Zilliz Cloud vs pgvector
- ✦ Open Source (Apache 2.0)
- ✦ Distributed Architecture
- ✦ Billion-Scale Vectors
- ✦ PostgreSQL Extension
- ✦ Exact Nearest Neighbor
- ✦ Approximate Nearest Neighbor (IVFFlat, HNSW)
Zilliz Cloud and pgvector are both Vector Databases tools. Compare features, pricing, and ratings below to find the best fit for your team.
When to Choose Zilliz Cloud vs pgvector
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
Milvus scales to trillions of vectors using hierarchical index structures with tiered storage, serving billion-scale collections that exceed single-machine memory limits via Zilliz Cloud managed deployment.
pgvector stores embeddings as a native column type and queries them with standard SQL, avoiding the operational complexity of a separate vector database for applications already running on Postgres.
Milvus GPU indexing builds IVFPQ indexes on billion-vector collections 10x faster than CPU-only builds, reducing the time from ingestion to searchable index for large-scale embedding pipelines.
pgvector's HNSW index achieves sub-50ms similarity search for collections under 10M vectors, covering most product recommendation and semantic search use cases without a specialized vector database.
Milvus partitions split a collection by tenant key, improving query isolation and allowing partition-level operations like load/release to balance memory usage across a shared cluster.
pgvector writes and deletes embeddings within standard Postgres transactions, ensuring vector index and application data never diverge in multi-step operations that require rollback.
Pricing Comparison & PlansHigh· Verified Jul 8, 2026
Zilliz Cloud Plans
Free tier available
Free
FreeBest for: Learning & personal projects
- ✓5 GB storage
- ✓2.5M vCUs/mo included
- ✓Up to 5 collections
- ✓Serverless
Standard (Dedicated)
from $126/GB/moBest for: Prototypes & testing (non-critical)
- ✓From $0/mo serverless OR $126/GB-mo dedicated
- ✓Fully managed core APIs
- ✓Backup & restore
- ✓Basic monitoring
- ✓Encryption in transit + at rest
Enterprise (Dedicated)
from $197/moBest for: Production applications
- ✓99.95% uptime SLA
- ✓Audit logs
- ✓SSO (SAML 2.0)
- ✓Granular RBAC
- ✓Multi-replica + elastic scaling
Business Critical
Contact SalesBest for: Healthcare / finance / regulated mission-critical
- ✓Global cluster HA + DR
- ✓CMEK + full-path encryption
- ✓HIPAA-eligible
- ✓Priority support
pgvector Plans
Free tier available
Open Source
Open SourceBest for: You get a fully-featured, self-hosted PostgreSQL extension for vector search
- ✓Vector similarity search (L2 distance, inner product, cosine distance, L1 distance)
- ✓Exact and approximate nearest neighbor search
- ✓HNSW (Hierarchical Navigable Small World) indexing
- ✓IVFFlat (Inverted File with Flat Compression) indexing
- ✓ACID compliance and transactional safety via PostgreSQL
Capability Breakdown
7 differences found across 14 standardized features
- •Open Source (Apache 2.0)
- •Distributed Architecture
- •Billion-Scale Vectors
- •ANNS Algorithms (HNSW, IVF, DiskANN)
- •Hybrid Search
- •GPU Acceleration
- •Multi-vector Search
- •Sparse Vector Support
- •Metadata Filtering
- •Data Partitioning
- •Incremental Indexing
- •Python/Java/Go SDKs
- •Kubernetes Native
- •LangChain Integration
- •Time Travel (data versioning)
- •RBAC
- •PostgreSQL Extension
- •Exact Nearest Neighbor
- •Approximate Nearest Neighbor (IVFFlat, HNSW)
- •L2 / Cosine / Inner Product Distance
- •Indexing for Large Datasets
- •SQL Query Interface
- •JOIN with Relational Data
- •ACID Transactions
- •Standard PostgreSQL Tooling
- •Works with Supabase/RDS/Neon
- •pgvectorscale Extension
- •Python/JS/Ruby Support
- •Concurrent Queries
- •Type Safety
- •Open Source (MIT)
- •No Additional Ops
Strengths & Limitations
Evaluative strengths and weaknesses: not feature lists
- +CNCF project: battle-tested for billion-scale workloads
- +GPU acceleration and DiskANN for cost-efficient large-scale search
- +Distributed architecture with independent storage/compute scaling
- +Multi-vector search supports complex AI use cases
- −Operational complexity: requires Kubernetes expertise for self-hosted
- −Overkill for small-scale RAG applications
- +No new infrastructure: runs inside existing PostgreSQL
- +SQL interface familiar to every developer
- +ACID transactions across vectors and relational data
- +Works on Supabase, RDS, Neon: all managed PG providers
- −Performance trails purpose-built vector DBs at 10M+ vectors
- −No distributed vector search without manual sharding
At a Glance
Recent Price History
Zilliz Cloud added a new "Standard (Dedicated)" plan at $126/GB/mo
Plan added · Jul 6, 2026
Zilliz Cloud added a new "Enterprise (Dedicated)" plan at $197/mo
Plan added · Jul 6, 2026
Zilliz Cloud added a new "On-demand Compute" plan (Custom pricing)
Plan added · Jul 6, 2026
Zilliz Cloud removed the "Serverless" plan
Plan removed · Jul 6, 2026
Zilliz Cloud removed the "Dedicated Business Critical" plan
Plan removed · Jul 6, 2026
Frequently Asked Questions
Related Comparisons
Zilliz CloudFree · from $126/mo
pgvectorFree planSources & Data Trail · Zilliz Cloud
- 1.Official Pricing Page·Source of verified tiers(Checked: 2026-07-08)
- 2.Official Website·Official vendor website
- 3.G2·G2 verified reviews · 4.4/5 · 53 reviews
Sources & Data Trail · pgvector
- 1.Official Website·Official vendor website
- 2.G2·G2 verified reviews · 3.8/5 · 12 reviews