilters are applied during retrieval and reduce the work each query does. They have no effect on capacity. | Vertex AI Vector SearchGoogle notes that a high number of restricts (its term for metadata filters) increases memory usage and creates more shards to spread the load. |
| Index updates | PineconeContinuous. Pinecone re-optimizes the index in the background. There is no rebuild step to schedule or pay for separately. | Vertex AI Vector SearchBuilding and updating an index is billed by the volume of data processed. Streaming inserts have their own rate. Google periodically rebuilds the index to merge in streamed updates. It calls this compaction and bills the rebuild at the batch rate. |
| Pricing model | PineconeUsage based. The on-demand tier charges for the reads and writes you run. An idle index costs close to its storage alone. | Vertex AI Vector SearchDeployed nodes bill per node hour whether or not any queries arrive. Google computes serving cost as replicas per shard, multiplied by shard count, multiplied by the hourly node rate, multiplied by 730 hours. |
| Running it in your own cloud | PineconeBring your own cloud runs Pinecone inside your own account on AWS, Azure, or Google Cloud. | Vertex AI Vector SearchNo equivalent. Vertex runs only on Google Cloud, in Google's environment inside your project. |
| Hybrid and sparse retrieval | PineconeDense vectors, sparse vectors, and full-text search with BM25 keyword ranking. Dense and sparse vectors combine in one query. Full-text and dense combine as a text-match filter on a dense search, or as two searches merged in your application. | Vertex AI Vector SearchDense vectors, sparse vectors (for keyword-style matching), and hybrid search that combines them. No BM25 keyword ranking or full-text index. |
| Embeddings and reranking | PineconePinecone Inference hosts embedding and reranking models next to the index. | Vertex AI Vector SearchGoogle's own embedding models on the same platform. |
| Multi-tenancy | PineconeOne index holds a namespace per tenant, with nothing to provision for each new tenant. The documented limit is 100,000 namespaces per index on Standard and 1,000,000 on Enterprise. Dedicated Read Nodes support one namespace per index today. For now you cannot combine them with a design that keeps many tenants in one index. | Vertex AI Vector SearchEach tenant gets its own index and endpoint, or tenants share an index and are separated by filters. Either way, you provision nodes for all tenants combined. |
Vertex AI Vector Search by the numbers
Google documents each of these figures. They explain why a Vertex bill changes when your data or your filters change, even if your query volume stays flat.
730
hours per month in Google's serving-cost formula: replicas per shard, times shards, times the hourly node rate, times 730
5 days
age of the oldest uncompacted streamed data at which Google guarantees a compaction: a rebuild that merges those updates into the index, billed at the batch rate on top of the streaming cost
9
regions where Agent Retrieval, the self-tuning successor with no VMs to configure, is available today
0
Index Serving Capacity Units provisioned for exact KNN on Agent Retrieval. Exact KNN compares the query with every stored vector and runs directly against the database that holds your data objects
How each one runs
Google has renamed this product more than once. It started as Matching Engine, became Vertex AI Vector Search, and now appears in Google's documentation as Vector Search under the Gemini Enterprise Agent Platform brand. It is the same service throughout. Most people still call it Vertex AI Vector Search.
It is built on ScaNN, which Google Research developed and Google uses in its own products. ScaNN is a very good approximate nearest neighbor implementation.
The two products differ in what you have to set up and run around the algorithm. On Vertex you deploy an index to an endpoint, choose a machine type, and decide how many nodes and replicas serve it. Pinecone writes vectors to object storage and serves them from a pool of stateless query servers that Pinecone runs. That pool scales on its own.
That difference decides who plans capacity and how the system and the bill respond when the workload changes.
Filtering shows up in the bill
Google's term for metadata filters is restricts. Its pricing documentation says that dataset size and restricts both affect shard count. It also says that a high number of restricts increases memory usage and causes more shards to be created to distribute that load. More shards means more nodes. Vertex bills each node by the hour.
Adding attributes you can filter on looks like a schema decision. On Vertex it also changes how much capacity you need and what you pay. You have to model that ahead of time.
In Pinecone, filtering is part of retrieval. A selective filter reduces the work a query does. It does not change what you provision. On the on-demand tier there is nothing to provision at all.
Vertex bills for index updates
Vertex bills serving and index building separately. Building and updating indexes is charged by the volume of data processed. Streaming inserts carry an additional per-volume rate. Google's documentation also describes compaction, a periodic rebuild that merges streamed updates into the index. Google triggers it on a schedule based on how old the uncompacted data is. You pay for each rebuild at the batch update rate, on top of the streaming cost.
For a corpus you load once and refresh monthly, this is cheap and easy to plan. For a corpus that changes all day, every day, the cost of rebuilds becomes something you design around.
Pinecone assumes your data changes all the time. Writes are acknowledged immediately and become queryable in seconds. Pinecone re-optimizes the index in the background over time. There is no separate rebuild to schedule or pay for.
Where each one can run
Vertex AI is a Google Cloud service. For a company that runs on Google Cloud and expects to stay there, that is an advantage. You get one bill, one identity model, and one support relationship.
It becomes a limit when that plan changes. Some teams end up on another cloud through an acquisition. Some sell to customers who require a specific cloud. Others choose to run on more than one cloud on purpose. All of them end up needing retrieval they can take with them.
Pinecone runs on AWS, Azure, and Google Cloud with the same API. Some teams cannot send data to an environment a vendor operates. For them, bring your own cloud runs Pinecone inside their own account on any of the three clouds. The data stays within their perimeter while Pinecone still operates the system.
Agent Retrieval, Google's newer product
Vector Search has worked this way since launch, with nodes you provision and rebuilds you pay for. Most deployed Vertex indexes run on it today. In March 2026 Google made Agent Retrieval, previously called Vector Search 2.0, generally available alongside it.
Google describes Agent Retrieval as self-tuning and fully managed. It says the system is auto-tuned and you do not configure VMs or replicas. You work mainly with collections of data objects. The index is no longer the main resource you manage. Pricing is usage-based for smaller workloads or resource-based for tuned performance. Today Agent Retrieval is available in nine regions. Vertex runs in more.
If your team adopts Agent Retrieval, node sizing and index rebuild costs matter much less. The products still differ on which clouds they run on and on whether your retrieval can move with you to another cloud.
Frequently asked questions
What is the difference between Vertex AI Vector Search and Vertex AI Search?
The two are often confused, but they solve different problems. Vertex AI Vector Search is the approximate nearest neighbor service. You bring embeddings, build an index, deploy it to an endpoint, and query it for nearest neighbors. Vertex AI Search is a higher-level managed product for search and answers that handles ingestion and retrieval for you. This page compares Pinecone with Vector Search, the vector database layer.
Is Pinecone a good alternative to Vertex AI Vector Search?
Yes, if you want retrieval costs to follow the queries you run instead of the nodes you provision, or if you need the same API on more than one cloud. If you are committed to Google Cloud and want retrieval in the same project, under the same IAM policies, and on the same bill, Vertex is the more natural fit. A fixed node price is also easier to forecast than usage-based billing.
What is the difference between Pinecone and Vertex AI Vector Search?
Both run approximate nearest neighbor search at scale. They differ mainly in how you run and pay for them. The classic Vertex product deploys an index onto VM nodes you pick and size, then bills per node hour whether or not queries arrive. Google's newer Agent Retrieval tunes this automatically. Pinecone's on-demand tier has no node count to choose. It charges for storage plus the reads and writes you run. They also differ in where they run. Pinecone runs on AWS, Azure, and Google Cloud, including inside your own account. Vertex runs on Google Cloud.
How is Vertex AI Vector Search priced?
Pricing has two parts. Serving is billed per node hour for each VM that hosts a deployed index. Machine type, shard count, and replica count set the total. Building and updating indexes is billed by the volume of data processed. Streaming inserts have a separate rate. Google's documentation computes serving cost as replicas per shard, multiplied by shards, multiplied by the hourly rate, multiplied by 730 hours per month.
Does using more metadata filters cost more on Vertex AI?
It can. Google calls metadata filters restricts. Its pricing documentation says that dataset size and restricts both affect shard count. It also says a high number of restricts increases memory usage and causes more shards to be created. Because you pay per node hour, more shards cost more. In Pinecone, filtering is part of retrieval and does not change what you provision.
Can I use Pinecone on Google Cloud?
Yes. Pinecone runs on Google Cloud as well as AWS and Azure. It is also available through the Google Cloud Marketplace. With bring your own cloud on GCP, Pinecone runs inside your own Google Cloud account and your data stays within your perimeter.
Is Pinecone or Vertex AI better for RAG?
Both serve retrieval for RAG. Vertex fits when you are committed to Google Cloud, already use Google embedding models, and have a workload steady enough to size nodes for. Pinecone fits when retrieval is your main workload, your corpus changes continuously, you filter heavily, or you need to run somewhere other than Google Cloud. Test both against your own corpus and query patterns before you decide.
Sources
- Vertex AI Vector Search overview (Google Cloud)
- Agent Retrieval, formerly Vector Search 2.0 (Google Cloud)
- Vertex AI pricing, Vector Search section (Google Cloud)
- Pinecone pricing
- How Pinecone works
- Full-text search in Pinecone
Claims about Vertex AI Vector Search last checked against its own documentation on . Competitors ship changes we do not control. If something here is out of date, tell us and we will correct it.
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