Pinecone vs OpenSearch
Pinecone and Amazon OpenSearch Service both support vector search, but they differ in critical ways. Pinecone is a purpose-built vector database that supports dense and sparse indexes, is fully serverless across multiple cloud providers, and requires no capacity planning—making it highly cost-effective and easy to operate at scale. OpenSearch Service, while often repurposed for vector search, was originally designed for log analytics and general-purpose search. As datasets grow, OpenSearch demands increasingly large infrastructure, manual tuning, and sacrifices in recall to control costs—especially when using high-dimensional vectors. Pinecone avoids these tradeoffs, delivering scalable performance without complexity.
What is Pinecone?
Pinecone is a purpose-built, fully managed vector database designed for high-performance, low-latency semantic and full-text search at scale, supporting billions of vectors and seamless integration with AI workflows.
What is Amazon OpenSearch Service?
Amazon OpenSearch Service is a managed OpenSearch service that provides search and analytics for log analytics, real-time application monitoring, and clickstream analytics. It has added on vector search support by adopting existing open source libraries such as HNSW and IVF. OpenSearch is a fork of Elasticsearch 7.10.
Why Pinecone over OpenSearch?
Amazon OpenSearch Service struggles to scale vector workloads efficiently—its storage-heavy architecture leads to rising costs and degraded performance as your dataset grows. To compensate, you're forced to shrink vector sizes or quantize data, both of which hurt recall and add complexity. Pinecone, by contrast, handles high-dimensional vectors without sacrificing recall or driving up cost. Pinecone also eliminates the constant manual tuning OpenSearch demands—there is no algorithm lock-in, no endless parameter tweaks—just out-of-the-box performance that scales with your needs.
An In-Depth Look
Increased multi-tenant scalability
OpenSearch Service indexes have a 1 TB limit per index, which caps the number of vectors to around 150M per tenancy. Pinecone on the other hand supports multi-tenant workloads through its namespace construct that allows it to scale efficiently for billions of vectors.
More cost efficient
OpenSearch Managed Cluster has to be provisioned for peak capacity, and OpenSearch Serverless is capped at 6GB memory and 1 vCPU per OpenSearch Compute Unit (OCU). Pinecone scales reads and writes serverlessly and has no cap on resources, which leads to 25-50x more cost effectiveness than OpenSearch.
Better data freshness
OpenSearch Service requires the selection of an indexing engine (HNSW, IVF) which each come with different read and write optimizations with reindexing degrading performance, especially at scale. Pinecone decouples storage and compute which allows it to scale compute separately for reads and writes with algorithms dynamically selected based on workload.
No capacity planning needed
OpenSearch Service requires extensive VPC, networking, and IAM provisioning. For OpenSearch Managed Cluster, capacity planning is required which requires extensive price modelling. Pinecone is fully serverless with no capacity planning – you only pay for what you use.
Benchmarks
Benchmarking is performed using Cohere768 dataset. Total vectors = 10M with 768 dimensions. Metric = Cosine.
Benchmarking Tool = VSB. Testing Environment = EC2 server in AWS us-east-1 region, both Pinecone Indexes and OpenSearch Indexes are in us-east-1 region.
*OpenSearch Cluster - configuration: 3 nodes of type r5.12xlarge.search with 100GB of EBS storage attached to each node. Each r5.12xlarge.search node has 48 vCPU and 384GB memory.
22x higher inserts than Amazon OpenSearch Serverless
42min vs 15+ hours to insert 10 million vector embeddings
3x higher inserts than Amazon OpenSearch Cluster
42min vs 122min to insert 10 million vector embeddings
4x faster queries than Amazon OpenSearch Serverless
180ms vs 540ms query response time against a 10M index
9% more accurate search results than Amazon OpenSearch Serverless
25x cheaper than Amazon OpenSearch Serverless
50x cheaper than Amazon OpenSearch Cluster
Feature by Feature Comparison
Pinecone and Amazon OpenSearch Service differ in that the former is a dedicated vector database while the latter is a search engine that has vector index features. Here is a summary of the key features between Pinecone and Amazon OpenSearch Service.
| Feature | Pinecone | OpenSearch |
|---|---|---|
| Index type | Dense and sparse indexes | Dense, sparse and time series indexes |
| Fully managed | Serverless | Cluster-based and Serverless |
| BYOC | Yes, available in AWS, Azure, and GCP | No |
| Indexing algorithms | Proprietary, innovative algorithm that implements adaptive clustering for more efficient queries | HNSW, IVF and IVFPQ for Cluster-based Only HNSW for Serverless |
| Consistency model | Eventual consistency | Eventual consistency |
| Multi-tenancy | Data isolation achieved through namespace partitions | Isolation achieved through domains (clusters) |
| Namespaces | Yes, provides multi-tenancy and faster queries | Limited, provides multi-tenancy for querying but requires nodes provisioning for the aggregated workload size across all tenancies |
| Data operators | Supports upsert, query, fetch, update, list, import, and delete. | Supports upsert, query, fetch, update, list, import, and delete (Serverless does not support CustomId, which makes it harder to perform updates) |
| Metadata store | Yes, supports key-value pairs in JSON objects. Keys must be strings, and values can be string, number, booleans, and list of strings. | Yes, supports JSON objects |
| Metadata filtering | Yes, filtering available using query language based on MongoDB query and projection operators. | Yes, filtering available using query language based on Lucene engine query circle-info |
| Read latency | 130ms p50 and 180ms p95 | 470ms p50 and 540ms p95 |
| Pricing | Pricing is serverless (pay for what you use) | Cluster requires complex memory capacity estimations |
| Marketplace | Available through AWS, Azure, and GCP marketplaces | Only available through AWS Marketplace |
| Local development | Yes, available through Pinecone Local | Yes, available as open source OpenSearch |
| Ecosystem integration | Integrated with data sources, frameworks, infrastructure, models, and observability providers through the Pinecone Partner Program. | Integrated with AWS ecosystem of services |
| MCP | Yes, remote servers available for Pinecone Assistant. Local servers available for Assistant and Development. | No |
| Programmatic access | Yes, access through Pinecone API, Terraform, and Pulumi. | Yes, through AWS API |
| SAML/SSO support | Yes, supports all SAML 2.0 providers | Yes, supports SAML via IAM federation |
| Customer-managed Encryption Keys | Yes, encryption provided through AWS KMS | Yes, encryption provided through AWS KMS |
| Private Endpoints support | Yes, support for AWS PrivateLink | Yes, set up through OpenSearch Service-managed VPC endpoint (powered by AWS PrivateLink) |
| Audit logs | Yes, audit logs published to Amazon S3 | Yes, audit logs published toCloudWatch Logs |
| Access Controls | Yes, role-based access controls | Yes, through AWS IAM |
| Data Ingestion | Bulk data import from Amazon S3. Batch and parallel upserts | Data can be streamed through Amazon OpenSearch Ingestion |
| Embedding Models | Pinecone Inference provides llama-text-embed-v2, multilingual-e5-large, pinecone-sparse-english-v0 | Embedding models available through Amazon Bedrock |
| Reranking Models | Pinecone Inference provides bge-reranker-v2-m3, cohere-rerank-3.5, pinecone-rerank-v0 | Reranking models available through Amazon Bedrock |
| Disaster recovery | Yes, back and restore available per index | Yes, index snapshots stored in Amazon S3 |
Get Started with Pinecone and OpenSearch
It is easy to use Pinecone and OpenSearch side by side. Follow the Getting Started guide if you want to deploy a Pinecone index along with your existing OpenSearch clusters. If you want to migrate from OpenSearch to Pinecone for your vector search, contact us for help.
Get Started
Create your first index for free, then pay as you go when you're ready to scale.