Pinecone vs. SingleStore

What is SingleStore?

SingleStore is the world's only real-time data platform. It can read, write and reason on petabyte-scale data in a few milliseconds. At its core is a high-performance, SQL database engine that allows you to run transactions, analytics and vector search— eliminating the need for multiple databases and unnecessary data movement. SingleStore delivers high throughput ingestion from distributed data sources while serving single-digit millisecond queries, making it an ideal platform for real-time analytics and gen AI.

Highlights

Pinecone

Designed for scalable, high-performance similarity search
Fully managed cloud service, simplifying deployment and scaling
Suitable for AI-powered search and recommendation systems
Being a specialized service, it might not cover broader database functionalities
Relatively newer product and struggles with production database grade features
Potential dependency on cloud infrastructure and related costs

SingleStore

Ingestion performance. Parallel high-throughput parallel ingestion — up to millions of events per second
Analytical performance. High performance and scalability with millisecond query response times; supports complex queries
Vector search. Support for both KNN and ANN and makes it easy for developers to do vector and full-text search with SQL.
CRUD. SingleStore is suitable for both OLTP and OLAP workloads. It stores data in patented row and column-based storage, making it extremely capable for both transactional and analytics use cases. SingleStore’s columnstore supports point updates, row-level locking and high-volume concurrent writes, upserts and deletes.
Multi-model. Support for SQL, JSON/BSON (MongoDB® API compatible), geospatial, key-value and time-series.
Bottomless storage and distributed architecture. Three-tier storage architecture with in-memory rowstore, on-disk columnstore and cloud object storage enabling separation of compute and storage; shared-nothing architecture that allows scaling to thousands of concurrent user sessions.
Deployment. Fully managed platform-as-a-service on all three hyperscaler clouds (AWS, Azure, GCP), and self-managed offering for Kubernetes/on-prem deployment
Data platform. Includes native data integration, compute services and Notebooks (SQL/Python/R).
Not ideal for pure transactional workloads (with requirements like foreign-keys, multi-master or geo-partitioning)
Not ideal for purely batch analytics
Not ideal for very small scale datasets (<1 GB)

Technical comparison

Performance

Ingestion throughput and query performance

Capability	Pinecone	SingleStore
Ingestion performance	⬤	⬤⬤⬤⬤
	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, so writing in large batches can impact query latency and vice versa.	SingleStore Pipelines offer streaming, parallelized data ingestion with optional transforms from multiple data sources such as Amazon S3, Kafka, HDFS and Iceberg. Freshly ingested data is immediately queryable.
Query performance	N/A	⬤⬤⬤⬤
	Not applicable. Pinecone cannot be used for analytical queries on relational data. For vector-only workloads, Pinecone's performance is on par with other vector-only databases.	SingleStore supports low-latency analytics (~10s of milliseconds) on complex queries (involving aggregates, joins, filters, etc.)
High Concurrency	N/A	⬤⬤⬤⬤
	Information about Pinecone's access concurrency is not available.	SingleStore's distributed SQL architecture can scale to thousands of concurrent user sessions

Functionality

Build quickly

Capability	Pinecone	SingleStore
Multi-model	⭘	⬤⬤⬤⬤
	Pinecone can store vector embeddings. It is not a multi-model database.	In addition to relational data, SingleStore supports JSON, vectors, text, time-series, geospatial and key-value data. Notably, SingleStore does not support graph data — however it supports Recursive CTEs that can be used to query hierarchical data using standard SQL.
Notebooks	⭘	⬤⬤⬤⬤
	Pinecone does not include native Jupyter notebooks.	SingleStore includes native Notebooks that allow developers and data scientists to easily write and collaborate on SQL or Python code.
Vector Search	⬤⬤⬤⬤	⬤⬤⬤⬤
	Pinecone supports indexed approximate nearest neighbor (ANN) search. Distance metrics include cosine, dot product and euclidean distance.	SingleStore includes both exact K Nearest Neighbor (KNN) and Indexed Approximate Nearest Neighbor (ANN). Distance metrics include dot product and euclidean distance.
Hybrid Search	⬤⬤	⬤⬤⬤⬤
	Pinecone supports sparse and dense vectors, a capability it terms as hybrid search. It does support keyword search as well, however it lacks full-text search engine capabilities like complex queries, ranking and relevance.	SingleStore supports hybrid search, allowing for the effective combination of vector search + full-text search results. Its powerful SQL interface enables using filters, aggregations and joins along with hybrid search.
Compute service for ML	⭘	⬤⬤⬤⬤
	A native compute service for co-located workloads is not available with Pinecone. Users must deploy, configure and maintain their own compute clusters and VPCs.	SingleStore Aura compute service [preview] allows you to deploy scalable compute in a secure manner right next to your real-time application for AI/ML/ data prep workloads.
Ecosystem	⬤	⬤⬤⬤
	Founded in 2020, the community of Pinecone is still small compared even to other vector databases, especially those that are open sourced.	With MySQL wire-compatibility, SingleStore benefits from an extremely large MySQL ecosystem. In addition, SingleStore has a large and growing set of native integrations for tools, connectors, frameworks, etc. Learn more.

Operational Simplicity

Minimize costs, complexity and risks

Capability	Pinecone	SingleStore
Decoupled Storage and Compute	⬤⬤⬤	⭘
	Resources cannot be independently adjusted to meet specific workload demands.	SingleStore's three tiered "bottomless architecture" includes an in memory rowstore, a disk-based persistent cache/ columnstore and a cloud object storage. This separation of storage and compute enables supporting growing data volumes using low-cost storage infrastructure.
High Availability	⬤	⬤⬤⬤⬤
	Although Pinecone claims 99.9% uptime SLA, its customers have complained about complete data loss and severely long RTOs.	SingleStore provides up to 99.99% uptime SLA and includes support for Point-in-Time Recovery (PITR). This allows customers to run critical applications and workloads, and helps mitigate risks on business operations and reputation.
Auto Scaling	⭘	⬤⬤⬤⬤
	Scale up and scale out techniques can be used, however auto-scaling is not available.	Auto-scaling in SingleStore allows you to quickly and automatically adjust compute resources based on changing workload demands.
HTAP	⭘	⬤⬤⬤⬤
	Pinecone is purely a vector database and not suitable for transactions or analytics.	SingleStore Universal Storage is a unique, unified table type to handle both transactional and analytical workloads. This minimizes complexity and costs emanating from multiple databases, compute and license costs on ETL and data duplication.
Deployment Optionality	⬤⬤	⬤⬤⬤⬤
	Only available as a fully managed service.	SingleStore can be deployed as a fully managed cloud service or self-managed, either on-premises or on public cloud infrastructure.
Open-source software	⭘	⭘
	Pinecone operates under proprietary licensing and may also offer limited trial versions of its product. Costs for Pinecone tend to skyrocket, particularly in high-concurrency use cases as usage scales.	SingleStore is wire-compatible with MySQL and MariaDB. However it is a commercial, enterprise-grade database. It offers a free tier for development use.