pgvector vs Rivault: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of pgvector and Rivault — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
pgvector
pgvector
Open-source Postgres extension that adds a vector column type and vector similarity search for embeddings storage and nearest-neighbor queries.
Key features
- Postgres Extension: Installs as a PostgreSQL extension (CREATE EXTENSION IF NOT EXISTS vector) to add a first-class 'vector' column type directly in the database.
- Vector Column Support: Allows schema-level vector columns (e.g., vector(3)) enabling storage of fixed-dimension embeddings alongside relational data.
- Nearest-Neighbor Queries: Provides SQL-accessible operations to insert vectors and query nearest neighbors for similarity search use cases from within Postgres.
- Multi-language Client Libraries: Maintained official and community client libraries and bindings (Go, Python, Node.js, Rust, .NET, Elixir, C++, etc.) to simplify integration with application code and ORMs.
- CI & Installer Support: Provides GitHub Actions setup and package installation instructions (e.g., package installs for Ubuntu/Postgres runner images) for automated CI or dev environment provisioning.
- Open Source & MIT License: Source code and bindings are available under MIT, enabling self-hosting, modification, and community contributions.
- Postgres extension installable via SQL: CREATE EXTENSION IF NOT EXISTS vector
- Native vector column type (e.g., vector(n)) for storing embeddings
- Nearest-neighbor / similarity query support using Postgres queries
- Language client libraries for Go, Node.js/Deno/Bun/TypeScript, Python, Rust, C++, .NET, Elixir, Nim, and more
- ORM/driver integrations (Go: pgx, pg, Bun, Ent, GORM, sqlx; Node examples include Prisma/Bun integrations)
- Client APIs to create and manipulate vectors (constructors, toArray/values/to_vec helpers)
- Packaging and CI helpers, including GitHub Actions setup and OS packages (example: postgresql-16-pgvector)
- Examples and test workflows (create DB, run migrations, run language-specific tests/examples)
- MIT open-source license and public GitHub repositories for source and examples
Best for
- Semantic Search: Store text or image embeddings in Postgres and run nearest-neighbor queries to implement semantic document search within existing application databases.
- Recommendation Engines: Keep item and user embeddings in the same Postgres instance to compute similarity-based recommendations with SQL queries.
- Augmenting Relational Apps: Add embedding columns to existing relational schemas to combine vector search with relational filters and transactions.
- CI/Dev Automation: Use provided GitHub Actions setup to install pgvector on CI runners or test databases during automated workflows.
- Language-Specific Integration: Use official bindings (e.g., pgvector-go, pgvector-node, pgvector-python) to insert, index, and query vectors from application code and ORMs.
- Prototyping Without New DB: Prototype embedding-driven features without deploying a separate vector database by leveraging existing Postgres infrastructure.
- Semantic search over text/documents by storing and querying embedding vectors inside Postgres
- Recommendation systems that compute nearest neighbors of item embeddings
- Similarity matching for images, audio, or other embedded data types within an existing Postgres-backed app
- Augmenting application databases for retrieval workflows without a separate vector DB
- Integration into CI/CD workflows (GitHub Actions) and language-specific application stacks using provided client libraries
Rivault
Rivault
A zero-knowledge vault that lets AI agents request sensitive data on demand, unlocked by Face ID or passkey and redacted after each task.
Key features
- Zero-knowledge Vault: Sensitive data is encrypted client-side so Rivault never has access to the underlying values by default.
- Per-request Auth: Every agent access triggers a fresh authorization request that you approve with Face ID or a passkey.
- Deterministic Redaction: Data is scoped to a single task and redacted from the agent's context after completion.
- Agent-agnostic Integration: Works with chat-based AI agents and Computer Use Agents that automate browser or desktop flows.
- Ownership of PII: Store SSN, payment info, phone numbers, and emails once and reuse without leaving copies in session logs or memory.
- Biometric Unlock: Face ID or passkey approval keeps the human in the loop for every sensitive field release.
Best for
- Agent-driven Bookings: Approve passport and card details on-demand when an agent books flights or hotels.
- Automated Form Fill: Let CUAs complete government or insurance forms without leaving PII in browser logs.
- Customer Support Delegation: Grant scoped access to account numbers when an agent handles a service call for you.
- Recurring Task Automation: Reuse stored credentials across many agent workflows without pasting them each time.
- Enterprise Agent Deployments: Add a consent + audit layer for teams deploying agents that touch sensitive employee or customer data.