See Rockset
in action

Get a product tour with a Rockset engineer

Rockset vs Pinecone

Compare and contrast Rockset and Pinecone by architecture, ingestion, queries, performance, and scalability.

Rockset vs Pinecone Ingestion

Ingestion
Rockset
Pinecone
Streaming and bulk ingestion
Rockset supports streaming and bulk data ingestion. Bulk ingestion is used for an initial load of data into the system and uses temporary compute to process incoming data. Rockset supports streaming ingestion and can ingest and index high-velocity event and CDC streams within 1-2 seconds.
Pinecone supports inserting vectors in batches of 100 vectors or fewer with a maximum size per upsert request of 2MB. Pinecone cannot perform reads and writes in parallel and so writing in large batches can impact query latency and vice versa.
Index updates
Rockset supports in-place updates for vectors and metadata with Rockset's Converged Indexing technology built on mutable RocksDB.
Pinecone offers two methods for updating vectors: full and partial updates. Full updates modify the entire dataset and a partial update will update specific fields using a unique identifier.
Embedding generation
Rockset can store and process embeddings generated from OpenAI, Cohere, HuggingFace and more.
Pinecone supports API calls to OpenAI, Cohere and HuggingFace to insert and index embeddings.
Size of vectors and metadata
Rockset has a document size of 40MB and supports a vector dimensionality of up to 200,000.
Pinecone supports 40kb of metadata per vector and a maximum vector dimensionality is 20,000. Pod sizes, resource confidurations, are storage bound.
Versioning
Rockset uses aliases for versioning with no downtime.
There does not appear to be a way to version in Pinecone.

Rockset is built for streaming data and is a mutable database, supporting in-place updates for vectors and metadata. As a real-time search and analytics database, Rockset supports searches across large-scale data, including vector embeddings and metadata.

Pinecone supports batch insertion of vectors and updates and in-place updates for vectors and metadata. Pinecone supports searches across high dimensional vector embeddings.


Rockset vs Pinecone Indexing

Indexing
Rockset
Pinecone
KNN and ANN
Rockset supports KNN and ANN search. Rockset is built to be algorithm agnostic and currently build a distributed FAISS index for scalability. Rockset uses its cost-based optimizer to tradeoff between KNN and ANN search for greater efficiency. At query time, metadata on indexes is accessed to determine where the ANN index is stored for more efficient retrieval. This architecture avoids extensive memory overhead found in other solutions and limitations on metadata filtering.
Pinecone supports KNN and ANN search. The algorithms leveraged by Pinecone are not documented.
Additional indexes
Rockset builds a Converged Index or a search, ANN, columnar and row index on the data for efficient retrieval.
Pinecone supports the creating a single sparse-dense vector for hybrid search. The sparse vector is used for text search and includes support for BM25 algorithms. Because this is a single vector there's no ability to independently weight the sparsity or density of the coordinates of the vector.
Vectorization
All of Rockset's ANN and KNN indexes are vectorized.
There is no documentation on vectorization in Pinecone.
Index management
Rockset handles all index creation and management.
Pinecone handles all index management.

Rockset supports KNN and ANN search using FAISS indexing algorithms. Rockset consolidates search, vector search, columnar and row indexes into a Converged Index to support a wide range of query patterns out of the box. Vectorization is used to speed up query execution

Pinecone supports KNN and ANN search. Pinecone supports sparse-dense vectors for hybrid search. Pinecone handles all index management.

See Rockset in action
Get a product tour with a Rockset engineer.

Rockset vs Pinecone Querying

Querying
Rockset
Pinecone
Metadata filtering
Rockset supports metadata filtering and hybrid search. Rockset's cost-based optimizer determines the most efficient path to query executing, either pre-filtering using metadata or applying the filter during the approximate kNN search.
Pinecone supports metadata filtering and hybrid search. Pinecone filters are applied during the approximate kNN search.
Pinecone supports a limited number of metadata field types. It recommends avoiding indexing high-cardinality metadata as that will consume significantly more memory. The maximum results a query will return with metadata filtering is 1,000.
Multi-modal models
Rockset enables searches across multiple ANN fields to support multi-modal models.
There is no documentation on multi-modal models in Pinecone.
API (SQL, REST, etc)
Rockset supports SQL and REST APIs. Rockset uses query lambdas to generate unique, parameterized API endpoints based on your SQL query.
Pinecone exposes REST APIs that can be called directly to configure and access Pinecone features.

Rockset supports pre-filtering and applying a filter during an approximate kNN search. Rockset supports SQL and REST APIs. Rockset applies a filter during an approximate kNN search.

Pinecone applies a filter during an approximate kNN search. Pinecone supports REST APIs.


Rockset vs Pinecone Ecosystem

Ecosystem
Rockset
Pinecone
Integrations (Huggingface, Langchain, etc.)
Rockset can store and process embeddings generated from OpenAI, Cohere, HuggingFace and more. Rockset has an integration to LangChain and LlamaIndex. Rockset also offers built-in connectors to event streaming platforms (Kafka, Kinesis, etc.), OLTP databases (MongoDB, DynamoDB, etc.) and data lakes (S3, GCS, etc.).
Pinecone supports API calls to OpenAI, Cohere and HuggingFace to insert and index embeddings. Pinecone has an integration to Langchain and LlamaIndex.

Rockset vs Pinecone Architecture

Architecture
Rockset
Pinecone
Cloud architecture
Built for the cloud. Indexing and queries can be run on isolated compute clusters (ie: Virtual Instances) for predictable performance at scale.
Pinecone is a cloud-based service deployed partly on Kubernetes with a tightly coupled architecture. Each pod, configuration of resources, has one or more replicas and provides the RAM, CPU and SSD required.
Scalability
Rockset is the only search and analytics database with compute-storage and compute-compute separation. The bulk and streaming ingestion and indexing of vector embeddings is fully isolated from the compute and RAM used for query serving. This removes resource contention between the two workloads. Furthermore, Rockset separates hot storage from compute so you are not bound by the size of your vector embeddings in increasing the size of your cluster. Rockset can scale up and down on demand for better price-performance.
Pinecone offers a number of pods, or resource configurations, that can be picked depending on the performance requirements of the vector search. Pods are storage bound so once you cross a threshold you will scale your pod size up (1x, 2x, 4x and 8x) without downtime. It is not possible to scale a pod size down. You can also horizontally scale by adding more pods but this will pause new inserts and index creation. You can also add replicas to increase QPS.
Enterprise readiness
Rockset is used by enterprises at scale including Allianz, JetBlue and Whatnot.
Pinecone does not have case studies of enterprises using their product in production. It reported a partial database outage on March 1st, 2023.

Rockset is built for the cloud and separates compute-storage and compute-compute. The compute used for ingestion and indexing of vector embeddings is isolates from the compute used for query serving. Rockset is used by enterprises including Allianz, JetBlue and Whatnot.

Pinecone is a cloud-service with a tightly-coupled architecture.

See Rockset in action
Sub-second SQL on streaming data with surprising efficiency.