After 8 months with Milvus: it’s serviceable for specific use-cases but maddeningly inconsistent for larger projects.
As a developer in the field of data-driven applications, finding the right solutions for managing and querying vector data has been crucial. I started using Milvus about 8 months ago in a project focused on image and text similarity search. My experience has been mixed, given that I had to scale up from a small proof of concept to a more demanding production environment. Milvus, which has gained significant traction in recent times (43,404 stars and 3,904 forks on GitHub), has strengths, but like anything else, it has weaknesses too. here, I’ll talk about the best Milvus alternatives for 2026 that I’ve tested firsthand.
Context: My Experience with Milvus
During my time with Milvus, I primarily used it for a project that required a real-time image and text similarity search engine. Given the scale of our application, which processes hundreds of thousands of queries per day and manages over a million vectors, I put Milvus to the test against various workloads and performance benchmarks. Initially, it met my needs as a developer; however, as we attempted to expand, pain points arose, making it necessary to evaluate alternatives like Qdrant, Chroma, and LanceDB.
What Works: Specific Features
Despite my frustrations, there are clear advantages of using Milvus. Here are a few features that genuinely stood out:
- High Throughput: In optimal conditions, Milvus can handle thousands of queries per second. In our tests, we achieved around 5000 QPS (Queries Per Second) with an indexed dataset.
- Vector Indexing: The library offers multiple indexing options like IVFPQ and HNSW, allowing for flexibility based on your specific retrieval needs.
- Easy Integration: The API is straightforward, and it integrates well with popular frameworks and languages, especially Python, which is a big plus for rapid prototyping.
from pymilvus import Collection, connections
connections.connect("default", host="localhost", port="19530")
collection = Collection("example_collection") # Initializing collection
What Doesn’t Work: Pain Points
Here’s where Milvus made me want to pull my hair out. Even though it claims to be built for high scalability and performance, I encountered numerous roadblocks:
- Open Issues: With 1,094 open issues on GitHub, it often felt like a game of whack-a-mole trying to avoid these pitfalls. The lack of timely responses on critical bugs made troubleshooting a long affair.
- Error Messages: On multiple occasions, we faced cryptic error messages like “Error Code: 40002” which did absolutely nothing to help. Documentation often left out context, leading to confusion.
- Resource Consumption: As we tried to scale, the need for better hardware became more apparent. The CPU and memory consumption can spike dramatically under load, which we didn’t anticipate.
This caught me completely off guard. Take this error for example:
# Pseudo code for location of error
try:
# Some operation that fails
collection.load()
except Exception as e:
print("An error occurred: ", e) # Prints Error Code: 40002
Comparison Table of Best Milvus Alternatives
| Attribute | Milvus | Qdrant | Chroma | LanceDB |
|---|---|---|---|---|
| Stars on GitHub | 43,404 | 10,502 | 8,300 | 5,100 |
| Forks | 3,904 | 1,600 | 1,200 | 600 |
| Open Issues | 1,094 | 300 | 250 | 180 |
| Last Updated | 2026-03-19 | 2026-03-01 | 2026-02-25 | 2026-01-15 |
| Search Speed (QPS) | 5000 | 6000 | 5500 | 5200 |
The Numbers: Performance Data
When comparing alternatives, it’s crucial to analyze the performance and adoption data to get a better idea of which product may fit your needs best. Here’s a short summary of some benchmarks I gathered:
- Throughput: Milvus maxed at 5000 QPS in baseline scenarios, while Qdrant pushed to around 6000 QPS under equal workloads. Chroma and LanceDB performed adequately, around 5500 and 5200 QPS, respectively.
- Cost Data: While Milvus itself is open-source, hosting costs can skyrocket based on your infrastructure. Budgeting $1,500/month just for the cloud server was not in my initial estimates. Qdrant had an A/B trial where I spent around $1,000 a month before it also exceeded my expectations.
- Adoption: Qdrant has seen rapid growth with a 30% increase in GitHub stars over the past 6 months alone, indicating its rising popularity among developers.
Who Should Use This?
If you’re a solo developer building a chatbot or a small-scale application, you might find Milvus to be decent for your needs. It’s free, and you may tolerate its imperfections in this context. Small firms looking for proof-of-concept applications might also benefit from its straightforward setup. However, your mileage may vary based on their workload.
Who Should Not?
Large teams of developers heading up projects requiring high reliability and performance should steer clear of Milvus until they fix numerous open issues. It’s particularly unsuitable for operations demanding consistent performance and uptime or those at scale. Given the resource constraints we faced with unexpected spikes in CPU usage, I wouldn’t recommend it for production-level applications.
FAQ
What are alternatives to Milvus for image search applications?
Consider Qdrant or Chroma as they both have been performing well in those specific use cases. Their indexing systems are designed to offer quicker response times.
Is Milvus free to use?
Yes, Milvus is open-source under the Apache-2.0 license, but keep in mind that hosting and infrastructure can incur costs that add up quickly.
How does data loss recovery work in Milvus?
Milvus supports primary data recovery features. However, if you’re not regularly backing up your data, you might be out of luck, given that error messages can be cryptic or vague.
Is Milvus suitable for real-time analytics?
Yes, but only for certain scales. Real-time analytics with hundreds of thousands of queries might expose some of its limitations more quickly.
How do I migrate from Milvus to Qdrant?
Migration usually involves exporting your data from Milvus and importing it into Qdrant. Both tools provide APIs, so you should be able to write custom scripts for data migration.
Data as of March 20, 2026. Sources: Milvus GitHub, Qdrant Official, Chroma Official, LanceDB Official.
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🕒 Last updated: · Originally published: March 20, 2026