Migrate from Pinecone to Qdrant: what the move involves
Your team runs one or more Pinecone indexes and wants to self-host Qdrant in infrastructure you control. The bulk transfer of vectors and payloads is handled by the Qdrant Migration Tool, which is documented, runs as a container or binary, streams data in batches and can resume an interrupted run. The effort sits elsewhere: mapping each index's settings to a Qdrant collection, sizing and securing the target cluster, re-checking retrieval quality after the move, and switching application clients from Pinecone to Qdrant without losing traffic or data.
The Qdrant engine is open source under the Apache-2.0 license according to its GitHub repository, and the self-hosted engine is free to use. That means your cost is not a license fee. It is the infrastructure you run, the people who build and operate it, and any external help you buy. Qdrant also offers managed options (Qdrant Cloud, Hybrid Cloud and Private Cloud), which are priced separately through Qdrant. This page covers the self-hosted path only.
The effort is driven by three things: the number of Pinecone indexes you are moving, how complex the metadata on each vector is, and how you run the parallel period while both systems serve traffic. A small number of indexes with flat metadata is a short exercise. Many indexes with nested metadata, namespaces, or filters that the application relies on need more mapping, more test queries and more careful cutover planning. Ask whether your indexes use namespaces, and list which metadata fields your queries filter on, before anyone scopes the work.
This page does not give a duration. Timelines depend on your data volume, network path between the environments, the target topology and the validation you require, and none of those are fixed until your environment has been reviewed. DeployOpen agrees scope, deliverables and timelines in writing before work starts, so ask for a written scope that lists each index, the target cluster design, the validation method and the cutover steps.
Before you request a quote, collect the inventory that drives the work: index names, dimensions and distance metric per index, approximate vector counts, metadata schemas, namespaces, the query patterns your applications use, and your required recall and latency targets. Confirm the Pinecone API limits and export options with Pinecone for your account, since those affect batch planning. Also decide who will hold the Qdrant API keys and TLS certificates, because a default self-deployed Qdrant instance has no authentication and listens on all interfaces.
What maps cleanly from Pinecone to Qdrant and what needs redesign
This table compares Pinecone concepts with their Qdrant equivalents and marks each as maps cleanly, maps with changes, or needs redesign. Use it to scope the review of each index before any data moves. The main takeaway is that vectors and top-k queries carry over most directly, while namespaces, sparse or hybrid usage, and access control need decisions before the migration starts.
| Pinecone concept | Qdrant equivalent | Migration status |
|---|---|---|
| Index | Collection | Maps with changes. Confirm vector size and distance metric match the source index before creating the collection. |
| Vector with ID | Point with ID and vector | Maps cleanly. Confirm your ID format is accepted as-is in the migration tool documentation. |
| Metadata | Payload | Maps with changes. Check how field types and nested values are converted in your sample run. |
| Namespace | No direct equivalent documented here | Needs redesign. Confirm the treatment in the migration tool documentation and decide whether to use separate collections or a payload field. |
| Query filter | Payload filter | Maps with changes. Filter syntax differs, so rewrite each filter and compare results on the same test queries. |
| Top-k query | Search with a result limit | Maps cleanly. Confirm scores and ranking match after the move by re-checking recall. |
| Sparse or hybrid vectors | Sparse or named vectors in collection configuration | Needs redesign. Confirm supported types in the migration tool documentation and against your own index configuration. |
| API keys and hosting | Self-managed API keys, TLS and JWT access control | Needs redesign. Qdrant instances are open on all interfaces with no authentication until you configure them. |
Choosing the self-hosted Qdrant target before the first import
Settle the target topology before any vectors move, because the migration tool writes into whatever collection layout already exists. Start with one question: does this workload need a single node or a distributed cluster? Qdrant's distributed mode uses the Raft consensus protocol to manage cluster topology and collection structure, splits each collection into shards across nodes, and keeps additional copies through a replication factor. The distributed deployment documentation recommends at least three nodes for production so that quorum survives a node failure. Choose a shard count that is a multiple of the node count so shards spread evenly. Set the replication factor in advance as well. A factor of 2 doubles the storage each collection consumes, so size disks from the replicated total, not from the raw vector count.
Storage is the second decision. Qdrant requires block-level storage with a POSIX-compatible file system for its data directory. It cannot run on network file systems such as NFS or on object storage such as S3, and the installation documentation recommends SSD or NVMe drives. Before the first import, confirm that each node's data volume in your cloud account or data center meets these conditions. In a Kubernetes deployment, check that the storage class or mount behind the volume is block-backed rather than a shared network mount. Ask the infrastructure team to measure free capacity against the replicated size from your topology plan, and leave headroom for snapshots. Snapshots can be written to S3-compatible storage since v1.10.0, but that is a backup destination, separate from the data directory.
A self-deployed Qdrant instance is not secure by default. The security documentation states that instances listen on all network interfaces and have no authentication configured, so the control set must be in place before the migration tool connects. Qdrant supports admin and read-only API keys and TLS for REST and gRPC, available since v1.2.0. JWT-based access control with per-collection read or write permissions arrived in v1.9.0, and JSON audit logging of authenticated API operations arrived in v1.17.0. Decide which identity the migration job uses, which application clients get read-only access, and where audit logs will be shipped. Run the migration job against a non-production node first, and confirm the exact credential and TLS options the migration tool accepts for your target version.
Choose the production path before building anything. Qdrant documents three: the 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, including zero-downtime upgrades and automatic shard rebalancing. Teams that expect to upgrade or add nodes under load should either plan manual procedures with a tested runbook or evaluate the operator. Pin the version before the first import. The latest release on GitHub is v1.19.2, published October 5, 2026. Confirm that version against the Qdrant Migration Tool's supported versions, and confirm your snapshot restore path as well, since snapshots restore only to clusters running the same minor version or the next minor version.
Documented import paths for moving Pinecone data into Qdrant
The Qdrant Migration Tool is the documented route from Pinecone to self-hosted Qdrant. The cards cover what the tool is, which credentials each side needs, how the same runbook can extend to other sources, and which details to confirm against current documentation before the run.
Qdrant Migration Tool
The Qdrant Migration Tool is the documented path from Pinecone into Qdrant. It ships as a container image or a standalone binary, so you can run it from a host or pipeline inside the network that already reaches both systems. It streams data in batches and resumes interrupted runs, which matters for large indexes where a network drop should not force a full restart. Confirm the delivery format and resume behavior against the current tool documentation before you schedule the run window.
Pinecone source access
The source side needs a Pinecone API key that can read each index you plan to move, plus the exact index names as they appear in your Pinecone project. Ask the platform owner to confirm that the key is scoped to the right project and that no index is missing from the list. Record the names in the runbook, because a mistyped index name can produce a partial migration that looks successful. Keep the key in your secrets store, out of shell history and job logs.
Qdrant target URL and API key
The target side needs the URL of the self-hosted Qdrant endpoint and an API key that can write to the destination collections. A default Qdrant instance listens on all network interfaces without authentication, so confirm that API keys and TLS are enabled before any data arrives. Where your Qdrant version supports per-collection permissions, scope the key to the target collections. Confirm the key types and TLS setup in the Qdrant security documentation.
One runbook for mixed stores
The same tool covers other sources, including Weaviate, Milvus, Elasticsearch, OpenSearch, Chroma, Redis, MongoDB and FAISS, so a team with several vector stores can keep one runbook. Each source has its own credentials and options, so the runbook should hold a separate section for each source with its own settings. Confirm the current source list and each source's requirements in the documentation before you assume a source is covered.
pgvector path on a sibling page
Teams moving embeddings out of PostgreSQL with pgvector use a separate path, the tool's pg subcommand with parallel workers, which is covered on the sibling page on migrating from pgvector to Qdrant. Pinecone's index model and pgvector's table model differ, so Pinecone settings should not be reused for a pgvector source. Read that page when a PostgreSQL source is part of the same program.
Verify syntax and index types
Command syntax, flag names and the Pinecone index types the tool supports can change between releases, so confirm them against the current migration tool documentation before you write the job. Avoid relying on examples from memory or older posts. Record the tool version you deploy in the runbook, test the exact command against a small non-production index, and repeat that test whenever the version changes.
How the Qdrant Migration Tool moves vectors, batches and resumes
The Qdrant Migration Tool works in a fixed order: it connects to the source Pinecone index with credentials you supply, creates the target Qdrant collection, streams vectors and their metadata in batches, and records progress as it goes so an interrupted run can resume. Knowing this order helps you find where a failure happened. A connection failure should leave nothing written to Qdrant. A failure after collection creation means the collection exists and may be partly filled, so inspect it before rerunning. Confirm in the migration tool documentation where progress is recorded and whether a resumed run skips or re-sends the last batch.
Batch size is a tuning decision, not a fixed setting. Larger batches reduce round trips but raise memory use on the migration host and load on the network and the Qdrant node receiving writes during each insert. Smaller batches are gentler on those resources but take longer overall. The right value depends on three things: vector dimensionality, payload size per record, and the CPU, memory and disk available on the target node. Large metadata objects can make a batch that is safe for small payloads too heavy. Set the value during a pilot on a representative slice of the index, watch node memory and write latency, and carry the chosen value into the full run.
After every run, verify the result before anyone switches application clients. Start with vector counts: compare the number of vectors in the Pinecone index with the number stored in the Qdrant collection, accounting for any records you excluded on purpose. Then check ID coverage by sampling IDs from the source and confirming each one exists in the target, paying particular attention to the range the tool processed last before an interruption. Check payload fields next. Confirm that metadata keys and value types match, especially fields your application filters on. Keep this comparison as a script in your runbook so the same checks run again after a resumed run.
The collection configuration needs its own check, because copying data does not confirm the target was set up as intended. Confirm that the vector dimension in the Qdrant collection equals the dimension of the Pinecone index, and that the distance metric matches the metric the source index used. A mismatch in either can produce search results that look plausible but are wrong. Also review the storage setup on the Qdrant node. Qdrant requires block-level storage with a POSIX-compatible file system, and network file systems and object storage are not supported. Record each setting in the migration log so the audit trail shows what was in place at cutover.
Pinecone to Qdrant migration sequence: inventory, pilot, parallel run and cutover
This sequence moves Pinecone indexes into self-hosted Qdrant in stages, so each stage is checked before the next begins. It produces a Qdrant deployment holding the same vectors and payloads, application clients reading from it, and a documented point at which Pinecone is retired. The parallel-run length and cutover window are set in the written scope.
Inventory indexes, namespaces, metadata and queries
Your team and DeployOpen list every Pinecone index, namespace, vector dimension, distance metric, metadata field and filter the application uses. The list also captures each query pattern, including result counts, metadata filters and whether records are fetched by ID. This inventory drives collection design in Qdrant, including which payload fields need indexes for filtering. The step is done when application owners have signed off the inventory and every query pattern has a named owner and a test case for later recall checks.
Pilot one representative index
Choose one index that carries the widest mix of metadata and query patterns, then migrate it with the Qdrant Migration Tool into a collection built to the inventory. Run the transfer in batches and record timing for each batch, so the batch size can be tuned on your infrastructure. Interrupt the run on purpose and resume it to confirm the tool continues from its last point without duplicating or dropping vectors. Compare vector counts and sampled records between source and target. The pilot is done when counts match, the resume test passes and the team has agreed batch settings for the bulk transfer.
Run the full bulk transfer with a write plan
Migrate the remaining indexes and namespaces using the settings proven in the pilot, starting with the lowest-risk indexes. Before the run starts, decide with application owners how writes to Pinecone during the transfer will be handled. Either Pinecone writes are frozen for the transfer window, or every write is replayed into Qdrant after the bulk copy. Record which choice applies to each index. The step is complete when per-namespace vector counts match, sampled payloads agree, and the log shows how every failed batch was retried.
Compare application traffic in a parallel run
Send the same application queries to Pinecone and Qdrant and compare the results, including overlap of returned IDs, ordering, filtered results and latency. Recall is re-checked against the query test set from the inventory, so any drop in result quality after the move is visible before users depend on it. The parallel-run length is set in the written scope and agreed with application owners. The step closes when the agreed comparison criteria are met on every query pattern, and any gap has an owner and a planned fix.
Cut over application clients to Qdrant
Change application configuration so clients read from Qdrant during the cutover window set in the written scope. Keep the Pinecone endpoint and credentials available so the team can switch back if a defect appears. Confirm that the application's write path follows the freeze or replay decision made during the bulk transfer. The step is done when production queries are served from Qdrant, error rates and recall checks stay within the agreed criteria, and the rollback steps have been rehearsed.
Decommission Pinecone indexes after retention
After the retention period agreed in advance, delete the Pinecone indexes and revoke their API keys. Before deletion, confirm that Qdrant backups or snapshots of the migrated collections exist and that a restore has been tested in a non-production environment. Record the decommissioning date and final vector counts in the migration record. The step is done when Pinecone holds no production data, its API keys are revoked, and the record is filed with the operations runbook.
Re-checking recall and switching application clients after the Pinecone move
A successful transfer does not prove that retrieval quality is unchanged. The migration tool can finish with every vector and payload accounted for while the search results your application returns still differ, because index parameters, distance metric handling, filter semantics and score scales are not identical between Pinecone and Qdrant. Before any application points at the new cluster, the team should run a recall check that answers a narrower question: for the queries that matter to the business, do the top results still make sense, and do they still rank in an acceptable order?
Build the check from a fixed set of representative queries with known expected results. Ask the team that owns the content to select queries across the main user journeys, including a few that depend on payload filters such as tenant, region, document status or date range. For each query, record the expected documents or a minimum set that must appear in the top results. Run the same queries against Pinecone and Qdrant with identical filters and identical top-k values, then compare overlap of the top results, the position of expected documents and how the similarity scores are distributed. Scores from the two systems will not be numerically equal, so compare ordering and gaps rather than raw values.
The acceptance criteria belong to the team, and they should be written down before the test runs, not after the numbers arrive. Agree on the minimum overlap of top results, the maximum acceptable drop in expected-document rank, the filter cases that must return identical sets, and who signs off. The same agreement should name the rollback trigger: the condition under which the team reverts clients to Pinecone during cutover, and how long that revert path must stay available. DeployOpen will document these thresholds and the trigger in the written scope, but the values are the team's decisions and depend on the risk the application carries.
Client switching is a code change with its own risks. Replace the Pinecone SDK calls for upsert, query and delete with the Qdrant client calls, and translate each Pinecone metadata filter into the equivalent Qdrant filter expression, testing the translation against the recall query set. Keep the Pinecone path behind a feature flag or configuration switch so that a single setting can route traffic back. For the new endpoint, move the Qdrant API key into the same secret store as other credentials, confirm that the key is an admin key only where needed and a read-only key where the application only searches, and enable TLS on the REST and gRPC endpoints the client uses. Confirm the certificate chain and hostname validation in each environment, not only production.
Plan the cutover in stages that the rollback switch can undo. Route a small share of read traffic to Qdrant first, watch result quality and latency against the agreed criteria, then widen the share if the criteria hold. Keep writes flowing to both systems for as long as the rollback path must remain open, and decide in advance when the Pinecone index can be retired. Ask DeployOpen to confirm in the written scope which of these steps are included, which the team performs itself, and how monitoring for the new endpoint will be set up.
What drives the cost and duration of a Pinecone to Qdrant migration
This table lists the effort drivers that most change the cost and duration of moving indexes from Pinecone to self-hosted Qdrant, with what raises each one and what keeps it down. One-time work (inventory, pilot, transfer, client changes, testing, decommissioning) and recurring work (self-hosted infrastructure, storage replicas, upgrades, monitoring, on-call) appear within the rows, so read each row for both. Cost is quoted from the written scope, so ask each bidder to price every driver line separately.
| Driver | What raises cost or duration | What keeps it down |
|---|---|---|
| Index count and size | Many indexes and large vector counts add one-time export, transfer and validation runs for each collection | Complete the inventory first, group small indexes, and run the largest index as the pilot |
| Vector dimensions and count | Higher dimensionality and more vectors increase transfer time, disk needs and memory sizing, which recur in hosting | Confirm dimensions and any sparse vector use before sizing, and test batch size on a copy |
| Metadata and filter complexity | Rich metadata and nested filters need payload indexes and query rework in every client | Map metadata fields to Qdrant payload types early and index only the fields filters use |
| Namespaces and query paths | Each Pinecone namespace needs a mapping decision, and each client query path needs its own recall and result checks | Decide the namespace mapping once and list every query path so the pilot covers them |
| Parallel-run length | Running Pinecone and Qdrant side by side adds hosting cost and comparison effort until the run ends | Agree exit criteria for recall and latency in writing before the run starts, then end it once they are met |
| Target topology | Distributed mode needs at least three nodes, shard planning and replicas, which raises recurring infrastructure and storage cost | Choose single-node or distributed based on written availability needs, and set the replication factor deliberately |
| Security and identity integration | API keys, TLS, per-collection JWT access and audit logging each add configuration, secret handling and review effort | Reuse existing secret stores and identity patterns, and define access roles before the client cutover |
| Backup and snapshot requirements | Snapshot retention, restore drills and cross-cluster restores add recurring storage cost and one-time test effort | Keep restores within supported Qdrant version pairs and use approved S3-compatible storage for snapshots |
| Team's Kubernetes and automation | Manual deployment on tooling the team does not already run lengthens the build and increases on-call load | Existing GitOps, monitoring and backup pipelines shorten the build and lower recurring operations effort |
What usually goes wrong in a Pinecone to Qdrant cutover and how to prevent it
A Pinecone to Qdrant move rarely fails in one dramatic place. Most problems come from security defaults, storage and cluster sizing, interrupted batch runs, and quiet changes in search results that only show up after users are already on the new index. Each card below names one failure point and the check that prevents it.
Target deployed without authentication
Self-deployed Qdrant listens on all network interfaces and has no authentication configured by default. A migration target stood up quickly for loading can end up reachable from networks it should never see, with the migrated vectors and payloads exposed. Before the first batch runs, set an admin API key, a read-only key for application clients, TLS for REST and gRPC, and bind the listeners to the interfaces your network design allows. Confirm with an unauthenticated request from outside the approved network that it fails.
Storage on NFS or object storage
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. A volume that mounts cleanly but sits on an unsupported backend can work during a small test and then fail or corrupt under the full migration load. Confirm the volume type with your storage team, prefer SSD or NVMe, and record the mount details in the cutover runbook before any collection is created.
Replication and node count sized too small
In distributed mode, a replication factor of 2 doubles stored data, and the Qdrant distributed deployment docs recommend at least three nodes for production so quorum survives a node failure. Teams that size the cluster from the Pinecone index size alone often discover the shortfall during the load. Calculate disk for the replication factor and headroom for the migrated data, choose a shard count that is a multiple of the node count, and confirm the plan with a capacity test on a copy of the data.
Batch sizes that overload the target
The Migration Tool streams data in batches, and a batch size chosen for Pinecone's read path can push a freshly deployed Qdrant node into memory pressure, slow indexing or timeouts. Start with a small batch size, measure ingest rate and node resource use over a sample run, then raise the size only while the cluster stays healthy. Record the value that worked in the runbook so a retry uses the same setting.
Resumed runs that duplicate or skip points
The Migration Tool resumes interrupted runs, but a restart without checking the target can leave duplicate points from the batch in flight or gaps where a batch never completed. Before restarting, compare the target point count with the source count and sample known point IDs across the range that was running. Only resume from a position you have confirmed against the target, and repeat the count check when the run finishes.
Recall that differs and is found after cutover
Vector index settings, distance metric and quantization choices can change which neighbors come back for the same query, so recall on Qdrant may differ from Pinecone without any error. If nobody measured this before switching, users find it in production. Build a recall test set from real queries with known relevant results, run it against both indexes before cutover, and set a pass threshold agreed with the application owner.
Filter translation errors that change results
Metadata filters written for Pinecone's syntax do not always map one to one onto Qdrant filter conditions, and a mistranslated condition can return a plausible but wrong result set with no error. Review every filter the application sends, including range, list and negation conditions, and compare filtered query results from both systems on the recall test set. Treat any mismatch as a blocker until the cause is understood.
Snapshot restores blocked by minor-version rules
Qdrant snapshots can be restored only to clusters running the same minor version or the next minor version. A backup taken on one release and restored into a target that has since been upgraded, or a rollback plan that assumes an older binary, can fail at the moment it is needed. Pin the target version for the migration, record it in the runbook, and rehearse a snapshot restore into a clean cluster on that exact version before the cutover date.
Questions teams ask before moving from Pinecone to Qdrant
How much does a Pinecone to Qdrant migration cost?
The cost has three parts: the engagement, the infrastructure, and your team's time. The engagement is priced from a written scope, and DeployOpen does not publish a price list, so request a written scope and quote that lists each deliverable. The Qdrant engine is free to self-host under the Apache-2.0 license, so the recurring cost is the infrastructure: compute nodes, SSD or NVMe block storage, backups, and monitoring. Replication multiplies storage, since a replication factor of 2 doubles it. Keep Pinecone running during the parallel period, because its subscription continues until you cancel it. Ask the quote to separate one-time migration work from ongoing support and upgrades, and to name who pays for test environments and any Pinecone-side costs you will carry.
How long does it take to move indexes from Pinecone to Qdrant?
Duration depends on the size and number of indexes, how many application clients must switch, and whether you can freeze writes. The Qdrant Migration Tool streams data in batches and resumes interrupted runs, so a failed transfer does not force a full restart, but copy time still grows with vector count, dimensions, and payload size. Validation, recall testing, and client cutover often take as much planning as the copy itself. No duration figure applies to your environment until someone inventories your indexes and sizes the test run. Ask for a timeline in the written scope that shows phases, dependencies on application owners, and the window each cutover needs.
Can our own team run the Qdrant Migration Tool without outside help?
Yes, a team with the right skills can run the documented tool alone, but the risk sits outside the copy step. The tool runs as a container or binary, supports Pinecone as a source, and resumes interrupted runs. The harder work is operating a new cluster: storage that must be block-level with a POSIX-compatible file system (network file systems and object storage are not supported), securing a deployment that listens on all interfaces with no authentication by default, sizing for replication, and designing the cutover and rollback. Assess your team on those skills first. DeployOpen plans and delivers this work as fixed-scope engagements, and the written scope will state which parts your staff own.
What does our team need to provide for a Qdrant migration?
Five inputs decide whether the work starts on schedule:
- Index inventory: index names, dimensions, distance metric, vector counts, and payload fields.
- Application owners: a named person for each index who approves query behavior and the client switch.
- A test environment: a non-production Qdrant cluster and a copy of representative data.
- Access to both systems: read access to the Pinecone indexes and administrative access to the target Qdrant deployment.
- A write-freeze decision: whether writes to Pinecone can pause during the final copy, and for how long.
Missing any of these usually moves the start date, so confirm each one with the scope.
Is self-hosted Qdrant free, and where do the real costs sit?
The engine is free to self-host, and most of the cost sits in running it. Qdrant is licensed under Apache-2.0 and the open-source engine has no license fee. The costs that recur are infrastructure and operations: nodes (the distributed docs recommend at least three for production so quorum survives a node failure), SSD or NVMe storage, and replication overhead. Production paths also differ. The community Helm chart lacks enterprise features such as zero-downtime upgrades and automatic shard rebalancing, while the enterprise operator is part of Qdrant Private Cloud, which is priced through Qdrant sales. Security configuration (TLS, API keys, per-collection JWT access, audit logging), monitoring, backups, and upgrades all need an owner. Qdrant Cloud and Hybrid Cloud pricing are separate from self-hosting.
Will search recall change after moving from Pinecone to Qdrant?
Recall can change, and the result depends on index configuration and how the vectors were copied. The migration moves vectors and payloads, but differences in distance metric handling, index parameters, quantization settings, or filtering behavior can shift which results come back. Measure it rather than assume it. Build a fixed set of representative queries with known relevant results, run them against Pinecone and Qdrant, and compare overlap in the top results, including filtered queries. Tune Qdrant parameters if the gap is outside the threshold your application owner accepts. Set that threshold before the migration starts, and record the results as evidence for the cutover decision.
What is the rollback plan if the Qdrant cutover fails?
Keep the Pinecone index unchanged until Qdrant passes every check, so the client switch is the reversible step. Take a snapshot of the Qdrant collections before cutover. Qdrant snapshots restore only to clusters on the same minor version or the next minor version, so record the versions of both source and target and keep upgrades out of the cutover window. Snapshots can be stored in S3-compatible storage since v1.10.0, which gives you an off-cluster copy. Any writes accepted by Qdrant after cutover must be reconciled before you point clients back to Pinecone, which is why the write-freeze decision matters. Confirm with Pinecone how long the source index stays available and what it costs to keep it during the rollback window.
How DeployOpen scopes, delivers and supports the Pinecone to Qdrant migration
DeployOpen plans the Pinecone to Qdrant move as a written, fixed-scope engagement inside infrastructure you control. The work covers collection design, running the Qdrant Migration Tool, re-checking recall and switching application clients, with ongoing support available as a retainer.
How we scope
- The written scope lists each Pinecone index to move and its target Qdrant collection.
- We record vector dimensions, distance metric, payload fields and the filters your applications depend on.
- The scope names the Qdrant deployment path, storage requirements and who owns each cutover step.
How we build
- We stand up self-hosted Qdrant with authentication and TLS enabled before any data moves.
- We run the Qdrant Migration Tool in batches, tuning batch size against source limits and your network.
- We re-check recall and query results on the migrated collections against the Pinecone baseline before switching clients.
How we hand over
- You receive a runbook covering collection settings, migration commands, resume steps and rollback points.
- Recall test results, access configuration and client connection changes are documented for your team.
- We walk your engineers through the Qdrant operations tasks needed after cutover, including snapshot procedures.
How we support
- Ongoing support, upgrades and continuous testing are available as a retainer after handover.
- Qdrant version upgrades are planned against the snapshot restore rules between minor versions.
- Confirm with DeployOpen which support tier, response expectations and deliverables your retainer includes.
