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Guidance on how to upgrade your Qdrant version without interrupting the availability of your application and ensuring data integrity.
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Based on SOC occupation classification
| name | qdrant-version-upgrade |
| description | Guidance on how to upgrade your Qdrant version without interrupting the availability of your application and ensuring data integrity. |
| allowed-tools | ["Read","Grep","Glob"] |
Qdrant have the following guarantees about version compatibility:
Major and minor versions of Qdrant and SDK are expected to match. For example, Qdrant 1.17.x is compatible with SDK 1.17.x.
Qdrant is tested for backward compatibility between minor versions. For example, Qdrant 1.17.x should be compatible with SDK 1.16.x. Qdrant server 1.16.x is also expected to be compatible with SDK 1.17.x, but only for the subset of features that were available in 1.16.x.
For migration to next minor version, it is recommended to first upgrade the SDK to the next minor version, and then upgrade the Qdrant server.
Storage compatibility is only guaranteed for one minor version. For example, data stored with Qdrant 1.16.x is expected to be compatible with Qdrant 1.17.x. If you need to migrate more than 1 minor version, it is required do the upgrade step by step, one minor version at a time. E.g. to migrate from 1.15.x to 1.17.x, you need to first upgrade to 1.16.x, and then to 1.17.x. Note: Qdrant cloud automates this process, so you can directly upgrade from 1.15.x to 1.17.x without intermediate steps.
Qdrant cluster with replication factor of 2 and above can be upgraded without downtime, by performing a rolling upgrade. This means that you can upgrade one node at a time, while the other nodes continue to serve requests. This allows you to maintain availability of your application during the upgrade process. More about replication factor: https://qdrant.tech/documentation/guides/distributed_deployment/#replication-factor
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Guides developers through building community Qdrant skills that embed domain knowledge into Claude. Use when someone wants to "create a community qdrant skill", "build an external skill for qdrant", "make a qdrant skill for the community", "build a public qdrant skill", or asks how to "create a skill for qdrant memory issues for developers". Also trigger when someone says "I want to build an open source qdrant skill" or "help me create a qdrant skill for public use". Do NOT use for internal SA skills, general non-Qdrant skill creation, or Python project skills unrelated to Qdrant.
Diagnoses when and why to recommend specific cost optimization techniques for Qdrant customers. Use when a customer or SA says "too expensive", "need to reduce cost", "pricing too high", "how to lower price", "sticker shock", or asks about scaling costs. Also trigger when a deal is blocked by pricing, a customer compares competitor pricing, a startup says the quote is too high, or someone needs a formula to predict costs at scale. This skill answers "when should I recommend scalar vs binary quantization?" not "how to configure quantization." Do NOT use for general Qdrant setup, feature requests, or implementation how-tos.
Handles technical objections that block Qdrant deals and provides proven response playbooks. Use when a deal is stalled due to pricing concerns, missing features, performance issues, or competitive pressure. Triggers on "deal blocked", "customer concerned about cost", "proposal too expensive", "missing feature blocking production", "latency issues on call", "why not use pinecone", "customer built workaround", or "champion lost budget battle". Do NOT use for general product questions, documentation lookups, or technical implementation.
Guides Qdrant SAs through building internal skills that embed domain knowledge into Claude. Use when someone wants to "create a qdrant skill", "build a skill for qdrant diagnostics", "make an internal skill for qdrant customers", "create a qdrant troubleshooting skill", or asks how to "turn SA knowledge into a skill". Also trigger when someone says "help me build a skill for qdrant deal objections" or "I want to create a skill for qdrant cluster sizing". Do NOT use for general skill creation unrelated to Qdrant, external community skills, or Python/JS project skills.
Diagnoses and fixes Qdrant memory problems. Use when a developer reports OOM crashes, high RAM usage, HNSW consuming too much memory, latency spiking after switching storage, or needing to fit more vectors on existing hardware. Also trigger when someone asks which quantization to use, how to choose between mmap and RAM, how to estimate memory for scaling, or why their Qdrant node keeps dying. Do NOT use for initial Qdrant setup, API scripting, embedding model selection, search accuracy tuning, backups, or vector DB comparisons.
