Qdrant vs Milvus: when each one is the right choice
Choose Qdrant when your team wants one service it can run, monitor and back up with few moving parts. Choose Milvus when you expect very large collections and already have people who can operate a disaggregated cluster. Both projects are Apache-2.0 licensed, so license does not separate them and the decision rests on operating model. Qdrant is a single Rust binary that scales by sharding and replication, while Milvus separates proxies, coordinators and worker nodes and depends on etcd for metadata, object storage such as MinIO, S3 or Azure Blob, and a write-ahead log such as Woodpecker, Kafka or Pulsar.
Three factors decide the choice. The first is target corpus size: estimate how many vectors you will hold after two or three years of ingestion, including the payload and any sparse vectors, and test both engines against a sample of that size before committing. The second is in-house skill: if your platform team already runs Kubernetes, object storage and a message log well, the Milvus footprint is manageable; if not, the extra components become your on-call burden. The third is how much operating surface you will accept. Qdrant needs block storage with a POSIX-compatible file system and does not run on NFS or S3, and its self-deployed defaults are insecure until you configure API keys and TLS.
Before you choose, confirm the deployment path you will actually use. Qdrant documents a community Helm chart and an enterprise operator, and the Helm chart lacks features such as zero-downtime upgrades and automatic shard rebalancing, so ask the vendor what your path includes. For Milvus, confirm which deployment mode fits your scale among Standalone and Distributed, and which write-ahead log and object store your environment supports. Check the current release line for each engine on its Milvus releases page and the Qdrant GitHub releases before you write a design document.
Which scenarios favor Qdrant and which favor Milvus
This table maps common team situations to the better fit between Qdrant and Milvus, and names the condition that tips each one. Read it with the operating capacity column in mind, because team skills and platform staffing often matter as much as the data size.
| Scenario | Better fit | Condition that makes it so |
|---|---|---|
| Single-team RAG pilot on one server | Qdrant | Qdrant runs as one binary on block storage with no coordinator or write-ahead log to operate. Milvus Standalone is possible, but it adds more components to learn for a pilot. |
| Regulated enterprise with a small platform team | Qdrant | Qdrant's documented access controls (API keys, JWT per-collection permissions, JSON audit logging) are simpler to assess and run. Confirm the feature set you need against your edition and version. |
| Multi-billion-vector search workload | Milvus | Milvus separates proxies, coordinators, workers, metadata, object storage and the write-ahead log, so each tier scales on its own. This only pays off if you have the staff to run that stack. |
| Team already running Kubernetes with object storage and an event log | Milvus | Milvus's distributed mode is Kubernetes-native and fits existing etcd, object storage and streaming operations. Qdrant still needs block storage, so object storage cannot serve as its primary store. |
| Team with no Kubernetes experience | Qdrant | Qdrant can run on a single node without a cluster orchestrator. Its documented production paths include a community Helm chart or the enterprise operator, so Kubernetes skills still matter at scale. |
| Growing from one node to a multi-node cluster | Qdrant (with planning) | Qdrant uses Raft and shards with a replication factor, and recommends at least three nodes for quorum. Teams should plan shard counts and rebalancing by hand if they self-host. |
| Hybrid keyword and vector search with multi-tenancy | Milvus (confirm fit) | Milvus documents BM25, sparse vectors and tenancy at database, collection, partition or partition key level. Qdrant also offers sparse and hybrid search, so compare tenancy models against your isolation needs. |
| Choosing by operating capacity, not only scale | Depends on staffing | If no one can operate etcd, a write-ahead log and object storage reliably, a simpler Qdrant deployment is safer. Scale alone does not justify Milvus's footprint. |
How Qdrant and Milvus are built and what that means for operations
Qdrant and Milvus both sit on Apache-2.0 licenses, but they make very different bets about how many moving parts you run. The cards below follow those choices into backups, upgrades and on-call work, and note what to confirm in your own environment before you commit.
One binary, sharded by design
Qdrant ships as a single Rust binary. A single node needs no external metadata store or message bus, so the unit you back up, upgrade and restart is the Qdrant process and its storage. Distributed mode adds Raft consensus for cluster topology and collection structure, splits collections into shards, and keeps extra copies through a replication factor. Confirm how your team will run snapshots and rolling restarts across nodes, since self-hosted clusters handle rebalancing and resharding by hand.
Block storage only, no NFS or S3
Qdrant needs block-level storage with a POSIX-compatible file system and cannot run on network file systems such as NFS or on object storage such as S3. SSD or NVMe drives are recommended. For operations, this means your storage class, volume snapshots and disk-replacement runbooks define recovery time, and object storage cannot serve as the primary data tier. Check that your cloud or data center can provide local block volumes before you size the cluster.
Stateless proxies over coordinator and workers
Milvus splits the system into stateless proxies, a coordinator and worker nodes. Because the proxies hold no data, you can scale and restart them without moving collections, but the coordinator and workers still need careful rollout ordering during upgrades. On-call engineers must learn which component is failing before they act, because a slow query path and a stalled ingest path can look alike from the client side. Ask the vendor or your own tests which Milvus release line and component versions you will pin.
etcd holds the metadata
Milvus keeps metadata in etcd, so etcd becomes a dependency you must back up, monitor and keep healthy alongside the data plane. Losing or corrupting etcd can leave healthy object storage unusable, so restore drills should cover both systems together. Qdrant avoids this extra store in single-node setups, but its Raft state still needs the same discipline in cluster mode. Plan etcd quorum, certificates and compaction as recurring work.
Object storage holds the segments
Milvus stores data in object storage such as MinIO, S3 or Azure Blob. That shifts bulk durability onto the object store and its lifecycle rules, which your storage team may already run. Backups become a question of coordinating object versioning or replication with metadata backups taken at the same point. Confirm which object store your platform team supports, how it is replicated, and who owns its restore procedure before you choose Milvus.
Choosing the write-ahead log
Milvus can use Woodpecker, Kafka or Pulsar as its write-ahead log. Each choice brings its own failure modes, upgrade cadence and expertise requirements. Choosing Kafka or Pulsar means your team also operates a streaming platform, while the Woodpecker option reduces that dependency if it fits your release line. Make this decision explicitly during design, because switching the log later is a migration project rather than a configuration change.
Milvus Lite, Standalone and Distributed
Milvus offers three deployment modes: Milvus Lite as a Python library, Standalone on a single machine, and Distributed as a Kubernetes-native cluster. Lite suits development and tests, Standalone suits a single-host pilot, and Distributed carries the etcd, object storage and log dependencies described above. Plan the promotion path from Standalone to Distributed now, including data export and a test restore, so the move to production does not depend on an untested procedure.
Feature and capability comparison: Qdrant against Milvus
This table compares license, release lines, indexing, search modes, quantization, access control, audit logging and deployment options for Qdrant and Milvus. Read each row as a question to confirm for your version and edition, since some capabilities are documented for only one side. The main takeaway is that both are Apache-2.0 and both offer authentication and TLS, but Qdrant's documented access and audit controls are simpler to map onto a small team, while Milvus's index and GPU options matter most at larger scale.
| Capability | Qdrant | Milvus |
|---|---|---|
| License | Apache-2.0, free to self-host the open-source engine | Apache-2.0, under the LF AI and Data Foundation with Zilliz as primary contributor |
| Release lines to track | v1.19.2 published October 5, 2026 as the latest GitHub release | Two lines: v3.0.3 (October 14, 2026) and v2.6.25 (September 29, 2026) |
| Index types | Confirm the full index list for your version in the Qdrant documentation | HNSW, IVF, FLAT, SCANN and DiskANN, per the Milvus README |
| GPU indexing | Not covered in the supplied facts, so confirm support with Qdrant before planning | GPU indexing with NVIDIA CAGRA, per the Milvus README |
| Hybrid and sparse search | Dense, sparse and hybrid search, per the Qdrant README | BM25 full-text, sparse vectors and hybrid dense plus sparse search, per the Milvus README |
| Quantization and memory | Built-in quantization that Qdrant says cuts RAM use by up to 97%, a vendor claim to test on your data | Quantization and memory-mapped files, per the Milvus README; confirm the savings on your workload |
| Multi-tenancy | Collections and per-collection permissions, confirm isolation model for your tenant count | Database, collection, partition or partition key level, per the Milvus README |
| Authentication and TLS | Admin and read-only API keys and TLS for REST and gRPC since v1.2.0; off by default, so configure before exposure | Authentication, TLS encryption and role-based access control, per the Milvus README |
| Granular access control | JWT-based per-collection read or write permissions since v1.9.0 | Role-based access control, confirm the role model and granularity for your version |
| Audit logging | JSON audit logging of authenticated API operations since v1.17.0 | Not covered in the supplied facts, so confirm audit trail options with Milvus or Zilliz |
| Deployment modes | Single node, distributed cluster with Raft, Helm chart and enterprise Kubernetes operator | Milvus Lite, Standalone on one machine and Distributed on Kubernetes |
| Managed options | Qdrant Cloud, Hybrid Cloud and Private Cloud, priced through Qdrant sales for the latter two | Zilliz Cloud with Serverless, Dedicated and bring-your-own-cloud options |
Licensing, editions and paid options for each platform
Qdrant's engine is licensed under Apache-2.0 and is free to self-host, according to its GitHub repository and pricing page. That means the software itself carries no license fee for self-hosted use. The cost you carry is the infrastructure and the people who run it. The latest release on GitHub as of October 2026 is v1.19.2, published October 5, 2026, following v1.19.0 on August 5, 2026.
- Apache-2.0 engine: the open-source server can be self-hosted without a license fee for the software.
- Community Helm chart: Qdrant documents a community-supported Kubernetes Helm chart, but it lacks enterprise features such as zero-downtime upgrades and automatic shard rebalancing.
- Enterprise operator: the enterprise Kubernetes operator is offered as part of Qdrant Private Cloud, not in the open-source path.
- Recurring costs: compute, block storage on SSD or NVMe, backups, monitoring and the staff time to upgrade all remain yours.
- Who to ask: confirm with Qdrant which features, support terms and upgrade mechanics come with each path before you commit.
Confirm which upgrade and rebalancing tasks you will perform by hand in your own environment before you budget operator time.",
Qdrant offers several paid routes beyond self-hosting. The Qdrant Cloud free tier provides a single node with 0.5 vCPU, 1 GB RAM and 4 GB disk. The Standard tier is usage-priced and carries a 99.5% uptime SLA. The Premium tier requires a minimum spend and adds SSO, private VPC links and a 99.9% uptime SLA. The sources do not give the Standard or Premium price, so request both in writing.
- Free single node: 0.5 vCPU, 1 GB RAM and 4 GB disk, suited to evaluation rather than production load.
- Standard tier: usage-priced, with a 99.5% uptime SLA.
- Premium tier: minimum spend, adding SSO, private VPC links and a 99.9% uptime SLA.
- Hybrid Cloud and Private Cloud: managed clusters on your own infrastructure or a dedicated, fully isolated enterprise deployment, both priced through Qdrant sales.
- Quote scope: ask for the minimum spend, the usage metering and what support is included at each tier.
Check the uptime SLA language and the data residency terms in the contract before you rely on either tier for regulated workloads.
Milvus is licensed under Apache-2.0 and operates under the LF AI and Data Foundation, with Zilliz as its primary contributor. The project is written in Go and C++. Its open-source code can be self-hosted under the Apache-2.0 terms, so the software license itself is not a barrier for internal use. Two release lines are maintained: v3.0.3, published October 14, 2026, and v2.6.25, published September 29, 2026.
- Apache-2.0 license: the code is governed by the LF AI and Data Foundation project structure.
- Primary contributor: Zilliz drives most development, which matters when you weigh vendor dependency.
- Two release lines: v3.0.3 and v2.6.25 are both current, so choose a line and plan its upgrade path before going live.
- Self-hosted costs: the distributed mode relies on etcd, object storage and a write-ahead log, all of which you pay to run.
- Support: confirm with the Milvus community or a commercial provider what support and patch windows apply to the line you pick.
Confirm which release line receives patches for your planned support window before choosing one.
Zilliz Cloud provides fully managed Milvus with three options: Serverless, Dedicated and bring-your-own-cloud. The sources describe these options but do not give their prices, so ask Zilliz for a quote for each option you consider. Serverless and Dedicated are hosted by Zilliz, while bring-your-own-cloud runs in your cloud account. Each option moves different costs between you and the vendor.
- Serverless: a managed option where the vendor operates the cluster, suited to variable workloads when the pricing model fits.
- Dedicated: a managed cluster reserved for your workload, with pricing and terms to confirm in writing.
- Bring-your-own-cloud: Zilliz operates Milvus in your cloud account, so cloud charges stay with you while operation shifts to the vendor.
- Pricing questions: ask how each option meters storage, compute and data transfer, and what is one-time versus recurring.
- Data location: confirm where data and backups sit for each option before you assess regulated data.
Request written terms covering uptime commitments, support levels and exit or data export procedures for each option.
What drives the running cost of each vector database
Running either database costs more than its license line, and the largest costs come from the infrastructure each one needs and the people who keep it healthy. The cards below separate what you pay once from what recurs, and list what to request in a written quote so the two options can be compared on the same basis.
Qdrant node storage and cluster size
Qdrant needs block-level storage with a POSIX-compatible file system and cannot run on NFS or S3-style object storage, so SSD or NVMe nodes are the expected hardware. The docs recommend at least three nodes for a production cluster so quorum survives a node failure. Ask any integrator to price three storage-backed nodes, the shard count as a multiple of the node count, and the monitoring needed on each.
Replication doubles storage at factor 2
In Qdrant, the replication factor sets how many copies of each shard exist, and a factor of 2 doubles the storage footprint. This recurs every month as the corpus grows, so the cost of disks and backup capacity scales with the replication setting, not only with the vectors you load. Confirm the replication factor you plan to run and size disks for it before comparing totals.
Milvus services beyond the database
A Milvus distributed deployment depends on etcd for metadata, an object store such as MinIO, S3 or Azure Blob, and a write-ahead log service such as Woodpecker, Kafka or Pulsar. Each dependency is another component to host, patch, back up and monitor, and in many estates that means existing platform teams carry extra duties. Ask for the sizing and support model of every dependency, not only the Milvus nodes.
Staff skills and on-call load
Qdrant's single binary and Raft-based cluster keep the moving parts few, which lowers the skill set needed to run it. Milvus's disaggregated design asks for people who can reason about etcd, object storage and the log layer during an incident. Price the staff time for on-call, upgrades and capacity reviews, and decide whether that work sits with an existing team or a new hire.
Upgrades and rebalancing effort
The Qdrant Helm chart lacks enterprise features such as zero-downtime upgrades and automatic shard rebalancing, so self-hosted clusters take that work by hand or through the enterprise operator. Milvus upgrades span its own release lines, with v3.0.3 and v2.6.25 both current as of this writing, and each dependency has its own upgrade path. Ask for a written upgrade runbook, the expected maintenance windows, and who performs rollbacks.
Support, managed fees and quote terms
Support differs by path: Qdrant Cloud Standard and Premium carry uptime SLAs and priced tiers, while Hybrid and Private Cloud are priced through Qdrant sales, and Zilliz Cloud offers managed Milvus. Self-hosting shifts the license cost to zero and moves spend to infrastructure, labor and any retainer for support or testing. Request a quote that lists one-time setup, recurring hosting, support hours, upgrade work and any managed-service fees, each priced separately.
Moving data from Qdrant to Milvus or back: what transfers and what is rebuilt
This sequence separates the data you can carry across from the configuration you must rebuild by hand, so a Qdrant to Milvus move or return trip ends with a tested, parallel-read cutover instead of a surprise on launch day.
Inventory collections, payloads and embedding models
List every collection, its vector size and distance metric, the payload fields your filters depend on, and the exact embedding model and version that produced each vector. Your team owns this inventory, and DeployOpen can run it alongside your application owners. The step is done when each collection has a written record of dimensions, payload schema, sparse or hybrid vectors, and the model version. Confirm model version details with the model provider or your own pipeline logs, because a vector without a recorded model is hard to trust later.
Snapshot the Qdrant source before any change
Create snapshots of each Qdrant collection using the documented snapshot process, then store them outside the production cluster with checksums. This gives you a restore point for the source data and a known-good baseline. The step is done when a snapshot restores into a separate test instance and the point count and a sample of search results match the source. Snapshot behavior depends on your storage setup and version, so confirm the procedure against the Qdrant snapshot documentation for your release.
Export vectors and payloads to a neutral format
Extract vectors, point IDs and payloads from Qdrant into an intermediate format your team controls, such as Parquet or JSON Lines, and check that IDs, nested payload types and sparse vectors survive the trip. The Qdrant migration tool is the documented starting point for Qdrant-side work, but confirm whether it targets Milvus directly or only moves data between Qdrant instances. The step is done when row counts, ID sets and a hash sample of payloads match between the export and the source.
Regenerate embeddings only when the model changes
If the embedding model and its settings are unchanged, carry the existing vectors across and skip re-embedding. If the model, dimension or normalization changed, re-embed the source documents with the target model in a separate batch job. The step is done when every vector in the target has a recorded model version and a spot check of query results matches your acceptance criteria. Re-embedding recurs on every future model change, so include it in your change process.
Re-create indexes, quantization and filter settings
Define the target collections or schemas in Milvus or Qdrant, then rebuild index types, distance metrics, quantization settings, sparse and hybrid search configuration and payload or scalar indexes for the fields your filters use. Index parameters do not carry over as a simple export, so treat each setting as a rebuild item. The step is done when index build completes, recall on a labeled query set meets your threshold and filtered queries return the same top results as the source within agreed tolerance. Confirm the supported index and quantization options for your exact version with each vendor's documentation.
Rebuild access control and API keys
Recreate users, roles, API keys and tenant boundaries on the target. Qdrant uses admin and read-only API keys, JWT-based per-collection permissions and audit logging, while Milvus uses its own authentication and role-based access control, so the mappings are not one to one. Your security team should write the permission matrix first and test each role against a denied and an allowed request. The step is done when every application identity has a tested least-privilege grant and the audit trail is confirmed to record authenticated operations.
Load the target and run parallel reads
Ingest the exported data into the target, then send a copy of production read traffic to both systems while the application still reads from the source. Compare top results, latency and error rates per collection over a period you define with the business owners. The step is done when mismatches are explained, latency is within the target you set, and ingestion of new writes into both systems is confirmed to be consistent or is paused with a documented plan.
Re-point applications and cut over
Change connection strings, client libraries and API key references in each application, one service at a time, and keep the source readable until the last consumer has moved. Document a rollback that points applications back to the source. The step is done when all services query the target, the rollback path has been tested, and the source is set to read-only and retained for an agreed period before decommissioning. Confirm client library compatibility and any SDK changes with each vendor before you start.
Where the decision turns in practice: scale, team and access control
The operating model is where Qdrant and Milvus diverge most. Qdrant distributed mode keeps sharding, replication and cluster topology inside one service family, coordinated through the Raft consensus protocol, so a team runs one kind of process and one storage layout. Milvus splits the same job across stateless proxies, a coordinator, worker nodes, etcd for metadata, an object store such as MinIO, S3 or Azure Blob, and a write-ahead log such as Woodpecker, Kafka or Pulsar. That separation is what lets Milvus scale compute and storage independently, but every component becomes something your team must monitor, back up and upgrade. If you have no one who already runs etcd or a message log, price that skill gap into the decision before the corpus size forces it.
Access control follows the same split. Qdrant gives you admin and read-only API keys, TLS for REST and gRPC, and JWT-based granular access with per-collection read or write permissions, plus JSON audit logging of authenticated operations. Milvus implements authentication, TLS and role-based access control, and offers multi-tenancy at database, collection, partition or partition key level. Milvus's wider tenancy options matter when many tenants share one cluster and you need isolation below the collection. Qdrant's collection boundary is usually simpler to reason about and audit, but if tenants need separate indexes or strict isolation, test whether per-collection permissions meet your requirement. Before you commit, ask both vendors for the exact role model, how audit trails are produced, and how tenant boundaries are enforced in your deployment pattern. Qdrant's security documentation also states that self-deployed instances listen on all interfaces with no authentication by default, so configure keys and TLS on day one whichever product you pick.
Questions buyers ask before choosing between Qdrant and Milvus
How much more effort does a self-hosted Milvus cluster need than Qdrant?
A self-hosted Milvus distributed deployment usually takes more operating effort than Qdrant, because it is a disaggregated system with several moving parts. The Milvus architecture documentation describes stateless proxies, a coordinator and worker nodes, etcd for metadata, object storage such as MinIO, S3 or Azure Blob, and a write-ahead log that can be Woodpecker, Kafka or Pulsar. Each of those components needs its own sizing, monitoring, backup and upgrade plan. Qdrant is a single Rust binary, and its distributed mode relies on Raft consensus and shards across nodes. The documented production paths for Qdrant are the managed Qdrant Cloud, a community Helm chart and an enterprise Kubernetes operator, and the Helm chart lacks features such as zero-downtime upgrades. Ask your team to count the services they would run, and who owns each one, before choosing.
How long does a migration between Qdrant and Milvus take?
No supplied fact gives a migration duration, so confirm it with the vendor and against your own data. The main drivers are corpus size, vector dimensions, the number of collections, how much payload metadata must be remapped, and whether embeddings are reused or recomputed. Index settings and filtering logic also differ between the two systems, so the schema and query layer usually need rework even when the vectors move cleanly. Plan a parallel run with both stores serving the same test queries, then compare recall and filtered results before cutting over. Ask any consultant for a written migration plan that covers data export, validation, rollback and the query changes in your application.
Can a small team run Qdrant or Milvus without outside help?
A small team can run a single-node Qdrant instance in-house if it has someone comfortable with Linux, block storage and API key management. By default, self-deployed Qdrant listens on all interfaces with no authentication, so the first setup must enable API keys, TLS and network restrictions. Qdrant's distributed mode recommends at least three nodes for production quorum, which adds operating work. Milvus's Standalone mode suits a single machine, but production use typically means the Distributed mode on Kubernetes with etcd, object storage and a write-ahead log to maintain. Teams without Kubernetes and storage operations experience usually need outside help for the production deployment and the first upgrade cycle.
What does a DeployOpen engagement cover, and what does it need from my team?
DeployOpen plans, implements, customizes, integrates, hardens, backs up, upgrades, documents and supports self-hosted open-source platforms inside your own cloud accounts, data centers or air-gapped networks. For a vector database, that can include deployment design, security configuration, backup and restore, upgrade planning and documentation. Most engagements are fixed-scope, with scope, deliverables and timelines agreed in writing before work starts, and ongoing support is available as a retainer. Your team typically needs to provide access to the target environment, the data and embedding pipeline details, the expected query and filter patterns, and a named owner who can approve design decisions. DeployOpen does not publish a price list, and a quote follows from the written scope.
Does Qdrant's sharding handle multi-billion vector collections?
No supplied fact states a vector count that Qdrant or Milvus handles, so do not assume a ceiling from either vendor's marketing. Qdrant scales horizontally through sharding and replication, and its distributed mode splits a collection into shards across nodes, with a replication factor that keeps extra copies and doubles storage at a factor of 2. Shard count should be a multiple of the node count, and self-hosted clusters must handle rebalancing and resharding manually. Milvus separates storage and compute, so it is designed around scaling each layer independently. Test both with your own vector dimensions, filters and recall targets on hardware that matches production, and ask each vendor for reference benchmarks at your target size.
Does the Apache-2.0 license restrict either Qdrant or Milvus?
Both Qdrant and Milvus are Apache-2.0 licensed, so self-hosting the open-source engine does not carry a license fee or a usage restriction from the license itself. Apache-2.0 still requires you to keep license notices and to follow its terms on redistribution and patents. Licensing is not the only cost question, because managed options are separate products. Qdrant Cloud, Hybrid Cloud and Private Cloud are priced through Qdrant, and Zilliz Cloud offers managed Milvus in Serverless, Dedicated and bring-your-own-cloud options. Confirm with the vendors whether any feature you need, such as enterprise access control or operator support, sits outside the open-source distribution.
What should a written quote for either deployment include?
A useful quote lists each deliverable against a named scope item, so the cost follows from what is included. Ask for the environment assumptions, including the cloud or data center, storage type and whether the setup must support block storage with a POSIX-compatible file system, since Qdrant cannot run on NFS or S3. Request the node count, the high availability design, backup and snapshot approach, security configuration such as API keys and TLS, and the monitoring plan. Separate one-time work, such as deployment and migration, from recurring work, such as support, upgrades and testing retainers. Also ask what is excluded, who performs the migration validation, and what happens if the scope changes.
How DeployOpen helps teams choose and implement a vector database
DeployOpen is a Chainscore Labs company that plans, implements, hardens, backs up, upgrades and documents self-hosted open-source platforms inside your own cloud accounts, data centers or air-gapped networks. For a team choosing between Qdrant and Milvus, that work starts before any installation: the choice depends on your corpus size, your filtering and access requirements, and whether your operations team can run an etcd, object storage and write-ahead log stack. DeployOpen can help you test those assumptions against your own data and infrastructure, and it also runs security testing on the AI systems that sit on top of the database, including retrieval paths and the API keys and roles that guard them. DeployOpen does not favor either platform, and it is not an official partner, reseller or certified provider for Qdrant or Milvus.
Engagements are fixed-scope. Scope, deliverables and timelines are agreed in writing before work starts, and DeployOpen does not publish a price list, so a quote follows from that written scope. Ask any provider for a quote that separates the one-time work (architecture decisions, deployment, hardening, migration of an existing collection, documentation) from recurring work such as upgrades, backups and monitoring. For Qdrant, the scope should state whether the team will use the community Helm chart or a different path, since the Qdrant installation docs note that the Helm chart lacks enterprise features such as zero-downtime upgrades. For Milvus, the scope should name the etcd, object storage and write-ahead log choices, because each one adds components to back up and patch. Ongoing support, upgrades and continuous testing are available as retainers, which suit teams that will keep a vector store in production for years. Confirm current release status and support windows directly with each vendor before you sign, using the Qdrant GitHub releases and Milvus GitHub releases pages as your starting points.
