| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes sub-10ms vector similarity search on pre-computed embeddings using approximate nearest neighbor (ANN) algorithms with a two-tier memory architecture: hot data cached in NVMe SSD/memory for p50 latency of 8ms, cold data retrieved from S3 object storage on first access. Supports topk result limiting and operates at scale across 500M+ documents per namespace with observed throughput of 25k+ queries/second.
Unique: Separates compute and storage layers with S3-backed tiered caching (NVMe SSD + memory for hot data, object storage for cold), enabling 10x cost reduction vs alternatives while maintaining sub-10ms p50 latency on warm queries through intelligent cache management rather than keeping all vectors in-memory
vs alternatives: Cheaper than Pinecone/Weaviate at scale because it uses S3 for persistent storage instead of expensive managed vector storage, while maintaining competitive latency through SSD caching for frequently accessed namespaces
Performs keyword-based document retrieval using BM25 ranking algorithm combined with optional metadata filtering to narrow result sets by document attributes. Operates independently from vector search or in hybrid mode, with measured p50 latency of 343ms on warm namespaces. Metadata filter syntax and exact filtering capabilities are undocumented but support structured attribute-based result narrowing.
Unique: Integrates BM25 full-text search as a first-class capability alongside vector search within the same API, enabling hybrid search queries that combine both ranking signals without requiring separate search infrastructure or post-processing to merge results
vs alternatives: Simpler than maintaining separate Elasticsearch/Meilisearch instances for keyword search because full-text and vector search are unified in a single API with shared namespace isolation and S3 storage
Secures API access using API key-based authentication with undocumented header format and encoding. Supports role-based access control (RBPR) at Scale tier with SSO (single sign-on), and fine-grained permissions at Enterprise tier. Specific authentication mechanisms, token formats, and permission models are completely undocumented.
Unique: Tiered authentication where Launch uses basic API keys, Scale adds RBAC and SSO, and Enterprise adds fine-grained permissions, but all authentication mechanisms are undocumented making integration difficult
vs alternatives: unknown — cannot compare authentication security or usability to alternatives without API specification
Supports deployment across multiple AWS regions with data residency controls, but specific regions, latency characteristics, and failover behavior are completely undocumented. Region selection appears to be tied to S3 bucket location.
Unique: unknown — insufficient data on region availability, replication strategy, and failover behavior
vs alternatives: unknown — cannot assess multi-region capabilities without documentation
Provides tiered support with Launch offering community support, Scale offering 8-5 business hours support with private Slack channel, and Enterprise offering 24/7 support with 99.95% uptime SLA. Specific response times, escalation procedures, and SLA terms are undocumented.
Unique: Tiered support model where Launch includes community support, Scale adds business hours support with private Slack, and Enterprise adds 24/7 support with 99.95% SLA, but SLA terms and support response times are undocumented
vs alternatives: More accessible than Pinecone for startups because Launch tier includes community support, though 24/7 support requires Enterprise tier like most SaaS products
Executes simultaneous vector and full-text search queries and combines their ranking signals to produce a unified result set that balances semantic similarity with keyword relevance. Implementation details of ranking combination (weighted sum, learning-to-rank, etc.) are undocumented, but enables use cases requiring both semantic and keyword precision without separate round-trips.
Unique: Provides native hybrid search combining vector and full-text signals in a single query without requiring application-level result merging or separate API calls, with unified ranking across both modalities within the same namespace isolation model
vs alternatives: More efficient than querying vector and full-text search separately and merging results in application code because ranking is unified server-side, reducing latency and eliminating deduplication logic
Isolates documents and queries into logical namespaces, enabling secure multi-tenant deployments where each tenant's data is completely segregated at the API level. Supports up to 100M+ namespaces with independent vector/full-text indexes, metadata schemas, and cache policies. Namespaces can be pinned (up to 256) to keep data in warm cache, or unpinned to use cold S3 storage for cost optimization.
Unique: Implements namespace-based isolation with optional pinning to control which tenants' data stays in warm cache vs cold S3, enabling fine-grained cost optimization where high-value tenants get guaranteed low latency while others use cheaper cold storage
vs alternatives: More cost-efficient than per-tenant Pinecone instances because multiple tenants share infrastructure with namespace isolation, and pinning allows selective warm caching instead of keeping all data hot
Stores all vector and document data durably in AWS S3 object storage while maintaining a two-tier cache layer (NVMe SSD + memory) for hot data. On first query to a namespace, data is loaded from S3 into cache; subsequent queries hit the faster cache layer. Namespaces can be explicitly pinned to keep data in warm cache, or unpinned to allow cache eviction and S3 fallback for cost savings.
Unique: Decouples compute and storage by using S3 as the durable backend with intelligent tiered caching (NVMe SSD + memory) for hot data, enabling 10x cost reduction vs in-memory vector databases while maintaining sub-10ms latency for frequently accessed data through automatic cache management
vs alternatives: Cheaper than Weaviate/Milvus at scale because persistent storage is S3 (pay-per-GB) instead of expensive managed storage, while SSD caching prevents S3 latency from impacting warm queries
+5 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Turbopuffer scores higher at 56/100 vs Supabase at 42/100. Turbopuffer leads on adoption and quality, while Supabase is stronger on ecosystem. However, Supabase offers a free tier which may be better for getting started.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities