I Benchmarked 6 Vector Databases for RAG — Here’s What Surprised Me Most
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I Benchmarked 6 Vector Databases for RAG — Here’s What Surprised Me Most
If you’ve ever worked with LLMs, you’ve probably hit the same wall I did — choosing the right vector database. There are a ton of options, and every one of them claims to be faster, smarter, or more scalable than the rest.
But marketing only takes you so far. When you’re actually building a Retrieval-Augmented Generation (RAG) pipeline, what matters most are latency and relevance — and how they translate into real-world performance.
So I decided to dig in. How does a bare-metal library like FAISS actually compare to a fully managed vector database? And what kinds of challenges come up once you move beyond simple semantic search into hybrid retrieval (BM25 + DPR)?
To find out, I ran a hands-on benchmark with six popular options — FAISS, Qdrant, ChromaDB, Redis, Milvus Lite, and Pinecone — using Gemma 300M embeddings and common index types like FLAT and HNSW-like algorithms.
This post isn’t just a benchmark table. It’s about the three most unexpected lessons I learned — the kind that actually change how I think about RAG architecture choices.
I went into this expecting FAISS to dominate. It’s bare metal, it’s optimized, and it’s been the gold standard for years. And yes, it was the fastest. But the surprise? The gap between FAISS and the newer “lite” or embedded databases was smaller than I expected.
Benchmark Results (Gemma 300M Embeddings, 54 Docs):
- FAISS: 7.33 ms average search latency
- Milvus Lite: 10.62 ms
- Qdrant: 9.29 ms
- ChromaDB: 10.95 ms
- Redis: 15.98 ms
- Pinecone: 12.3 ms
FAISS still came out on top, but Milvus Lite and Qdrant weren’t far behind — both under 11 ms on average. Even Pinecone, despite running as a fully managed cloud service, held its own once I accounted for network latency. Clearly, managed environments have come a long way in optimizing for speed.
The nice part is how accessible these results make RAG prototyping. Milvus Lite and ChromaDB run locally with persistence built in, so I can spin up quick experiments without external dependencies. And when I want a hands-off, scalable setup, Pinecone’s managed model is a strong option. Between these, I get the best of both local and cloud workflows.
Takeaway 2: True Hybrid Search Is Still Rare
In production, pure vector search isn’t enough. The best systems combine semantic embeddings with sparse keyword search — a hybrid approach that blends relevance with precision.
But when I tested for native hybrid support, the results were pretty uneven:
| Database | Native Hybrid Support | Notes |
|---|---|---|
| Qdrant | ✅ Yes | Handles sparse (BM25) + dense fusion natively. |
| Milvus Lite | ⚙️ Partial | Needs external BM25/Whoosh integration. |
| ChromaDB | ⚙️ Partial | Works via external retrievers. |
| Redis | ⚙️ Partial | Only with the RediSearch module. |
| FAISS | ❌ None | You have to fuse BM25 and DPR manually. |
| Pinecone | ⚙️ Partial | Supports “sparse + dense” API, but limited scoring control. |
🧠 Key takeaway: Only Qdrant offers true native hybrid retrieval right now. Pinecone’s “sparse + dense” setup works well for simpler cases but doesn’t allow deep BM25 weighting. Everything else needs external retrievers or manual score fusion.
For me, this made Qdrant a standout. Having hybrid retrieval built in means less glue code, fewer dependencies, and more predictable results — a big deal if you’re building something production-grade.
Takeaway 3: The Best Database Might Already Be in Your Stack
Raw speed isn’t always the deciding factor. Sometimes, the best database is the one that fits your infrastructure with minimal friction.
That realization hit me when comparing Redis and Pinecone.
Redis had the highest search latency (~15.98 ms), but its indexing time (1.631 s) was almost identical to FAISS (1.623 s). Pinecone, on the other hand, adds a bit of latency from API calls, but the trade-offs are clear: instant scalability, built-in replication, and zero maintenance.
So the decision really comes down to priorities:
- Redis is great if you’re already running it and want to keep everything in-house.
- Pinecone shines if you’d rather pay for simplicity and scale without managing servers.
For me, Redis felt like a practical middle ground when I needed tight integration. Pinecone was the easy button when I just wanted things to work.
Wrapping Up: Benchmarks Are Only Half the Story
Choosing a vector database isn’t just about chasing the lowest latency number. It’s about finding the right balance between performance, hybrid capabilities, and integration effort.
Here’s how I’d summarize what I learned:
| Database | Strength | Best For |
|---|---|---|
| FAISS | Fastest dense retrieval | Research or GPU-heavy setups |
| Qdrant | True hybrid + production-ready | Enterprise RAG systems |
| Milvus Lite | Near-FAISS speed + persistence | Local development and prototyping |
| ChromaDB | Simple persistence, easy setup | Quick experiments |
| Redis | Ecosystem integration | Teams already using Redis Stack |
| Pinecone | Fully managed scalability | Cloud-first production pipelines |
My take:
- FAISS is still unbeatable for raw speed.
- Qdrant leads in hybrid capabilities.
- Milvus Lite gives me FAISS-like performance locally.
- ChromaDB makes quick experiments painless.
- Redis wins when it’s already part of your system.
- Pinecone owns the “no-ops” cloud experience.
Ultimately, it all comes down to what you value most:
- Speed? → FAISS or Milvus Lite
- Hybrid intelligence? → Qdrant or Pinecone
- Integration simplicity? → Redis
- Local prototyping? → ChromaDB
- Effortless cloud scaling? → Pinecone
