Every source GILD analyzed, newest first. Each says who the source is, how it knows, whether it still applies and which findings it counts toward. Open a count to see only its sources.
Source
65 sources in Teams and technical leaders, with product documentation Showing all 72 sources
Negative
GitHub issue by dstocktonGitHub issue
After raising replication beyond what the disks could hold, around 1 in 6 attempts crashed a node, with or without the disk quota enabled. A fix was still open when the page was stored.
Negative
GitHub issue by rsicartGitHub issue
Random reads and writes stalled for about 62 s before returning 200, which the reporter traced to idle internal gRPC connections dropped by the network. The issue was closed by a linked change.
Negative
GitHub issue by jkbeGitHub issue
The HNSW links residency bug led to about 2x memory consumption under v1.18.3 compared to v1.17.1, and his team persistently ran into OOM on startup, which made v1.18.3 a broken release for them.
Mixed
GitHub pull request by doogle-ossGitHub pull request
PyPI did not show FastEmbed's license as Apache 2.0, so his organization could not use the library. A maintainer closed his pull request, saying a fix had been sitting on main unreleased, and that once it was released the license on PyPI was correct.
Positive
PeerSpot review by Pawel CisloPeerSpot review
Raw vector retrieval and payload filtering in the reviewer's RAG assistant take under 15 milliseconds. The reviewer wants more native fusion tooling.
Counts toward
Negative
GitHub issue by bvolkmerGitHub issue
The Python client's get_snapshot always fails, because snapshots are tar files and the wrapper parses JSON. A maintainer replied that the clients do not expose it and to download snapshots through the raw REST API.
Negative
GitHub issue by scheung-lbgGitHub issue
A client's package firewall quarantined fastembed 0.8.0 under a banned Proprietary-Clause license finding, so the download failed. A maintainer said the finding is probably about the licenses of some of the models.
Mixed
PeerSpot review by Uzair FaruqiPeerSpot review
Qdrant is good and Pinecone is much more costly. The reviewer runs it self-hosted in Docker, wants an option outside containers and has not used Qdrant Cloud.
Counts toward
Negative
GitHub issue by ronenperezGitHub issue
After upgrading 1.18.1 to 1.18.3, each node gained 19 to 25 GB of heap memory and latency regressed, and downgrading to v1.18.1 fully reversed both. A maintainer closed the issue as implemented in Qdrant 1.19.0.
Mixed
GitHub issue by cainzhongGitHub issue
The team's scan of the latest qdrant/qdrant image reported 4 critical and 41 high vulnerabilities. A maintainer replied that all come from debian:13-slim base packages that Qdrant does not use at runtime, and another doubted parts of the scan.
Positive
Reddit comment by sinevilsonReddit comment
Runs Qdrant in production with many collections over millions of points, and says the operations take work once, maybe twice, to automate everything.
Negative
Reddit post by SilverConsistent9222Reddit post
Wasted time setting up Qdrant way too early, when a local Chroma was completely fine for the corpus the team had.
Negative
PeerSpot review by Amrit DashPeerSpot review
Running Supabase and Qdrant meant every operation updated both, which risked the two falling out of sync when an API call failed and complicated the developers' work.
Mixed
PeerSpot review by Vedant WaniPeerSpot review
Recommendation accuracy rose from 45 to 50% with FAISS to around 85 to 95% with Qdrant. The local file lock forced the team to run Qdrant as an always-on Docker service.
Mixed
Practitioner blog post by Manfred DreesePractitioner blog post
Once his team indexed its filters properly, P99s dropped from hundreds of milliseconds to single digits, and quantization cut the memory footprint by over 4x with negligible impact on recall. When WAL recovery itself hits the Kubernetes memory limit, the pod is killed again and ends in a CrashLoopBackOff until someone bumps the limits by hand.
Negative
GitHub issue by kaancfidanGitHub issue
A nested payload filter over a subset of 500k to 1M vectors gave 20-50 queries per second, against about 1500 for global search. A maintainer said regular payload indexes need an extra step that reads the original payload.
Negative
Hacker News comment by arlogilbertHacker News comment
On 3 text collections of 10k items each, averaged over about 500 queries per collection, Qdrant answered in 21.1ms against 5.9ms for LanceDB and 0.8ms for Zvec. The author ran Qdrant and LanceDB with Inverse Document Frequency enabled and calls that a slight performance hit.
Mixed
GitHub issue by bytestreamGitHub issue
The staged upgrade from 1.10.1 went smoothly until the final step, where the 1.16.3 Linux release binary could not open storage that still relied on RocksDB. The maintainer's workaround was to start 1.16.3 once from the official Docker image.
Positive
Hacker News comment by sa-codeHacker News comment
Calls Qdrant a good default choice because the same engine runs in memory for development, on a hard drive for small deployments, and for large workloads.
Negative
GitHub issue by 02denoGitHub issue
Queries that worked before the upgrade made Qdrant 1.16.3 restart with a panic and then OOMKilled errors. A Qdrant maintainer, timvisee, read the jump in memory as more than 55GB.
Positive
Hacker News comment by p410n3Hacker News comment
Runs Qdrant for 300,000 products with weekly updates and pays 250€ a month for all Qdrant instances across all environments.
Counts toward
Negative
GitHub issue by ffletcherrGitHub issue
With 325 GiB used for about 33 million points, removing 5 million points freed only about 10 GiB, and deleting 5 million more pushed disk usage to 100%. A maintainer replied that Qdrant needs headroom on disk to work.
Mixed
GitHub issue by boris-petrovGitHub issue
Upgrading from 1.16.0 to 1.16.1 made one of the reporter's tests fail consistently. Qdrant released 1.16.2 with the fix a week later, and the reporter thanked the team for the quick fix.
Mixed
GitHub issue by DragosGhineaGitHub issue
In one developer's test after upgrading to 1.16, a filtered query with ACORN enabled took 3.65 ms against 4.83 ms without it, with identical top 10 scores. A Qdrant contributor said a filter leaving too few vectors, under full_scan_threshold, skips the index, and the developer found the filter matched only 999 points.
Positive
Hacker News comment by couAUIAHacker News comment
Lists Qdrant under what worked for the company's internal search: easy to use, solid performance, and well suited to self-hosting.
Negative
GitHub issue by fraenkiGitHub issue
Raised that nothing stopped unauthorized nodes from joining a cluster. Qdrant's self-hosted security page now says that as of v1.20.0 the receiving peer checks the key by default, and that without an API key no authentication layer is installed.
Mixed
GitHub issue by ApidcloudGitHub issue
With large string payloads, the official gRPC client averaged 226.307 ms per batch over 100 runs against 59.447 ms for REST. TypeScript client v1.16.0 made compression optional.
Positive
Reddit comment by eujzmcReddit comment
The team picked Qdrant for simplicity and would only switch to Milvus if scale really demanded it.
Negative
Hacker News comment by k9294: Comparing Qdrant and Zilliz Cloud against turbopuffer on operational simplicityHacker News comment
Tried Qdrant and Zilliz Cloud and says turbopuffer is ahead of both on operational simplicity.
Negative
GitHub issue by ml-abdulaGitHub issue
Queries took around 7 seconds with everything on disk, against a target under 1 second. A maintainer said Qdrant normally recommends keeping quantized vectors and HNSW in RAM.
Positive
Reddit comment by junegirl_198Reddit comment
Self-hosted Qdrant is lightweight, easy to manage and much cheaper than Pinecone or Zilliz as you scale.
Negative
GitHub issue by eekGitHub issue
On a 4,777,333-point collection with a tenant filter, only 34% of points were found in the top 100 results, against 100% with exact search. The reporter suspects a past is_tenant config change, and the problem recurred on v1.15.5.
Negative
GitHub issue by orhanraufGitHub issue
After a pod restart, shard recovery ran at 3 items/second while using 1 CPU core of 12 and 1.1GB of 100GB RAM. The issue was open when stored.
Negative
GitHub issue by morsikGitHub issue
Nodes returned data inconsistently. A maintainer said points created in v1.15.0 might be related to a known bug, which the team confirmed. 4 of 6 nodes had indexed the collection on that version, so fixing it meant replacing about 1/3 of the data.
Negative
GitHub issue by aalekhpatel07GitHub issue
Recovering a collection from its snapshot loses the collection's aliases. Qdrant declined to change the snapshot and advises migrating aliases with a separate request.
Negative
GitHub issue by amircodotaGitHub issue
After the team moved vectors to disk, disk usage in the production database grew fast until it ran out of space, with 492G in temp_segments against 67G of segments. A maintainer advised upgrading from 1.8.4 through each minor version.
Negative
GitHub issue by rajanpatilGitHub issue
Snapshot recovery failed with Azure File Share storage. A Qdrant maintainer replied that Azure File Share is not supported and that data corruption is possible later even where collections seem to work.
Negative
GitHub issue by bdeckelGitHub issue
An upgrade from 1.13.4 to 1.13.5 failed and the pipeline rolled back, after the team's own deployment had added a step that removed raft_state.json. A maintainer said the file should never be removed, and advised putting the original back if kept, otherwise rebuilding the cluster.
Mixed
Hacker News comment by boboshaHacker News comment
Ran Qdrant in production and recommends it, but the team is moving everything to Postgres with pgvector for fewer moving parts, because keeping data synced between Postgres and Qdrant was a pain.
Positive
Hacker News comment by k9294: A relatively small startup running managed QdrantHacker News comment
Managed Qdrant for vector search works well, is quite simple and scales well. It needed an API boundary layer and more work upfront, and was quite low-maintenance in the long run.
Counts toward
Negative
GitHub issue by MohammadsakniniGitHub issue
During a bulk upload toward 89 million points in Docker on WSL, the container started at roughly 7GB of RAM and kept climbing to the reporter's 30GB limit after 5 to 6 million points, with vectors and HNSW on disk. The reporter found it happened only under Docker, and a maintainer recommended a Linux native machine for production.
Mixed
Reddit comment by Hungry-Horror-7577Reddit comment
At 32k points, Qdrant's local mode warns it is not recommended above 20k points. The author finds it fine for one process doing eval runs and would move to the server for anything concurrent.
Mixed
Hacker News comment by cbcoutinhoHacker News comment
Says the biggest cost driver of a system like theirs is the memory required to host the vector database.
Negative
Reddit comment by tenequmReddit comment
Migrated from Qdrant to LanceDB because the use case needed a database that does not require a running server instance.
Positive
Hacker News comment by vunderbaHacker News comment
Uses Qdrant with Qwen3-VL captions to semantically search about 100k image assets and has no complaints so far.
Counts toward
Positive
Reddit comment by Master_Page_116Reddit comment
Moved a RAG pipeline off Pinecone to Qdrant a few months earlier, and the framework's vector store layer made the move easy.
Counts toward
Negative
Reddit comment by Zealousideal_Wolf624Reddit comment
Uses Qdrant with mild success, but it takes a lot of resources for acceptable latency at scale, and the author wonders whether turbopuffer is a good alternative.
Negative
GitHub issue by avvertixGitHub issue
Starting 1.16.1 over 1.16.0 in Docker gave a startup panic. A maintainer replied that data on a Windows-mounted volume is not supported because those mounts do not support all IO operations, and the reporter fixed it with named volumes.
Pages from Qdrant, used to check facts for every group.
Qdrant supports dense vectors for semantic similarity, sparse vectors for full-text search, and multivector search for objects with multiple embeddings or late interaction models such as ColBERT. Qdrant attaches a JSON payload to vectors and filters on it with keyword matching, full-text, numeric range and geo-location conditions, combined with should, must and must_not clauses. Qdrant combines multiple vectors in a single query and merges the results with fusion strategies such as Reciprocal Rank Fusion (RRF) and Distribution-Based Score Fusion (DBSF). Qdrant has built-in quantization that cuts RAM usage by up to 97%, with a tunable trade-off between search speed and precision. Qdrant scales horizontally with sharding and replication and updates or resizes collections with zero downtime. Qdrant provides a REST API with an OpenAPI 3.0 specification and a gRPC interface. On Qdrant Cloud, REST runs on port 6333 or 443 and gRPC on 6334, and clients exist for Python, TypeScript, Rust, Go, Java and .NET. Qdrant is licensed under the Apache License, Version 2.0, which grants a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license.
Qdrant stores and searches vectors from any embedding model. A dense vector can have between 1 and 65,536 dimensions.
Managed deployments on Qdrant Cloud have access to Cloud Inference, where Qdrant Cloud hosts a range of embedding models, some for free. BM25 and all-MiniLM-L6-v2 are free to use, including on free-tier clusters. Cloud Inference is a Managed Cloud feature, and Hybrid Cloud and self-hosted deployments use client-side inference or in-cluster BM25. On Qdrant Cloud, Cloud Inference can call embedding models hosted by OpenAI, Cohere, Jina AI and OpenRouter, which requires your own API key for the provider.
FastEmbed is Qdrant's lightweight Python library for embedding generation. Self-hosted Qdrant uses client-side inference, for example with FastEmbed, and the Python client runs inference locally with FastEmbed when cloud inference is off.
Qdrant Cloud registration works with an email address and passwordless login, a Google account, a GitHub account, or an enterprise SSO connection. A free tier cluster needs no credit card.
LangChain distributes the Qdrant integration as the langchain-qdrant partner package, which supports dense, sparse and hybrid retrieval.
Qdrant's framework integrations list includes LlamaIndex, a data framework for building LLM applications.
The Haystack integration is QdrantDocumentStore, a separate qdrant-haystack package that the Qdrant team maintains and updates independently of Haystack.
Qdrant runs self-hosted on 64-bit x86_64 or ARM64 systems and needs block-level storage with a POSIX-compatible file system. It does not work with NFS or S3. For testing or development it runs as a container or binary, and the Kubernetes Helm chart has community support only.
Qdrant Managed Cloud runs natively on AWS, GCP and Azure. Free clusters have a limited choice of cloud providers and regions. Paying through a cloud marketplace limits cluster creation to paid clusters in that provider's regions.
On Qdrant Cloud, data in Qdrant clusters is stored only in the cluster's deployment region, and telemetry and logs are pushed to Qdrant's US management plane. Qdrant holds SOC 2 Type 2, with compliance reports in its Trust Center for Qdrant Cloud and for Hybrid and Private Cloud. The security page marks compliance documentation as not applicable to self-hosted Qdrant. Qdrant is HIPAA certified, and a Business Associate Agreement is available for Qdrant Managed Cloud. PHI handling requirements are discussed with Qdrant. On Qdrant Cloud, every storage volume is encrypted at rest, Qdrant does not access data stored in Qdrant clusters, and API keys are stored as hashes. Premium customers can encrypt data at rest with their own keys.
The Qdrant Cloud Free Tier is free forever, for testing and prototypes, with a single node cluster of 0.5 vCPU, 1 GB RAM and 4 GB disk. Qdrant Cloud Standard has usage-based pricing. Billing follows actual resource usage of compute (vCPU), memory (GB), cluster storage (GB), backup storage (GB) and used inference tokens of paid models, and usage is billed hourly.
Cloud Inference is billed per token at a fixed, region-independent price that depends on the model, shown in the Cloud Console. Free models carry no token charges. On paid clusters, paid models run on metered tokens after a monthly free allowance of up to 5 million tokens per model.
Through an AWS, GCP or Azure Marketplace subscription, Qdrant Cloud usage is added as US$0.01 Resource Usage Units, so a cluster costing US$85 in a month adds 8,500 units to the cloud provider bill. Pricing does not differ between payment methods.
Standard is usage-based, paid as you go or under a committed-spend contract. Premium uses Premium tier rates under a committed-spend contract with an annual minimum.
In self-hosted Qdrant, audit logging is available from v1.17.0, records API operations that require authentication or authorization as JSON, and is not enabled by default. By default, audit logs rotate daily and the seven most recent log files are kept.
For customers subject to GDPR, Qdrant offers a Data Processing Agreement that covers its data protection and privacy commitments.
Customers can delete any Customer data from the Managed Cloud Service at any time during the term. Qdrant deletes or destroys Customer Data 30 days after the Cloud Service Order expires, unless a statutory obligation requires it to retain some, which it destroys when that obligation expires. Under the Qdrant Cloud Service Agreement, Qdrant uses Customer data on Managed Cloud to make it available to Team Members and to provide the Cloud Service and support services. Qdrant accesses Customer data only to support the customer's use and address service or technical problems, to render other Qdrant Services, or as the customer permits in writing. The agreement lets Qdrant use Aggregated Data, which does not identify the customer, for developing and improving its products.
Qdrant Cloud backup schedules set Days of Retention, the number of days after which a backup snapshot is deleted.
A free tier cluster has 1 node with 1 GB RAM, 0.5 vCPU and 4 GB disk, which supports about 1 M vectors of 768 dimensions. It runs without high availability, with only manual snapshots and restores via API and version upgrades with downtime. Unused free clusters are suspended after 1 week and deleted after 4 weeks of inactivity if not reactivated.
A dense vector can have between 1 and 65,536 dimensions. For multivectors, the number of sub-vectors times the vector size must stay under 1,048,576, a limit that is not configurable.
Each Cloud Inference model has a context window, and longer input text is truncated to fit. For dense models, the collection's vector size must match the model output, or the upsert fails with an error.
The Qdrant Cloud SLA commits to quarterly uptime of 99.5% for Standard, 99.9% for Premium single-AZ clusters and 99.95% for Premium multi-AZ clusters. It covers Managed Cloud clusters only, and Hybrid Cloud and Private Cloud fall outside it.
From Qdrant v1.19.0, resource quotas stop a node from taking on more data once it is low on memory or disk space, and quotas are disabled by default.
Source index
Every source GILD read, with the original address and the context used to count it. Open a finding to filter the full cards above. On smaller screens the index lists each source once by name, cohort and date; its full record above carries the address, context and findings.
| Source | Original URL | Author and context | Who | Published | Review date | Applies | Findings |
|---|---|---|---|---|---|---|---|
| GitHub issue by dstocktonID qd-c029 | Developer Communities | github.com/ | dstockton (David Stockton), GitHub issue reporterself-described: A 3-node cluster after raising a collection's replication factor | Product or platform team | Published Oct 23, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by rsicartID qd-c028 | Developer Communities | github.com/ | R.Sicart (rsicart), JobTeaser (GitHub profile)self-described: A 3-node Qdrant Hybrid Cloud cluster on Kubernetes with Calico eBPF | Product or platform team | Published Oct 20, 2026 | Assessment reviewed Nov 1, 2026 | Still applicable | |
| GitHub issue by jkbeID qd-c024 | Developer Communities | github.com/ | Jakob Edding (jkbe), bakdata (GitHub profile)self-described: A memory-scarce Qdrant deployment with only the HNSW index in memory | Product or platform team | Published Oct 13, 2026 | Assessment reviewed Nov 1, 2026 | Still applicable | |
| GitHub pull request by doogle-ossID qd-c708 | Developer Communities | github.com/ | Debabrata Patnaik (doogle-oss), Application Development Manager, JP Morgan Chase Global Payments (GitHub profile)self-described: Approving FastEmbed for use inside a company | Organization-wide | Published Sep 19, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| Reddit comment by Hungry-Horror-7577ID qd-c037 | Developer Communities | www.reddit.com/ | Hungry-Horror-7577, Reddit commenterself-described: A fully offline RAG index over technical books on one machine | Individual builder | Published Sep 15, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| PeerSpot review by Pawel CisloID qd-c041 | Review Sites | www.peerspot.com/ | Pawel Cislo, PeerSpot reviewerself-described: MLOps engineer at a tech services company with 501 to 1,000 employees, per PeerSpot's reviewer line | Product or platform team | Published Aug 24, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by bvolkmerID qd-c706 | Developer Communities | github.com/ | Benedikt Volkmer (bvolkmer), Machine Learning Researcher and Developer (GitHub profile)self-described: Downloading snapshots through the Python client | Product or platform team | Published Aug 19, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by scheung-lbgID qd-c707 | Developer Communities | github.com/ | Sebastian Cheung (scheung-lbg), LBG (GitHub profile)self-described: Installing FastEmbed through a client's package firewall | Organization-wide | Published Aug 18, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| PeerSpot review by Uzair FaruqiID qd-c042 | Review Sites | www.peerspot.com/ | Uzair Faruqi, PeerSpot reviewerself-described: Head of data science at Mjunction Services, per PeerSpot's reviewer line | Technical leadership | Published Aug 17, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by ronenperezID qd-c023 | Developer Communities | github.com/ | ronenperez, GitHub issue reporterself-described: A production Qdrant cluster with on-disk vectors | Product or platform team | Published Jul 27, 2026 | Assessment reviewed Nov 1, 2026 | Still applicable | |
| GitHub issue by cainzhongID qd-c110 | Developer Communities | github.com/ | Cain (cainzhong), Micro Focus (GitHub profile)self-described: Scanning the latest qdrant/qdrant Docker image for vulnerabilities | Product or platform team | Published Jul 17, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| Hacker News comment by cbcoutinhoID qd-c020 | Developer Communities | news.ycombinator.com/ | Chris Coutinho (cbcoutinho), Data Engineer, Astrolabe Cloud (GitHub profile)self-described: Building a self-hosted Nextcloud knowledge-base and agent-memory app | Individual builder | Published Jul 16, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| Reddit comment by tenequmID qd-c032 | Developer Communities | www.reddit.com/ | tenequm, Reddit commenterself-described: An app that needed a database with no running server | Individual builder | Published Jul 15, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| Reddit comment by sinevilsonID qd-c033 | Developer Communities | www.reddit.com/ | sinevilson, Reddit commenterself-described: Running Qdrant in production with many collections | Product or platform team | Published Jul 15, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| Hacker News comment by vunderbaID qd-c019 | Developer Communities | news.ycombinator.com/ | Shaun Pedicini (vunderba), Hacker News commenter (HN profile)self-described: Local multimodal image search on their own hardware | Individual builder | Published Jun 24, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| Reddit comment by Master_Page_116ID qd-c034 | Developer Communities | www.reddit.com/ | Master_Page_116, Reddit commenterself-described: A RAG pipeline built on the Mastra agent framework | Individual builder | Published Jun 17, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| Reddit post by SilverConsistent9222ID qd-c038 | Developer Communities | www.reddit.com/ | SilverConsistent9222, Reddit posterself-described: An internal knowledge base tool built at work | Small product team | Published Jun 7, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| PeerSpot review by Amrit DashID qd-c043 | Review Sites | www.peerspot.com/ | Amrit Dash, PeerSpot reviewerself-described: Automation engineer at an educational organization with 11 to 50 employees, per PeerSpot's reviewer line | Small product team | Published May 26, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| Reddit comment by Zealousideal_Wolf624ID qd-c031 | Developer Communities | www.reddit.com/ | Zealousideal_Wolf624, Reddit commenterself-described: Running Qdrant at scale | Individual builder | Published May 21, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| PeerSpot review by Vedant WaniID qd-c044 | Review Sites | www.peerspot.com/ | Vedant Wani, PeerSpot reviewerself-described: Analyst at Synergy Connect, per PeerSpot's reviewer line | Small product team | Published May 18, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| Practitioner blog post by Manfred DreeseID qd-c039 | Practitioner Blogs | dreese.de/ | Manfred Dreese, tech lead and consultant (his own blog)self-described: Three years running Qdrant for a large-scale recommendation product on Kubernetes | Technical leadership | Published Apr 23, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by kaancfidanID qd-c111 | Developer Communities | github.com/ | Kaan C. Fidan (kaancfidan), Vispera (GitHub profile)self-described: A collection of about 30M vectors with nested array payloads | Product or platform team | Published Mar 16, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| Hacker News comment by arlogilbertID qd-c017 | Developer Communities | news.ycombinator.com/ | Arlo Gilbert (arlogilbert), serial founder (HN profile), Osano (GitHub)self-described: Comparing Zvec, LanceDB and Qdrant on a small text dataset | Technical leadership | Published Feb 23, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by bytestreamID qd-c207 | Developer Communities | github.com/ | Kieran (bytestream), SupportPal (GitHub profile)self-described: A staged upgrade from Qdrant 1.10.1 to 1.16.3 | Product or platform team | Published Feb 19, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| Hacker News comment by sa-codeID qd-c113 | Developer Communities | news.ycombinator.com/ | Samay Kapadia (sa-code), Principal ML Engineer, Zalando (GitHub profile linked from HN)self-described: Setting a default vector store for a team | Technical leadership | Published Jan 25, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by 02denoID qd-c701 | Developer Communities | github.com/ | Deniz Özdemirli (02deno), Computer Engineer at Nokia (GitHub profile)self-described: Hybrid RRF queries with min_should filters on Qdrant 1.16.3 | Product or platform team | Published Jan 23, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| Hacker News comment by p410n3ID qd-c022 | Developer Communities | news.ycombinator.com/ | p410n3, Hacker News commenterself-described: Semantic product search for an e-commerce catalog | Product or platform team | Published Jan 9, 2026 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by ffletcherrID qd-c026 | Developer Communities | github.com/ | ffletcherr (Homayoun), GitHub issue reporterself-described: A Kubernetes deployment with tens of millions of points | Product or platform team | Published Dec 21, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by boris-petrovID qd-c109 | Developer Communities | github.com/ | Boris Petrov (boris-petrov), profuz (GitHub profile)self-described: Upgrading self-hosted Qdrant with an automated test suite | Product or platform team | Published Nov 27, 2025 | Assessment reviewed Nov 1, 2026 | Still applicable | |
| GitHub issue by DragosGhineaID qd-c702 | Developer Communities | github.com/ | Dragos Dumitru Ghinea (DragosGhinea), AI Developer at It Just Works Inc. (GitHub profile)self-described: Filtered search over a 5,657,847-point collection after upgrading to 1.16 | Product or platform team | Published Nov 25, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by avvertixID qd-c703 | Developer Communities | github.com/ | Alessio (avvertix), Full-Stack Engineer and AI Engineer (GitHub profile)self-described: Qdrant in Docker with data on a Windows-mounted volume | Individual builder | Published Nov 25, 2025 | Assessment reviewed Nov 1, 2026 | Still applicable | |
| Hacker News comment by couAUIAID qd-c018 | Developer Communities | news.ycombinator.com/ | couAUIA, Hacker News commenterself-described: An engineering company building internal document search | Product or platform team | Published Nov 5, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by fraenkiID qd-c212 | Developer Communities | github.com/ | Frank Wall (fraenki), markt.de (GitHub profile)self-described: Operating a self-hosted Qdrant cluster | Product or platform team | Published Oct 27, 2025 | Assessment reviewed Nov 1, 2026 | Still applicable | |
| GitHub issue by ApidcloudID qd-c108 | Developer Communities | github.com/ | Luís Fernandes (Apidcloud), Artivive (GitHub profile)self-described: Returning large payloads from Qdrant through the Node client | Product or platform team | Published Oct 8, 2025 | Assessment reviewed Nov 1, 2026 | Still applicable | |
| Reddit comment by eujzmcID qd-c035 | Developer Communities | www.reddit.com/ | eujzmc, Reddit commenterself-described: A SaaS team with millions of vectors choosing between Milvus and Qdrant | Product or platform team | Published Sep 24, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| Hacker News comment by k9294: Comparing Qdrant and Zilliz Cloud against turbopuffer on operational simplicityID qd-c015 | Developer Communities | news.ycombinator.com/ | k9294, Hacker News commenterself-described: Comparing Qdrant and Zilliz Cloud against turbopuffer on operational simplicity | Small product team | Published Sep 9, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by ml-abdulaID qd-c027 | Developer Communities | github.com/ | ml-abdula (Abdullah), GitHub issue reporterself-described: A production deployment of about 4.6M points with vectors and HNSW on disk | Product or platform team | Published Sep 3, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| Reddit comment by junegirl_198ID qd-c036 | Developer Communities | www.reddit.com/ | junegirl_198, Reddit commenterself-described: A production deployment with lots of ingests and lower query volume | Small product team | Published Sep 3, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by eekID qd-c107 | Developer Communities | github.com/ | Radu-Sebastian Amarie (eek), AI startup founder (GitHub profile)self-described: Per-tenant semantic search over one large collection | Technical leadership | Published Aug 26, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by orhanraufID qd-c030 | Developer Communities | github.com/ | Rauf Akdemir (orhanrauf), co-founder and CTO, Airweave (GitHub profile)self-described: Self-hosting Qdrant v1.15.3 on Kubernetes (Azure AKS) with a 50,000-document collection | Technical leadership | Published Aug 23, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by morsikID qd-c202 | Developer Communities | github.com/ | Konrad Mosoń (morsik), DevOps, openhades (GitHub profile)self-described: A 6-node Qdrant cluster | Product or platform team | Published Aug 18, 2025 | Assessment reviewed Nov 1, 2026 | Still applicable | |
| GitHub issue by aalekhpatel07ID qd-c209 | Developer Communities | github.com/ | Aalekh Patel (aalekhpatel07), Canadian Centre for Child Protection (GitHub profile)self-described: Restoring collections with aliases from snapshots | Product or platform team | Published Jun 24, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by amircodotaID qd-c025 | Developer Communities | github.com/ | amircodota, GitHub issue reporterself-described: A large production collection on Qdrant 1.8.4 | Product or platform team | Published Jun 3, 2025 | Assessment reviewed Nov 1, 2026 | Still applicable | |
| GitHub issue by rajanpatilID qd-c205 | Developer Communities | github.com/ | Rajan Patil (rajanpatil), Elsevier (GitHub profile)self-described: A single Qdrant node on Azure Kubernetes Service | Product or platform team | Published Mar 28, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by bdeckelID qd-c705 | Developer Communities | github.com/ | Robert Deckel (bdeckel), PTC (GitHub profile)self-described: A single Qdrant pod on Kubernetes in Microsoft Azure with the default Helm values | Product or platform team | Published Mar 21, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| Hacker News comment by boboshaID qd-c008 | Developer Communities | news.ycombinator.com/ | bobosha, 3x entrepreneur and AI researcher (HN profile)self-described: A team that ran Qdrant in production beside Postgres | Small product team | Published Mar 18, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| Hacker News comment by k9294: A relatively small startup running managed QdrantID qd-c014 | Developer Communities | news.ycombinator.com/ | k9294, Hacker News commenterself-described: A relatively small startup running managed Qdrant | Small product team | Published Mar 17, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| GitHub issue by MohammadsakniniID qd-c704 | Developer Communities | github.com/ | Mohammad Saknini (Mohammadsaknini), Data & AI Engineer at Inform GmbH (GitHub profile)self-described: Bulk ingestion of 89 million 768-dimension points in Docker on WSL, for research | Product or platform team | Published Jan 8, 2025 | Assessment reviewed Nov 1, 2026 | Current | |
| READMEID qdd-01 | Product Documentation | raw.githubusercontent.com/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Embeddings: bring your own vectorsID qdd-02 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| InferenceID qdd-03 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| FastEmbedID qdd-04 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Cloud account setupID qdd-05 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| LangChain integrationID qdd-06 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Framework integrationsID qdd-07 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Haystack integrationID qdd-08 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| InstallationID qdd-09 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Managed CloudID qdd-10 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| SecurityID qdd-11 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| PricingID qdd-12 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Cloud InferenceID qdd-13 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Marketplace billingID qdd-14 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Premium tierID qdd-15 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Self-hosted audit loggingID qdd-16 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| GDPR data processing agreementID qdd-17 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Cloud Service AgreementID qdd-18 | Product Documentation | cloud.qdrant.io/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Cloud backups: retentionID qdd-19 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Create a clusterID qdd-20 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| VectorsID qdd-21 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Cloud Inference docsID qdd-22 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Qdrant Cloud SLAID qdd-23 | Product Documentation | cloud.qdrant.io/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
| Resource quotasID qdd-24 | Product Documentation | qdrant.tech/ | QdrantOfficial | Not counted (documentation) | No date stated | Documentation checked Nov 1, 2026 | Current | Verification only |
