In the age of AI, Retrieval-Augmented Generation (RAG) is king. The engine powering this revolution? The vector database. Choosing the right one is critical for building responsive, accurate, and cost-effective AI applications. But with a growing number of options, which one truly delivers?
To answer this, we put five popular AWS-hosted vector database solutions to the test in a head-to-head performance benchmark. We analyzed latency, consistency, and cost to help you decide which database is the perfect fit for your next project.
The Contenders 🥊
We tested a diverse lineup, ensuring each was configured with comparable compute and memory resources (generally 2 vCPU and 4 GB RAM) for a fair fight.
- Amazon OpenSearch: A powerful, full-featured search and analytics engine with integrated vector search capabilities.
- Aurora PostgreSQL (Provisioned): The reliable relational database workhorse, supercharged with the pgvector extension.
- Aurora PostgreSQL (Serverless v2): The serverless variant of Aurora, promising on-demand scaling and efficiency.
- Amazon MemoryDB for Redis: An in-memory database designed for ultra-low latency, now with vector search.
- ChromaDB on EKS: A popular open-source, purpose-built vector database deployed on a managed Kubernetes cluster.
For context, we also included Google’s Vertex AI Vector Search as a managed service benchmark.
Performance Showdown: The Raw Numbers 📊
We ran a series of queries against each database, keeping the search accuracy (recall) constant at 90% to focus purely on performance. The results speak for themselves.
| Database | Avg Latency (ms) | Std Dev (ms) | Min Latency (ms) | Max Latency (ms) |
| 🏆 MemoryDB | 1.61 | 0.15 | 1.43 | 2.16 |
| ChromaDB | 7.13 | 1.44 | 6.05 | 13.02 |
| Aurora PG (Serverless) | 7.86 | 0.39 | 7.58 | 9.51 |
| Aurora PG (Provisioned) | 11.39 | 1.11 | 10.35 | 14.37 |
| OpenSearch | 40.70 | 18.66 | 30.96 | 120.63 |
| Vertex AI (Benchmark) | 3.65 | 0.13 | 3.39 | 3.87 |
Analyzing the Results: Key Takeaways
🚀 MemoryDB is the Clear Winner
As an in-memory database, MemoryDB’s performance is in a class of its own. It is approximately 4.3 times faster than the next-best alternative (ChromaDB) and is exceptionally consistent, with a minuscule standard deviation of just 0.15 ms. For applications where every millisecond counts, MemoryDB is the undisputed champion.
💪 ChromaDB is an Excellent Self-Hosted Option
As a purpose-built vector database, ChromaDB delivers very strong performance, making it the fastest of the disk-based solutions we tested. Its results prove it’s a top-tier choice for teams looking for a powerful, open-source vector database they can manage themselves within a Kubernetes environment.
✨ Aurora PostgreSQL is a Highly Competitive Alternative
The pgvector extension continues to impress. Aurora PostgreSQL offers performance that is very close to ChromaDB’s but with better consistency (a lower standard deviation). Its versatility as a general-purpose relational database makes it a compelling choice for teams wanting to add vector capabilities without introducing another database into their stack.
😮 Aurora Serverless v2 is a Standout
Perhaps the most surprising insight is the performance of Aurora Serverless v2. It was significantly faster and ~6.7 times more consistent than its provisioned t3.medium counterpart. This showcases its ability to scale capacity instantly to meet query demand, making it a powerful and potentially more cost-effective choice for applications with variable or spiky traffic patterns.
🔍 OpenSearch is a Viable, Full-Featured Option
While it was the slowest in this specific vector search test, its performance is still well within acceptable limits for many applications. OpenSearch’s true strength lies in its ability to combine vector search with its powerful text search, filtering, and analytics capabilities in a single, unified platform.
What About the Cost? 💰
Speed is one thing, but budget is another. We estimated the monthly costs for running each of these services. For context, our current spending on Vertex AI is around €250-€280/month.
| Service | Instance / Configuration | Estimated Monthly Cost (€) |
| Amazon OpenSearch | 1 t3.medium.search node | ~€41 |
| Amazon Aurora (Provisioned) | 1 db.t3.medium instance | ~€66 |
| EKS Cluster (for ChromaDB) | 1 t4g.medium node + Control Plane | ~€94 |
| Amazon Aurora (Serverless v2) | Avg. 1 ACU usage | ~€97 |
| Amazon MemoryDB for Redis | 1 t3.medium node | ~€136 |
This data reveals a fascinating trade-off. OpenSearch is the most budget-friendly, while MemoryDB, the performance king, carries the highest price tag. The serverless Aurora option, despite its superior performance over its provisioned cousin, sits at a competitive price point.
The Verdict: Which Database is Right for You?
There’s no single “best” database—only the best one for your specific use case. Based on our findings, here are our recommendations:
- For blistering speed and the lowest possible latency: Choose Amazon MemoryDB.
- For a powerful, self-hosted, open-source solution: Go with ChromaDB on EKS.
- To add vector search to an existing relational workload: Aurora PostgreSQL with pgvector is an incredibly versatile and competitive choice.
- For unpredictable workloads where performance and cost-efficiency are key: Aurora Serverless v2 is the surprise standout and a fantastic option.
- For a unified platform combining vector search with rich text search and analytics: Amazon OpenSearch is your all-in-one powerhouse.
