Qdrant vs LanceDB: which to choose

Qdrant or LanceDB: which vector database fits your team

Choose Qdrant when several applications need retrieval from one shared store and you must control who can read or write it. Qdrant runs as a server, so every consumer talks to the same service over its API. The Qdrant security docs state that self-deployed instances listen on all interfaces with no authentication by default, so the first task is to configure admin and read-only API keys, TLS, and, if you need per-collection rights, JWT-based access control. Qdrant also documents JSON audit logging of authenticated API operations, which matters if you must show who queried what. Choose LanceDB when one application or a small team owns the retrieval layer and the data can stay on local disk or in your own cloud. LanceDB OSS is an embedded library, so there is no separate service to patch, expose or scale on day one.

The decision turns on four drivers. First, count the consumers: one application points to LanceDB, while three or four teams querying the same collections points to Qdrant. Second, define access control: if different users or services need different read or write permissions on the same data, Qdrant's server-side controls are the native fit. Third, estimate data scale and filtering needs, and test filtered search on your own corpus instead of assuming. Fourth, decide who runs the infrastructure. Qdrant needs block storage with a POSIX-compatible file system and SSD or NVMe recommended, and cannot run on NFS or S3, so plan the storage class before you commit. The Qdrant distributed deployment docs recommend at least three nodes for production clusters, which means an operations team must own that topology.

Confirm the licensing and hosting paths before you finalize the shortlist. Both engines are Apache-2.0 licensed, and both have a commercial route: Qdrant Cloud, Hybrid Cloud and Private Cloud on one side, and LanceDB Cloud and LanceDB Enterprise on the other. Ask each vendor which features, such as zero-downtime upgrades or distributed scale, are limited to those offerings, and get the answers in writing. If your team is unsure whether a retrieval workload needs a service, a short pilot with your real documents, your real filters and your real access rules will show more than either vendor's marketing. DeployOpen can plan and implement either path inside your own infrastructure under a fixed scope agreed in writing.

Which scenario favors which platform

This table matches common private AI scenarios to the platform that fits them better, with the condition that makes each choice hold. Read each row against your own topology, access model and audit obligations. The main takeaway: LanceDB suits a single application with versioned multimodal data, while Qdrant suits shared, permissioned and audited services.

Scenario	Better fit	Condition under which it holds
One embedded RAG application	LanceDB OSS	The index is used by one application process, and no other service needs network access to it.
Several applications sharing one index	Qdrant	Multiple services need network API access, which Qdrant serves over REST and gRPC with API keys or JWT permissions.
Multi-tenant or permissioned search	Qdrant	Tenants need per-collection read or write grants (JWT, since v1.9.0). Confirm how LanceDB isolates tenants if you choose it.
Multimodal data with versioning needs	LanceDB OSS	You want automatic zero-copy versioning of tables and multimodal data without running extra infrastructure.
Regulated environment requiring audit logs	Qdrant	You need JSON audit logging of authenticated API operations (since v1.17.0). Confirm LanceDB's audit capabilities with the vendor.
Index on network or object storage	Confirm before choosing	Qdrant cannot run on NFS or S3. Verify LanceDB's supported storage backends in its documentation for your environment.
Multi-node production cluster you operate	Qdrant	You run your own cluster with sharding and replication, and you plan for at least three nodes for quorum.
Vendor-run distributed scale	LanceDB Enterprise or Qdrant Cloud	You want a managed or private distributed platform. Compare LanceDB Enterprise's scale and deployment terms with Qdrant's Hybrid and Private Cloud options.

How Qdrant and LanceDB are built and what that means for operations

Qdrant runs as a networked service that you deploy, cluster and secure. LanceDB runs inside your application process on files you manage. The cards below cover where that split changes staffing, storage, security and growth decisions.

Client-server service with its own process

Qdrant runs as a standalone server that applications reach over REST and gRPC. Your team operates that process, its ports and its upgrades, and every client shares one service boundary. Check that your platform team can run a stateful service with health checks, restarts and version upgrades, and that application teams accept a network hop on each query.

Raft-coordinated distributed mode

In distributed mode, Qdrant uses the Raft consensus protocol to agree on cluster topology and collection structure, splits collections into shards across nodes and keeps extra copies through a replication factor. The docs recommend at least three nodes for production so quorum survives a node failure. Plan shard count as a multiple of node count and confirm how your team will handle resharding.

Block storage requirements

Qdrant needs block-level storage with a POSIX-compatible file system and does not run on NFS or object storage such as S3. SSD or NVMe drives are recommended. This shapes where you can place it on Kubernetes and in private clouds, and it means backup and restore rely on snapshots of local volumes rather than on a shared file share. Confirm your storage class before sizing the cluster.

Insecure by default

By default, self-deployed Qdrant instances listen on all network interfaces and have no authentication configured. Before any shared environment, you must configure admin and read-only API keys, TLS, and, where needed, JWT-based per-collection permissions and audit logging. Treat this as a launch gate in your deployment checklist, not a later hardening task.

Embedded library in your process

LanceDB OSS is an embedded retrieval library on the Lance columnar format, with client SDKs for Python, TypeScript and Rust. It runs inside your application or on a local machine, and the data lives in files you place, so there is no separate server to patch or cluster. The trade-off is that access control, concurrency and availability depend on how your application and storage are designed, so verify those for your workload.

Your cloud, or a managed or enterprise path

LanceDB OSS can run locally or in your own cloud, while LanceDB Cloud and Enterprise add managed operation and, for Enterprise, distributed scale and private deployments. Ask whether the storage backend you choose supports the concurrency and durability your application needs, and whether the Enterprise tier is required for the scale you expect. Any Enterprise terms come from LanceDB sales, not from the open-source license.

Filtering, hybrid search, multimodal data and versioning compared

This table compares how Qdrant and LanceDB handle payload filtering, hybrid search, multimodal data, versioning and access control. Read the cells as a checklist: where a capability is documented in the supplied material, it is stated, and where it is not, the cell tells you what to confirm in the docs or a proof of concept before you decide.

Capability	Qdrant	LanceDB
Metadata filtering	JSON payload filtering on vectors is a documented core feature of the Qdrant README	Filtering on metadata is part of SQL over vectors and metadata; confirm how filters combine with vector search in the LanceDB docs
Search modes	Dense, sparse and hybrid search in one engine	Vector similarity search and full-text search; confirm how hybrid ranking is combined in your chosen SDK
Query language	REST and gRPC APIs with client SDKs	SQL over vectors, metadata and multimodal data, plus Python, TypeScript and Rust SDKs
Multimodal data	Stores vectors and JSON payloads; multimodal pipelines are built in your application code	Built on the Lance columnar format for multimodal data; confirm supported media types and index options for your data
Quantization and memory	Built-in quantization that the README says cuts RAM usage by up to 97%	Supplied material does not cover quantization; test index size and recall on your own corpus
GPU index building	Not covered in the supplied material; confirm before relying on it	README lists GPU support for building vector indexes
Versioning	Snapshots are the documented backup and restore mechanism; confirm how they map to your change control	Zero-copy automatic versioning of data without extra infrastructure, per the README
Access control and audit	Admin and read-only API keys, JWT per-collection read or write permissions and JSON audit logging of authenticated operations	Access control and audit logging are not covered by the supplied material; confirm with the LanceDB Enterprise documentation

Licensing, editions and paid options for each platform

The Qdrant engine is licensed under Apache-2.0 and is free to self-host, so a team running it in its own cloud account, data center or air-gapped network pays no license fee for the software. The cost moves to the people and infrastructure that run it. The engine listens on all network interfaces and has no authentication configured by default, so the access controls that regulated environments expect must be set up during deployment.

Access controls: Admin and read-only API keys, TLS for REST and gRPC, JWT-based per-collection permissions and JSON audit logging of authenticated API operations are documented in the security docs. Confirm which of these your chosen release supports before you design the build.
Recurring costs: Compute, block storage, backups, monitoring, upgrades and on-call time recur for as long as the cluster runs. These are paid to your own staff or to an implementation partner.
Production path: The community Kubernetes Helm chart is a documented route, but it lacks enterprise features such as zero-downtime upgrades and automatic shard rebalancing, so those tasks become manual work.
Storage requirements: Qdrant needs block storage with a POSIX-compatible file system and does not run on NFS or S3, so the storage class you pay for has to match the requirement.

Before budgeting, check the current release notes and confirm which security and operations features are included in the open-source build and which depend on an enterprise offering.

How each platform scales from one node to a cluster

Qdrant scales in a documented sequence, and the first decision is whether one node is enough. A single Qdrant node is the simplest deployment. Moving to distributed mode is a deliberate change: the cluster uses Raft consensus to manage topology and collection structure, splits each collection into shards across nodes, and keeps extra copies through a replication factor. The Qdrant distributed deployment docs recommend at least three nodes for production so quorum survives a node failure, and a shard count that is a multiple of the node count so data spreads evenly. Replication factor 2 doubles storage, so size disks for every copy, not just the logical dataset. Qdrant documents three production paths: managed Qdrant Cloud, a community-supported Kubernetes Helm chart, and an enterprise Kubernetes operator offered as part of Qdrant Private Cloud. The Helm chart lacks enterprise features such as zero-downtime upgrades and automatic shard rebalancing, so confirm with Qdrant which of these your upgrade and resharding process will depend on before choosing it.

LanceDB scales differently because its open-source edition is an embedded retrieval library rather than a server. LanceDB OSS runs inside your application process or on a machine you control, using the Lance columnar format, and its docs describe it as easy to set up locally. That keeps the operating model simple for a single application, but it means there is no built-in cluster that coordinates nodes, shards data or fails over for you. Whether LanceDB OSS can serve concurrent writers or multiple application instances from shared storage, and what throughput or table size it handles reliably, are questions to put to LanceDB in writing rather than assume from the library model. For multi-node, petabyte-scale or private deployments, the LanceDB docs point to LanceDB Enterprise, a distributed platform that shares the same Lance format and table model as OSS, so tables can move between the two. Ask for the Enterprise architecture, its licensing terms and its support model before you treat it as the scaling path.

Access control and identity integration in each model

Qdrant is a network service that must be locked down before it holds production data, while LanceDB is an embedded library whose access control is largely inherited from the files and the application around it. Both models push identity work onto your team, but the points where you enforce it are different.

Qdrant's insecure default state

A self-deployed Qdrant instance ships listening on all network interfaces with no authentication configured. Anyone who can reach the port can read, write or delete collections until you change that. Before go-live, confirm the bind address, restrict network exposure to the services that need it, and test from an unauthorized host that requests are refused. This is a configuration task your team owns, not something the default install handles for you.

API keys and TLS for transport

Qdrant supports admin and read-only API keys, and TLS for both REST and gRPC since v1.2.0. Keys separate indexing jobs from query traffic at a coarse level, and TLS protects requests and keys in transit. Decide where certificates come from, how keys are rotated and where they are stored, since a shared admin key is the most common shortcut that weakens the model.

JWT per-collection permissions

JWT-based access control, available since v1.9.0, lets you grant read or write permissions per collection. This is the mechanism that maps application tenants or service accounts to the data they may touch. Ask your identity team which claims will carry collection scopes, how tokens expire, and who signs them, because the token issuance design determines whether the permissions hold up.

Audit logging of API operations

Since v1.17.0, Qdrant writes JSON audit logs of authenticated API operations. Those logs are the evidence trail for who changed or queried a collection. Confirm where logs are written, how long your SIEM or log platform retains them, and whether they capture the identity behind each call when a shared key is in use.

Identity provider wiring for Qdrant

Qdrant's access controls authenticate API callers, but they do not replace a directory. Your team must decide how human operators reach the cluster, usually through a bastion or an authenticated gateway that federates to your identity provider, and how service accounts receive JWTs. Verify that offboarding a user revokes their path to the cluster, and that keys issued to automation are inventoried.

LanceDB access depends on deployment

LanceDB OSS is an embedded library, so there is no server-side login for it to enforce. Access is governed by file system permissions on the Lance data, by the cloud storage policies where the data lives, and by the application that opens the tables. Confirm with LanceDB how access control works in the edition and deployment you plan to use, including its Enterprise and Cloud offerings, and do not assume Qdrant's controls carry over.

Moving data from LanceDB to Qdrant or back

This sequence moves a retrieval workload between an embedded LanceDB store and a Qdrant cluster without losing track of which vectors, payloads and filters the application depends on. It ends with a cutover that both teams have verified against recall and filtered-result checks.

Inventory collections, schemas and embedding models
The application owner and the platform team list every LanceDB table and every Qdrant collection in scope, along with its vector dimensions, distance metric, payload fields, full-text or SQL queries and the exact embedding model and version that produced each vector. This inventory is the basis for every later decision. Done means each table has a named owner, a documented schema and a recorded embedding model, and anyone can answer whether a given query depends on a payload field or a metadata filter.
Decide whether vectors can be reused
Compare the embedding model used to write the existing data with the model the application will query with after the move. If they differ in any way, including version or preprocessing, the stored vectors cannot be mixed with new query vectors, and the corpus must be re-embedded before cutover. Re-embedding is a compute and time cost that depends on corpus size and model choice, so ask the vendor or your own tests to confirm throughput before scheduling. Done means a signed-off decision per collection: reuse the vectors or re-embed.
Export vectors and payloads from LanceDB
The team reads each LanceDB table through its SDK (Python, TypeScript or Rust) and exports vector columns together with scalar metadata and any stored text or image references. Lance tables can carry versions, so the team should record which table version was exported. Keep the export in an open format so it can be loaded again if the move reverses. Done means row counts and checksums match the source table for every exported version, and the export manifest is stored with the project record.
Create Qdrant collections and load points
The platform team creates each Qdrant collection with the same vector size and distance metric recorded in the inventory, then uploads points with IDs, vectors and JSON payloads through the Qdrant client. Confirm the deployment meets Qdrant's storage requirements first: block-level storage with a POSIX-compatible file system, not NFS or S3. Done means point counts in Qdrant equal the exported row counts, and a sample of payloads matches the source byte for byte after type conversion.
Rebuild indexes, payload indexes and quantization
Qdrant does not inherit LanceDB's index objects, so the team declares payload indexes on every field used in filters, chooses quantization settings deliberately and lets the HNSW index build. LanceDB's full-text and SQL-style queries have no direct one-to-one copy in Qdrant, so each one must be mapped to a Qdrant filter, a sparse vector or a rewritten query. Done means every filter the application uses returns the same result set as the LanceDB version on a fixed test list.
Repoint application SDK calls
Developers replace LanceDB client calls with Qdrant client calls behind the same retrieval interface, keeping the embedding step, prompt assembly and access checks unchanged. Put both backends behind a feature flag or configuration key during the transition so rollback is a configuration change. Done means the application runs end to end against Qdrant in a staging environment with the flag set, and the diff shows no change outside the retrieval layer.
Validate recall and filtered results
Run a fixed set of labeled queries against both stores and compare the top results, recall at the chosen cutoff and the results returned under each metadata filter. Include queries that combine vector similarity with filters and any hybrid keyword queries the product depends on. Differences should be explained, either by approximate search settings or by a mapping gap, before cutover. Done means a written comparison report that the application owner and the security reviewer both accept.
Cut over and keep the rollback path
Freeze writes to the LanceDB source, run a final incremental export of any rows added since the first export, switch the feature flag to Qdrant and watch query results and error rates. Keep the LanceDB data and the export manifest until the team has accepted the new system for the agreed period. Done means production traffic is served from Qdrant, the rollback procedure has been rehearsed, and the decommissioning date for LanceDB is written in the change record.

What drives the running cost of each option

Running either option costs more than its license line, and the two products spread that cost across different places. Qdrant puts it mainly on nodes, storage and cluster operations, while LanceDB puts it on compute and object storage in your own cloud. Use the cards below to build a like-for-like quote request before comparing totals.

Qdrant node count and replication

A self-hosted Qdrant cluster is paid for in nodes, and the replication factor multiplies the storage each node holds. The distributed deployment docs recommend at least three nodes for production so quorum survives a node failure, and a replication factor of 2 doubles storage. Ask your provider to price the node count, storage per node and replication factor as separate line items, so you can see which one drives the total.

Storage class and disk

Qdrant needs block-level storage with a POSIX-compatible file system and cannot run on NFS or S3, and SSD or NVMe drives are recommended. That pushes storage cost toward premium block volumes. LanceDB OSS keeps data in files on a local disk or your own cloud object store, so its storage line usually follows object storage pricing. Confirm the storage class each option needs in your environment before pricing.

Engineering and operating skills

Qdrant's self-hosted cluster needs people who can run Raft-based clusters, plan shards and handle rebalancing and resharding by hand, while the Helm chart path lacks features such as zero-downtime upgrades. LanceDB is an embedded library, so the work sits in your application's release and data pipeline rather than in a separate cluster. Recurring engineering time is often the largest cost, so ask who will own each task and at what share of their week.

Backup, snapshots and upgrades

Qdrant provides snapshots, and you schedule, store and test restores yourself when self-hosted. LanceDB offers zero-copy versioning of tables, which covers part of the rollback need, but backup targets, retention and restore drills still belong to your team. Upgrades recur for both: Qdrant at each release line and LanceDB with your application's dependencies. Ask whether the quote includes restore testing and upgrade windows.

Paid cloud and enterprise tiers

Qdrant Cloud, Hybrid Cloud and Private Cloud are priced through Qdrant sales, and Premium cloud tiers carry a minimum spend. LanceDB Cloud and Enterprise are also sold as commercial offerings, with Enterprise aimed at petabyte scale and private deployments. These fees recur and replace some of your own infrastructure and operations work. Request the metered units, minimum commitment, SLA terms and what support is included in writing before comparing them with self-hosting costs.

One-time setup and implementation

Initial deployment, security hardening, access control design, data migration and integration with your application are mostly one-time costs, whether the work is done in-house or by a partner. For Qdrant this includes API key, TLS and JWT access setup, since a default self-deployed instance has no authentication. For LanceDB it includes embedding pipeline and table design. DeployOpen plans fixed-scope work from a written scope, so ask any provider for deliverables, timelines and exclusions in writing.

What implementing each platform involves and where projects run long

Implementing Qdrant is mostly infrastructure and access work. By default, self-deployed Qdrant instances listen on all network interfaces and have no authentication configured, so the first task is to enable API keys (admin and read-only), turn on TLS for REST and gRPC, and place the service behind private networking. For access beyond shared keys, the JWT-based model with per-collection read or write permissions and JSON audit logging of authenticated operations are the controls to design around. Storage sizing is the other early decision: Qdrant needs block storage with a POSIX-compatible file system, so NFS and S3-style object storage are out, and SSD or NVMe is the recommended target. If you expect to grow, plan the cluster early. The distributed deployment docs recommend at least three nodes for quorum and a shard count that is a multiple of the node count, and the replication factor doubles storage at a factor of 2. Backups use snapshots, and upgrades on self-hosted clusters need a rehearsed runbook, because zero-downtime upgrades and automatic shard rebalancing are managed-service features. Confirm the exact upgrade behavior of your chosen Helm chart or operator with Qdrant before you commit to a window.

Implementing LanceDB looks different because there is no server to harden. The work sits in the application: choosing the client SDK (Python, TypeScript or Rust), deciding how tables are laid out, and deciding who may read or write them. Because LanceDB OSS runs locally or in your own cloud, access control is whatever your storage bucket and application layer enforce, so the design has to be written down: which services hold write credentials, how read paths are scoped per tenant, and how object storage permissions are audited. Versioning is automatic and zero-copy, which is useful for rollback and reproducibility, but it also means you need a retention policy for old versions, or storage grows quietly. Index builds, including the option to use GPU support for vector indexes, should be scheduled and measured against your refresh cadence. Confirm with LanceDB which index types and build options suit your table sizes before you standardize on them.

Projects usually run long in the same places for both platforms. The most common is embedding pipeline change: switching embedding models changes vector dimensions and distances, which forces re-embedding and re-indexing of the whole corpus, so budget for that as a recurring event, not a one-off. Filter design is the second. Qdrant's JSON payload filtering is only as good as the payload schema, and LanceDB's SQL predicates are only as fast as your table layout, so both need agreed field names and indexed attributes before load testing begins. Recall testing is the third and most often skipped. Run a labeled query set against both approximate indexes and quantized settings, and record recall and latency at your expected data volume. Ask for that test plan in any written scope so it is priced and owned, not left to the end of the project.

Common questions about Qdrant and LanceDB

How much effort does it take to self-host Qdrant?

Self-hosting the open-source Qdrant engine is free under Apache-2.0, so your cost is the infrastructure and the people who run it. Qdrant is a single Rust service, but it needs block-level storage with a POSIX-compatible file system, SSD or NVMe drives are recommended, and it cannot run on NFS or S3-style object storage. Its security docs state that self-deployed instances listen on all interfaces with no authentication by default, so API keys, TLS and network controls must be configured before go-live. For production, the documented paths are Qdrant Cloud, a community Helm chart, or the enterprise operator in Qdrant Private Cloud, and the Helm chart lacks features such as zero-downtime upgrades and automatic shard rebalancing. Ask your team to size the storage, backup and upgrade routine before choosing a path.

Can LanceDB run without a dedicated platform team?

Yes, for many teams. LanceDB OSS is an embedded retrieval library under Apache-2.0 with Python, TypeScript and Rust SDKs, and the docs describe it as easy to set up on a local machine. There is no separate server to operate, so the work shifts to how your application stores, backs up and scales the Lance tables on your own storage. Zero-copy automatic versioning is included without extra infrastructure, which simplifies rollback. The trade-off is that you own concurrency, access control and multi-service sharing in your application design. Teams that need a shared, multi-client service with managed operations should price LanceDB Cloud or Enterprise, which the README describes as production-scale search with no servers to manage.

How long does a typical Qdrant or LanceDB implementation take?

No fixed duration can be given from the supplied facts, so treat any timeline as a quote item rather than a benchmark. The main drivers are the number of collections and the data volume, whether filtering and hybrid search must be tuned, the deployment path (single node, a distributed cluster with at least three nodes for quorum, or an embedded library inside an existing app), the identity and TLS integration, and the backup and upgrade runbooks. Migrating an existing corpus adds time, as does validating retrieval quality after the move. Ask for a written scope listing these items, the deliverables and the acceptance tests, since DeployOpen works to fixed scopes agreed in writing before work starts.

What does the work need from our side?

The engagement needs access to the target infrastructure, the data sources and embedding pipeline owners, and a named person who can decide on access control and retention. For Qdrant, you will need compute and block storage in your cloud account, data centers or air-gapped network, plus decisions on API keys, JWT-based per-collection permissions and audit logging. For LanceDB, you need the application owners who will embed the library, storage locations for the Lance tables, and agreement on how versions are retained. In both cases, your security team should confirm network policy, encryption and logging requirements early, because they shape the design. A scope document should name these dependencies so that delays are visible before the work starts.

Can our own engineers do this without outside help?

Often yes, if your team already runs stateful services on Kubernetes or in VMs and has experience with storage, backups and upgrades. Qdrant's open-source engine is free to run, and its documentation covers configuration, security and distributed deployment in detail. LanceDB OSS is simpler to start with because it has no server, but your engineers will still design versioning retention, concurrency and deployment packaging. The practical test is whether someone can answer questions on quorum, shard counts, snapshot restores and key rotation without guessing. If those gaps exist, a fixed-scope engagement can cover design, hardening and handover, and a retainer can cover upgrades afterward.

What changes if we move to Qdrant Cloud or LanceDB Enterprise?

Managed offerings move operational work to the vendor, but they change cost, control and compliance scope. Qdrant Cloud has a free single-node tier with 0.5 vCPU, 1 GB RAM and 4 GB disk, a usage-priced Standard tier with a 99.5% uptime SLA, and a Premium tier with a minimum spend that adds SSO, private VPC links and a 99.9% uptime SLA. Qdrant Hybrid Cloud runs managed clusters on your infrastructure, and Private Cloud is a dedicated isolated deployment, both priced through sales. LanceDB Enterprise is a distributed platform for petabyte-scale and private deployments that shares the Lance format with OSS. Ask each vendor for written pricing, data residency terms and the exit path before deciding.

What drives the cost of switching between Qdrant and LanceDB?

Switching cost depends mostly on how much application code depends on the database API, how large the indexed corpus is, and how much filtering and hybrid search logic is tuned to one engine. Both products use vectors with metadata, but their data models and client interfaces differ, so you should plan re-embedding only if the embedding model changes, and plan export and re-indexing of vectors and payloads either way. Qdrant offers snapshots for backup and restore, and a migration tool is documented for moving data in. LanceDB shares its table format between OSS and Enterprise, which can ease a move within that family. Keep the retrieval evaluation set so that you can prove parity after any move.

How DeployOpen helps choose, implement or migrate a vector database

DeployOpen plans, implements, hardens, documents and supports self-hosted open-source platforms inside your own cloud accounts, data centers or air-gapped networks. For Qdrant vs LanceDB, we start by agreeing on how retrieval will run in your environment, then write the scope before any work begins.

How we scope

Review your corpus size, metadata filters, multimodal data and update pattern before recommending either option.
Decide whether a client-server service or an embedded library fits the applications that will call the store.
Agree deliverables, timelines and exclusions in writing, since most engagements are fixed-scope.
Confirm which Qdrant or LanceDB edition, if any, your access control and scaling needs require.

How we build

Deploy the chosen store in your infrastructure with storage, network exposure and authentication configured before any data loads.
Set up filtering, hybrid search and backup or versioning routines that match the scoped retrieval workload.
Run security testing on the API surface and access controls, and record the results for review.
Connect the store to your RAG application, document each integration point and keep the configuration under version control.

How we hand over

Deliver architecture notes, runbooks and configuration documentation written for your platform and security teams.
Walk your operators through backup, restore, upgrade and scaling steps so they can run the system without us.
Provide compliance evidence and control documentation for your assessors, without issuing audit reports or certifications.
Record open decisions, such as a later move to a managed offering, so the next migration starts from a clear baseline.

How we support

Ongoing support, upgrades and continuous testing are available as retainers, scoped in writing.
Retainers can cover version upgrades, backup verification and periodic security testing of the vector store.
Ask us to confirm the support scope, response expectations and exclusions in the written agreement before signing.
Quotes follow from the written scope, so share your corpus, query volume and environment details early.