Pinecone vs Rockset
Compare and contrast Pinecone and Rockset by architecture, ingestion, queries, performance, and scalability.
Pinecone vs Rockset Ingestion
Ingestion
Pinecone
Rockset
Streaming and bulk ingestion
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.
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.
Index updates
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.
Rockset supports in-place updates for vectors and metadata with Rockset's Converged Indexing technology built on mutable RocksDB.
Embedding generation
Pinecone supports API calls to OpenAI, Cohere and HuggingFace to insert and index embeddings.
Rockset can store and process embeddings generated from OpenAI, Cohere, HuggingFace and more.
Size of vectors and metadata
Pinecone supports 40kb of metadata per vector and a maximum vector dimensionality is 20,000. Pod sizes, resource confidurations, are storage bound.
Rockset has a document size of 40MB and supports a vector dimensionality of up to 200,000.
Versioning
There does not appear to be a way to version in Pinecone.
Rockset uses aliases for versioning with no downtime.
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 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 vs Rockset Indexing
Indexing
Pinecone
Rockset
KNN and ANN
Pinecone supports KNN and ANN search. The algorithms leveraged by Pinecone are not documented.
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.
Additional indexes
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.
Rockset builds a Converged Index or a search, ANN, columnar and row index on the data for efficient retrieval.
Vectorization
There is no documentation on vectorization in Pinecone.
All of Rockset's ANN and KNN indexes are vectorized.
Index management
Pinecone handles all index management.
Rockset handles all index creation and management.
Pinecone supports KNN and ANN search. Pinecone supports sparse-dense vectors for hybrid search. 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 vs Rockset Querying
Querying
Pinecone
Rockset
Metadata filtering
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.
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.
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.
Multi-modal models
There is no documentation on multi-modal models in Pinecone.
Rockset enables searches across multiple ANN fields to support multi-modal models.
API (SQL, REST, etc)
Pinecone exposes REST APIs that can be called directly to configure and access Pinecone features.
Rockset supports SQL and REST APIs. Rockset uses query lambdas to generate unique, parameterized API endpoints based on your SQL query.
Pinecone applies a filter during an approximate kNN search. Pinecone supports REST APIs.
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 vs Rockset Ecosystem
Ecosystem
Pinecone
Rockset
Integrations (Huggingface, Langchain, etc.)
Pinecone supports API calls to OpenAI, Cohere and HuggingFace to insert and index embeddings. Pinecone has an integration to Langchain and LlamaIndex.
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 vs Rockset Architecture
Architecture
Pinecone
Rockset
Cloud architecture
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.
Built for the cloud. Indexing and queries can be run on isolated compute clusters (ie: Virtual Instances) for predictable performance at scale.
Scalability
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.
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.
Enterprise readiness
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 used by enterprises at scale including Allianz, JetBlue and Whatnot.
Pinecone is a cloud-service with a tightly-coupled architecture.
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.