Head to head
MongoDB Atlas Vector Search vs Supabase Vector (pgvector)
As a vector database API, MongoDB Atlas Vector Search rates higher on the APIbenchmarks Index, 86 to 85.2, a 0.8-point gap. Here is how they compare on each criterion.
Criterion by criterion
| Criterion | MongoDB Atlas Vector Search | Supabase Vector (pgvector) |
|---|---|---|
| Documentation & DX | ||
| Reliability | ||
| Ecosystem & SDKs | ||
| Accessibility | ||
| APIbenchmarks Index | 86.0 | 85.2 |
Specifications
| MongoDB Atlas Vector Search | Supabase Vector (pgvector) | |
|---|---|---|
| Best for | Vector search inside operational MongoDB | Postgres-native embeddings, no separate service |
| Free tier | M0 cluster: 512MB storage, shared | Free project: 500MB DB, pgvector included |
| Pricing | Atlas cluster consumption-based | Bundled in Postgres plan (storage-based) |
| Official SDKs | 12 languages | 7 languages |
Is MongoDB Atlas Vector Search better than Supabase Vector (pgvector)?
On the APIbenchmarks Index, MongoDB Atlas Vector Search rates higher (86 vs 85.2). It leads on the four weighted criteria, but price is reported separately, so the best choice still depends on your budget.
Verdict in one line
MongoDB Atlas Vector Search leads by 0.8 points (86 vs 85.2) and wins 2 of 4 criteria; Supabase Vector (pgvector) wins 2.
Winner by criterion
| Criterion | Winner | ||
|---|---|---|---|
| Documentation & DX | 85 | 86 | |
| Reliability | 90 | 80 | |
| Ecosystem & SDKs | 88 | 84 | |
| Accessibility | 80 | 92 |
Choose MongoDB Atlas Vector Search if… / choose Supabase Vector (pgvector) if…
- →reliability matters most to you (90 vs 80)
- →ecosystem & sdks matters most to you (88 vs 84)
- →you need broad SDK coverage (12 official SDKs vs 7)
- →your use case matches its focus: vector search inside operational mongodb
- →documentation & dx matters most to you (86 vs 85)
- →accessibility matters most to you (92 vs 80)
- →your use case matches its focus: postgres-native embeddings, no separate service
Pricing side by side
- Model
- Atlas cluster consumption-based
- Free tier
- M0 cluster: 512MB storage, shared
- Free (M0)$0/mo
- Flex$8-$30/mo
- Dedicated (M10+)from $0.08/hr (~$57/mo)
- Vector Search Nodes (High-CPU)$0.12-$3.26/hr per node
- Vector Search Nodes (Storage-optimized)~$1.04/hr for s50 (2 nodes)
- Model
- Bundled in Postgres plan (storage-based)
- Free tier
- Free project: 500MB DB, pgvector included
- Free$0/mo
- Pro$25/mo per org
- Team$599/mo per org
- EnterpriseCustom
Vendor-published pricing, verified 2026-06-27 (MongoDB Atlas Vector Search) and 2026-06-27 (Supabase Vector (pgvector)).
SDK and language coverage
Both · 3
Python (PyMongo)KotlinSwift
Only MongoDB Atlas Vector Search · 9
Node.js / JavaScriptJavaGoC# / .NETPHPRubyRustScalaC++
Only Supabase Vector (pgvector) · 4
TypeScript / JavaScriptvecs (Python vector client)Dart / FlutterSQL (native pgvector operators)
Strengths and weaknesses
- +Vectors, metadata, and operational data live in one database, eliminating a separate vector store and ETL sync pipeline
- +Native Voyage AI embedding generation auto-creates and syncs embeddings in-database with single-click setup
- +Binary quantization cuts vector RAM ~28x (e.g. 32.6 GB to 5.1 GB) and node cost ~75% while keeping 90-95% recall
- –Pricing is the most common complaint across G2/PeerSpot - expensive and hard to predict versus self-hosting
- –HNSW indexes need ~1.2-1.5x raw vector size in RAM and must fit in memory, making memory planning critical and upgrades costly
- –Production vector workloads require separately-billed Search Nodes with a two-node minimum, adding base cost
- +Embeddings live in the same Postgres as relational data, no separate vector service to provision or keep in sync
- +Combine vector similarity with SQL WHERE filters, joins, and row-level security in a single query
- +HNSW indexing (pgvector 0.5.0+) reaches accuracy@10 of 0.99 at 1M vectors and beat Qdrant on equivalent compute in Supabase's benchmarks
- –HNSW index builds are memory-bound and can take hours on a 10M-row, 1536-dim table, producing tens-of-GB indexes
- –Good query latency requires the index to fit in RAM, pushing you to larger, costlier compute add-ons
- –Performance degrades and p99 tail latency grows beyond roughly 5–50M vectors on a single node, not ideal for billion-scale search
MongoDB Atlas Vector Search vs Supabase Vector (pgvector): questions
- Which has better documentation, MongoDB Atlas Vector Search or Supabase Vector (pgvector)?
- Supabase Vector (pgvector) scores higher on documentation and developer experience: 86 vs 85 out of 100.
- Which is more reliable, MongoDB Atlas Vector Search or Supabase Vector (pgvector)?
- MongoDB Atlas Vector Search scores higher on reliability: 90 vs 80 out of 100.
- Do MongoDB Atlas Vector Search and Supabase Vector (pgvector) have a free tier?
- MongoDB Atlas Vector Search: yes (M0 cluster: 512MB storage, shared). Supabase Vector (pgvector): yes (Free project: 500MB DB, pgvector included).
- How are MongoDB Atlas Vector Search and Supabase Vector (pgvector) priced?
- MongoDB Atlas Vector Search: Atlas cluster consumption-based. Supabase Vector (pgvector): Bundled in Postgres plan (storage-based).
- Which has more official SDKs?
- MongoDB Atlas Vector Search lists 12 official SDKs, Supabase Vector (pgvector) lists 7.
Other Vector Database comparisons
MongoDB Atlas Vector Search vs PineconePinecone vs Supabase Vector (pgvector)MongoDB Atlas Vector Search vs Zilliz Cloud (Milvus)MongoDB Atlas Vector Search vs Qdrant CloudMongoDB Atlas Vector Search vs Weaviate CloudMongoDB Atlas Vector Search vs TurbopufferSupabase Vector (pgvector) vs Zilliz Cloud (Milvus)Qdrant Cloud vs Supabase Vector (pgvector)