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 algorithm is not the difference. The difference is the operating and billing model around it: you deploy an index onto nodes you choose, and you size and pay for them. Google now sells a second product alongside it, covered below, that changes that model.
Pinecone wins on cost that follows queries rather than provisioned nodes, and on running the same API across AWS, Azure, and Google Cloud. Vertex is the better choice when you are committed to Google Cloud and want retrieval inside the same project, IAM, and billing perimeter.
The short version
Vertex AI Vector Search is a strong choice when the team is already committed to Google Cloud, wants retrieval inside the same project, IAM, and billing perimeter, and is already using Google embedding models. Keeping one vendor is worth something real.
Pinecone is the better fit when retrieval is the primary workload and you would rather not operate it. Vertex serves an index from nodes you select and scale, and its own pricing documentation computes serving cost as replicas multiplied by shards multiplied by an hourly node rate. Index builds and updates are separately billed by volume, and Google notes that heavy use of filters increases memory and creates more shards. Pinecone has no node to size, no rebuild to schedule, and filters that narrow work rather than adding capacity.
The other difference is where it 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
Both systems have workloads they suit. This is where each one is the better answer.
Choose Pinecone when
- Retrieval is the primary workload and you want capacity to follow it without sizing nodes.
- You filter heavily and do not want filter cardinality showing up as shard count and cost.
- Data changes continuously and you would rather not think about index rebuilds.
- You need to run on AWS or Azure, across more than one cloud, or inside your own cloud account.
- You want many isolated tenants without provisioning for their combined footprint.
Choose Vertex AI when
- You are committed to Google Cloud and want retrieval inside the same project, IAM, and billing perimeter.
- You already use Google embedding models and Gemini, and want one vendor across the stack.
- Committed Google Cloud spend makes the effective price materially lower than list.
- Your workload is batch shaped, with an index rebuilt on a predictable schedule.
- Your corpus and query rate are steady enough that provisioned nodes are straightforward to size.
Feature by feature
How Pinecone and Vertex AI differ across the capabilities that usually decide the choice.
| Capability | Pinecone | Vertex AI |
|---|---|---|
| Deployment model | PineconeYou create an index and write to it. Storage sits on object storage, queries run on executors Pinecone operates, and there is no instance, node count, or cluster topology to choose. | Vertex AIAn index is deployed to an endpoint backed by VM nodes. You choose the machine type and the number of nodes. |
| Scaling model | PineconeAutomatic on the on-demand tier, with no node count to choose. Dedicated Read Nodes are provisioned capacity, with shards and replicas set by hand today. | Vertex AIYou pick the machine type and node count, then add shards as the index grows and replicas as query load grows. Agent Retrieval auto-tunes this instead. |
| Effect of filtering on cost | PineconeFilters are applied during retrieval and narrow the work done. No capacity effect. | Vertex AIGoogle notes that a high number of restricts increases memory usage and creates more shards to spread the load. |
| Index updates | PineconeContinuous. Background compaction re-optimizes data with no rebuild step you schedule or pay for separately. | Vertex AIBuilding and updating an index is billed by volume of data processed, and streaming inserts carry their own rate. Compaction triggers a rebuild billed at the batch rate. |
| Pricing model | PineconeUsage based. On-demand charges for the reads and writes you run, and an idle index costs close to storage alone. | Vertex AIDeployed nodes bill per node hour whether or not 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 in your own account, on AWS, Azure, or Google Cloud. | Vertex AINo equivalent. Vertex runs in Google's environment inside your Google Cloud project, and only on Google Cloud. |
| Hybrid and sparse retrieval | PineconeDense and sparse indexes, plus reranking models. | Vertex AIDense, sparse, and hybrid search. |
| Embeddings and reranking | PineconePinecone Inference hosts embedding and reranking models next to the index. | Vertex AIGoogle's embedding models within the same platform. |
| Multi-tenancy | PineconeNamespaces inside one index, with no per-tenant provisioning. | Vertex AISeparate indexes and endpoints, or filters, with nodes provisioned for the combined footprint. |
Vertex AI by the numbers
Google documents each of these. Together they are why a Vertex bill moves when your data or your filters change, rather than when your query volume does.
730
hours per month in Google's own serving-cost formula: replicas per shard, times shards, times the hourly node rate, times 730
5 days
age of the oldest uncompacted data at which Google guarantees a compaction trigger, billed at the batch rate on top of streaming cost
9
regions where Agent Retrieval, the self-tuning successor with no VM configuration, is available today
0
Index Serving Capacity Units provisioned for exact KNN on Agent Retrieval, which runs against the payload database directly
The algorithm is not where these differ
Vertex AI Vector Search is built on ScaNN, developed by Google Research and used in Google's own products. It is a genuinely good approximate nearest neighbor implementation.
The difference is everything around the algorithm. Vertex asks you to deploy an index onto 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 executors that scale on their own.
That distinction determines who does the capacity work, what happens when the workload changes shape, and how the bill responds.
Filtering shows up in the bill
Vertex calls metadata filters restricts. Google's pricing documentation states that dataset size and restricts both affect shard count, and 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, and nodes are the unit you pay for by the hour.
So a design decision that looks purely functional, adding attributes you can filter on, has a capacity and cost consequence you have to model in advance.
In Pinecone, filtering is part of retrieval. A selective filter reduces the work a query does. It does not change what you provision, because you do not provision anything.
Updating an index is a billed event
Vertex separates serving cost from build cost. Building and updating indexes is charged by the volume of data processed, and streaming inserts carry an additional per-volume rate. Google also documents that compaction is triggered on a schedule based on how old the uncompacted data is, and that you are billed for that rebuild at the batch update rate on top of the streaming cost.
For a corpus that is loaded once and refreshed monthly, this is easy to plan and cheap. For a corpus that changes all day, every day, the rebuild economics become part of the architecture.
Pinecone treats continuous change as the normal case. Writes are acknowledged immediately, become queryable in seconds, and background compaction re-optimizes the data over time without a separate rebuild you schedule or fund.
Where it can run
Vertex AI is a Google Cloud service. If your company is on Google Cloud and expects to stay there, that is a feature: one bill, one identity model, one support relationship.
It becomes a constraint when the answer changes. Teams that acquire onto another cloud, sell into customers who require a specific cloud, or run a deliberate multi-cloud posture end up needing retrieval that moves.
Pinecone runs on AWS, Azure, and Google Cloud with the same API. For teams that cannot send data to a vendor-operated environment at all, bring your own cloud runs Pinecone inside their own account on any of the three, which keeps the data in their perimeter while Pinecone still operates the system.
Google now sells a second product with a different model
Everything above describes Vector Search as it has worked since launch, which is what most deployed Vertex indexes run on 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, and says the system is auto-tuned so that you do not configure VMs or replicas. You work with collections of data objects rather than managing an index as the primary resource, and pricing offers a usage-based model for smaller workloads next to a resource-based model for tuned performance. It is available in nine regions today rather than everywhere Vertex runs.
Where a team lands on Agent Retrieval, the node-sizing and index-rebuild arguments on this page carry much less weight. We would rather point that out than let a comparison rest on a product generation you may not be buying. The differences that survive are the ones in the last section: which clouds it runs on, and whether retrieval can move with you.
Frequently asked questions
They solve different problems and are often confused. Vertex AI Vector Search is the approximate nearest neighbor service: you bring embeddings, build an index, deploy it to an endpoint, and query for nearest neighbors. Vertex AI Search is a higher-level managed search and answering product that handles ingestion and retrieval for you. This page compares Pinecone with Vector Search, which is the vector database layer.
Yes if you want retrieval cost that follows queries rather than provisioned nodes, or you need the same API across more than one cloud. If you are committed to Google Cloud and want retrieval inside the same project, IAM, and billing perimeter, Vertex is the more natural fit, and a fixed node price is easier to forecast than usage-based billing.
Both run approximate nearest neighbor search at scale, and the algorithms are not where they differ. The classic Vertex product deploys an index onto VM nodes you pick and size, and bills per node hour whether or not queries arrive. Google's newer Agent Retrieval auto-tunes that away. Pinecone has no node count to choose and charges for storage plus the reads and writes you run. The other difference is reach: Pinecone runs on AWS, Azure, and Google Cloud, including inside your own account, and Vertex runs on Google Cloud.
Two parts. Serving is billed per node hour for each VM hosting a deployed index, with the total driven by machine type, shard count, and replicas. Building and updating indexes is billed by the volume of data processed, with a separate rate for streaming inserts. Google's own documentation computes serving cost as replicas per shard multiplied by shards multiplied by the hourly rate multiplied by 730 hours per month.
It can. Google's pricing documentation states that dataset size and restricts, its term for filters, both affect shard count, and that a high number of restricts increases memory usage and causes more shards to be created. Because you pay per node hour, more shards means more cost. In Pinecone, filtering is part of retrieval and does not change what you provision.
Yes. Pinecone runs on Google Cloud as well as AWS and Azure, and is available through the Google Cloud Marketplace. Bring your own cloud also supports GCP, which runs Pinecone inside your own Google Cloud account so data stays within your perimeter.
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 the primary workload, the corpus changes continuously, you filter heavily, or you need to run somewhere other than Google Cloud. Test both against your own corpus and query pattern before deciding.
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
Claims about Vertex AI 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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