Decision comparison
Redis Vector Search vs Zilliz
This is a comparison between the cheapest possible way to add retrieval and a managed service built for retrieval at scale. Redis Vector costs you an index if Redis is already running; Zilliz costs you a new bill and gives you managed Milvus with compute and storage scaled independently. The deciding question is corpus size and whether anyone wants to think about vector infrastructure at all.
Direct comparison. These are reviewed substitutes bought for the same job, so the differences below are the ones that decide between them.
All 2 are vector databases.
Quick Comparison
| Decision factor | Redis Vector Search | Zilliz |
|---|---|---|
| What it is | Vector search inside Redis, on data stored as hashes or JSON documents | Fully managed Milvus, operated by the company that maintains Milvus |
| Who operates it | You, or your managed Redis provider, alongside everything else Redis is doing | Zilliz; there is no cluster for you to run |
| Scale target | Comfortable into the low millions of vectors; memory is the constraint | Billion-scale search, with compute and storage scaled independently |
| Marginal cost | Near zero if Redis is already deployed — an index rather than a platform | A new managed service with its own bill, separate from your existing infrastructure |
| Memory model | Memory-first: the index competes with caches, sessions and queues on the same instance | Managed tiering across memory, disk and object storage, sized by the service |
| Isolation | Shared failure domain with the rest of your Redis workload | Dedicated service; a retrieval problem stays a retrieval problem |
| Deployment | Self-hosted on Docker or Kubernetes, or a managed Redis service on AWS, GCP or Azure | Managed on AWS, GCP and Azure; Milvus itself can be self-hosted on Kubernetes if you prefer |
| Client libraries | Python, Node.js, Java and Go clients, with vectors on hashes or JSON documents | Python, Java, Go and Node.js clients over gRPC and REST, matching the Milvus SDK |
| Pricing model | Vector search ships inside Redis itself, which from Redis 8 is licensed under your choice of RSALv2, SSPLv1 or AGPLv3, so self-hosting is free. The distributed build is sold as Redis Cloud and Redis Enterprise Software. | Free (no cost), Standard $0/mo, Enterprise MOST POPULAR $155/mo |
Redis Vector Search
- What it is:
- Vector search inside Redis, on data stored as hashes or JSON documents
- Who operates it:
- You, or your managed Redis provider, alongside everything else Redis is doing
- Scale target:
- Comfortable into the low millions of vectors; memory is the constraint
- Marginal cost:
- Near zero if Redis is already deployed — an index rather than a platform
- Memory model:
- Memory-first: the index competes with caches, sessions and queues on the same instance
- Isolation:
- Shared failure domain with the rest of your Redis workload
- Deployment:
- Self-hosted on Docker or Kubernetes, or a managed Redis service on AWS, GCP or Azure
- Client libraries:
- Python, Node.js, Java and Go clients, with vectors on hashes or JSON documents
- Pricing model:
- Vector search ships inside Redis itself, which from Redis 8 is licensed under your choice of RSALv2, SSPLv1 or AGPLv3, so self-hosting is free. The distributed build is sold as Redis Cloud and Redis Enterprise Software.
Zilliz
- What it is:
- Fully managed Milvus, operated by the company that maintains Milvus
- Who operates it:
- Zilliz; there is no cluster for you to run
- Scale target:
- Billion-scale search, with compute and storage scaled independently
- Marginal cost:
- A new managed service with its own bill, separate from your existing infrastructure
- Memory model:
- Managed tiering across memory, disk and object storage, sized by the service
- Isolation:
- Dedicated service; a retrieval problem stays a retrieval problem
- Deployment:
- Managed on AWS, GCP and Azure; Milvus itself can be self-hosted on Kubernetes if you prefer
- Client libraries:
- Python, Java, Go and Node.js clients over gRPC and REST, matching the Milvus SDK
- Pricing model:
- Free (no cost), Standard $0/mo, Enterprise MOST POPULAR $155/mo
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Redis Vector Search | Zilliz |
|---|---|---|
| GitHub commits, 90d(Product adoption) | 549 | Not available |
| GitHub stars(Product adoption) | 6,000+ | Not available |
| Hacker News mentions, 90d(Community interest) | 0 | Not available |
| PyPI weekly downloads(Developer adoption) | 384.3k | Not available |
| Hugging Face downloads(Product adoption) | Not available | 832 |
| Hugging Face likes(Product adoption) | Not available | 104 |
| npm weekly downloads(Developer adoption) | Not available | 133.5k |
| PyPI weekly downloads(Ecosystem adoption) | Not available | 883.0k |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Redis Vector Search
September 21, 2026Package vulnerabilities
PyPI · redisvl@0.27.2
0 vulnerabilities
across 1 package
Repository security score
Not available
Zilliz
September 21, 2026Package vulnerabilities
npm · @zilliz/milvus2-sdk-node@3.0.6 · PyPI · pymilvus@3.0.2
0 vulnerabilities
across 2 packages
Repository security score
Not available
Interface Preview
Zilliz
Feature Comparison
| Feature | Redis Vector Search | Zilliz |
|---|---|---|
| Search | ||
| Approximate nearest-neighbour search | Full support | Full support |
| Filtered vector search | Full support | Full support |
| Hybrid keyword and vector search | Partial support | Full support |
| Partitioning for large tenant counts | Partial support | Full support |
| Scale | ||
| Billion-scale collections | Not verified | Full support |
| Separated compute and storage | Not verified | Full support |
| Quantisation to cut memory | Full support | Full support |
| Horizontal scale-out | Full support | Full support |
| Operations | ||
| Runs without adding a system | Full support | Not verified |
| Fully managed | Partial support | Full support |
| Isolated failure domain for retrieval | Not verified | Full support |
| Self-hosted option | Full support | Partial support |
| Ecosystem | ||
| Python and Node.js clients | Full support | Full support |
| RAG framework integrations | Full support | Full support |
| Open-source engine | Full support | Full support |
| Existing footprint in most stacks | Full support | Not verified |
Search
Approximate nearest-neighbour search
Filtered vector search
Hybrid keyword and vector search
Partitioning for large tenant counts
Scale
Billion-scale collections
Separated compute and storage
Quantisation to cut memory
Horizontal scale-out
Operations
Runs without adding a system
Fully managed
Isolated failure domain for retrieval
Self-hosted option
Ecosystem
Python and Node.js clients
RAG framework integrations
Open-source engine
Existing footprint in most stacks
Which to choose
This is a comparison between the cheapest possible way to add retrieval and a managed service built for retrieval at scale. Redis Vector costs you an index if Redis is already running; Zilliz costs you a new bill and gives you managed Milvus with compute and storage scaled independently. The deciding question is corpus size and whether anyone wants to think about vector infrastructure at all.
Best-fit scenarios
Choose Redis Vector Search if:
Choose Redis Vector when the corpus is in the low millions and Redis is already in production. You add an index instead of a system: no new deployment, no second backup policy, no extra vendor, and the operational knowledge already exists in the team. For retrieval that supports an application rather than defining it, that is usually the right trade, and quantisation keeps the memory cost manageable.
Choose Zilliz if:
Choose Zilliz when the collection is large enough that memory planning would otherwise become a recurring job, or when nobody on the team wants to own vector infrastructure. Milvus separates compute from storage so the two scale independently, which suits large collections with uneven query load, and Zilliz runs it for you. Partitioning also makes large tenant counts a first-class concern rather than a naming convention.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
At what size does Redis stop being the easy answer?
There is no single threshold, but the arithmetic gives you a useful estimate. A 1,536-dimension embedding at 32-bit precision is roughly 6 KB before index overhead, so 5 million documents is about 30 GB of vectors sharing an instance with your caches and sessions; 8-bit quantisation cuts that to nearer 8 GB. The signal to watch is behavioural rather than numeric: when the index is what determines your instance size, or when cache hit rates fall because vectors have squeezed out cached values, the shared arrangement has stopped being free and a dedicated store is buying something real.
Is Zilliz just hosted Milvus?
Essentially yes, and that is the appeal rather than a criticism. Zilliz is the company that maintains Milvus, so the managed service and the open-source engine share a data model and client libraries. Prototyping on Milvus and moving to Zilliz does not mean rewriting application code, and the option of running Milvus yourself remains open if the commercial arrangement ever stops suiting you.
What do we lose by leaving Redis?
Proximity, mostly. Vectors in Redis sit next to sessions, caches and other hot data, so a single round trip can serve several needs and there is one fewer service in the request path. Moving retrieval out adds a network hop and a second system to monitor. You gain isolation, independent scaling and headroom. Which matters more depends on how tightly your retrieval is coupled to the rest of the request.
How do the cost models compare?
They are not directly comparable, which is why modelling matters. Redis vector search adds no separate licence — the cost is the extra memory, and possibly a larger instance. Zilliz is usage-based on compute and storage, so the bill is visible and grows with the workload. A small corpus is almost free in Redis and cheap but non-zero on Zilliz; a large one inverts that, because the memory Redis would need is expensive infrastructure.
Can we run both?
Some teams do, with hot, small, latency-critical lookups in Redis and the large corpus in a dedicated store. It is a defensible architecture and it doubles the number of places embeddings live, so the consistency question becomes yours to answer. Do it when the two workloads genuinely differ in shape, not to avoid making a decision.