Supabase pgvector vs Pinecone RAG Retrieval Accuracy: 512 vs 1024 Chunk Size Results

Chunk size is one of those RAG configuration choices that looks boring until you see a 23% accuracy gap show up in your evals. That’s not a rounding error — that’s the difference between a chatbot that answers correctly and one that confidently hallucinates.

The question of Supabase pgvector vs Pinecone retrieval accuracy across chunk sizes has become genuinely pressing in 2026. RAG deployments scaled hard across enterprise software last year, and teams are now hitting the wall where “good enough” infrastructure stops being good enough. The choice between pgvector inside your Postgres instance and Pinecone as a managed vector database isn’t just an ops decision anymore — it directly shapes retrieval quality, latency, and the chunk size configurations that perform best in each system.

The core argument: pgvector and Pinecone don’t just differ in cost structure. They perform differently because of their architecture, and that architectural gap means optimal chunk sizes diverge between them.

In brief: Pinecone’s ANN index delivers faster retrieval at scale while pgvector’s exact search trades speed for precision, and this tradeoff shifts the optimal chunk size in each system. Chunk size 512 consistently outperforms 1024 in recall accuracy across both platforms, but the gap is larger on pgvector.

Three specific points this covers:

1. Why chunk size affects retrieval accuracy differently across vector stores
2. What the benchmark data actually shows for 512 vs 1024 tokens
3. How to pick the right stack given your query volume and accuracy requirements

Key Takeaways

• Chunk size 512 outperforms 1024 in RAG retrieval accuracy on both pgvector and Pinecone. pgvector shows the larger accuracy delta between the two settings — approximately 18–23% in recall@5 metrics, per Kalvium Labs testing.
• Pinecone’s HNSW-based approximate nearest neighbor search delivers sub-10ms p99 latency at 1M+ vectors, while pgvector with IVFFlat indexes typically runs 40–120ms at equivalent scale, according to Vecstore’s 2025 benchmark dataset.
• Supabase pgvector is genuinely competitive for datasets under 500K vectors and teams already on Postgres — the operational cost savings are real, but they come with retrieval accuracy trade-offs at scale.
• The 512 vs 1024 experiment result isn’t a single answer. It’s highly sensitive to embedding model, document type, and query distribution.

Why This Comparison Matters Now

RAG went from research prototype to production workhorse between 2023 and 2025. By early 2026, Gartner estimates over 60% of enterprise LLM applications use some form of retrieval augmentation. That adoption curve created a specific problem: teams built fast, chose vector stores based on tutorials, and are now debugging accuracy issues they don’t fully understand.

Supabase pgvector became popular because it sits inside Postgres. If your application data is already there, adding a vector column feels like zero infrastructure overhead. Pinecone took a different path — purpose-built vector database, managed service, HNSW indexing optimized for high-dimensional similarity search.

The chunk size question got serious when teams noticed their RAG pipelines returning semantically adjacent but contextually wrong passages. A 1024-token chunk from a legal document might contain the right answer and three misleading qualifications — and the embedding averages across all of them. A 512-token chunk is more likely to be topically coherent.

Kalvium Labs published production RAG findings in late 2025 showing that chunking strategy was the single highest-impact variable in retrieval quality — higher than embedding model choice for documents under 50 pages. That finding shifted the conversation from “which vector DB?” to “which vector DB at which chunk size?”

Chunk Size 512 vs 1024: What the Retrieval Data Actually Shows

The Kalvium Labs experiment ran retrieval evaluation on a mixed corpus — technical docs, legal contracts, support FAQs — using both 512 and 1024 token chunks with 50-token overlap, across both pgvector and Pinecone. The metric: Recall@5, meaning whether the correct passage appeared in the top 5 retrieved chunks.

The results were unambiguous. 512-token chunks outperformed 1024 on Recall@5 across both platforms. On Pinecone: 512 tokens scored ~82% vs ~71% for 1024. On pgvector: 512 tokens scored ~79% vs ~61% for 1024. The gap is wider on pgvector. That matters.

Why does this happen? Longer chunks produce denser, more averaged embeddings. When a 1024-token passage covers two subtopics, its vector sits between both in embedding space — not close enough to either query to rank in the top 5. Pinecone’s HNSW graph search handles this slightly better than pgvector’s IVFFlat because HNSW maintains more granular neighborhood connections. But neither system rescues a bad chunking strategy.

Chunk size is upstream of your vector store choice. Fix that first.

Latency and Scale: Where the Architecture Gap Opens Up

Vecstore’s 2025 benchmark compared pgvector (IVFFlat, lists=100), pgvector (HNSW), and Pinecone across dataset sizes from 100K to 5M vectors.

Source: Vecstore vector database performance benchmarks, 2025

pgvector HNSW closes the latency gap significantly versus IVFFlat. At 1M vectors it’s 45ms p99 vs Pinecone’s 9ms — still 5× slower, but workable for async or batch retrieval. At 5M vectors, that 180ms vs 11ms gap starts breaking user-facing applications.

The 512-token chunk configuration compounds this. More chunks mean more vectors. A corpus that produces 200K vectors at 1024 tokens produces roughly 380K at 512 — nearly double. On pgvector, that scale increase hits latency hard. On Pinecone, it barely moves the needle.

This isn’t always the answer either direction. The architecture that wins depends heavily on where your corpus sits today and where it’ll be in 12 months.

Three Scenarios, Three Recommendations

Scenario 1 — Early-stage product, Postgres already in your stack. Use Supabase pgvector with 512-token chunks and HNSW indexing. The operational simplicity is real — one connection string, one backup strategy, no new vendor relationship. Run with ef_construction=64 and m=16 for HNSW. Monitor Recall@5 in your evals as the corpus grows past 300K vectors. That’s your migration trigger.

Scenario 2 — Production RAG at 1M+ vectors with a sub-50ms SLA. Pinecone with 512-token chunks, 50-token overlap. The managed infrastructure removes index tuning from your team’s backlog entirely. Pinecone’s serverless tier, launched in late 2024, reduced the cost barrier significantly — you’re paying for what you query, not what you store idle.

Scenario 3 — Hybrid: transactional data plus vector retrieval. This is where pgvector wins on architecture, not raw performance. Joining retrieved vectors with user-specific Postgres rows in a single query is genuinely hard to replicate with Pinecone and a separate relational database. If your RAG needs to filter by user permissions, subscription tier, or row-level data, pgvector’s SQL-native context is worth the retrieval accuracy trade-off at moderate scale.

This approach can fail when corpus growth outpaces your Postgres instance’s memory headroom. HNSW indexes are RAM-hungry, and teams underestimate that cost until query latency starts spiking at 2 a.m.

What’s Coming That Changes the Calculus

Two developments worth tracking:

pgvector 0.8.x, expected mid-2026, is targeting HNSW improvements specifically for filtered search. If that ships as described, it could close the accuracy gap for permission-scoped retrieval — which is the main reason teams choose pgvector over Pinecone in hybrid architectures.

Pinecone’s sparse-dense hybrid search, generally available since Q1 2026, changes the chunk size calculus. Keyword signals compensate for some of the context loss at 512 tokens, which means the accuracy penalty for smaller chunks shrinks further. That tilts the recommendation even harder toward 512 across both platforms.

And semantic chunking — using sentence boundary detection rather than fixed token counts — is already outperforming both 512 and 1024 fixed sizes in early 2026 evaluations from LlamaIndex’s benchmark suite. Fixed token chunking may be the interim answer, not the permanent one.

The Bottom Line

The 512 vs 1024 experiment lands on a few durable conclusions:

• 512-token chunks outperform 1024 on Recall@5 across both platforms — by 11 percentage points on Pinecone and 18 points on pgvector
• pgvector HNSW is viable under 500K vectors; beyond that, Pinecone’s latency consistency is hard to argue against
• The right chunk size depends on corpus structure — FAQ-style docs favor 512, long-form narrative content sometimes benefits from 768 as a middle ground
• Cost isn’t free at scale — doubling vector count with 512 tokens has infrastructure implications that matter significantly more on pgvector than Pinecone

Start with 512 tokens. Profile your actual Recall@5. Let the data tell you whether your scale justifies moving from pgvector to Pinecone. Intuition gets you started. Evals keep you honest.

What chunk size are you running in production — and have you benchmarked it against a smaller alternative?

References

1. RAG in Production: pgvector vs Pinecone, Embeddings & Chunking | Kalvium Labs
2. r/Rag on Reddit: Overwhelmed by RAG (Pinecone, Vectorize, Supabase etc)
3. Vector Database Performance Compared: pgvector vs Pinecone vs Qdrant vs Weaviate - Vecstore

Photo by Microsoft Copilot on Unsplash