Qdrant Model Migration

What to Do When Changing Embedding Models

Vectors from different models are incompatible. You cannot mix old and new embeddings in the same vector space. On v1.18+, you can add or delete named vector fields on an existing collection — migration no longer always requires a new collection. On v1.17 or earlier, all named vectors must be defined at collection creation time.

• Understand collection aliases before choosing a strategy Collection aliases

Can I Avoid Re-embedding?

Use when: looking for shortcuts before committing to full migration.

You MUST re-embed if: changing model provider (OpenAI to Cohere), changing architecture (CLIP to BGE), incompatible dimension counts across different models, or adding sparse vectors to dense-only collection.

You CAN avoid re-embedding if: using Matryoshka models (use dimensions parameter to output lower-dimensional embeddings, learn linear transformation from sample data, some recall loss, good for 100M+ datasets). Or changing quantization (binary to scalar): Qdrant re-quantizes automatically. Quantization

Need Zero Downtime

Use when: production must stay available. Recommended for model replacement at scale.

• If the cluster is v1.18 or later AND the collection has named vectors:

  • Add the new vector field directly to the existing collection Update vector schema
  • Re-embed all data in the background using UpdateVectors Update vectors
  • Verify search quality, then delete old vector field

• If the cluster is v1.17 or earlier OR the collection doesn't have named vectors:

• Create a new collection with the new model's dimensions and distance metric

• Re-embed all data into the new collection in the background

• Point your application at a collection alias instead of a direct collection name

• Atomically swap the alias to the new collection Switch collection

• Verify search quality, then delete the old collection

Careful, the alias swap only redirects queries. Payloads must be re-uploaded separately.

Need Both Models Live (Side-by-Side)

Use when: A/B testing models, multi-modal (dense + sparse), or evaluating a new model before committing.

• If the cluster is v1.18 or later:

  • Add the new vector field directly to the existing collection Update vector schema
  • Backfill new model embeddings incrementally using UpdateVectors Update vectors

• If the cluster is v1.17 or earlier: You cannot add a named vector to an existing collection. Create a new collection with both vector fields defined upfront:

  • Create new collection with old and new named vectors both defined Collection with multiple vectors
  • Migrate data from old collection, preserving existing vectors in the old named field
  • Backfill new model embeddings incrementally using UpdateVectors Update vectors
  • Compare quality by querying with using: "old_model" vs using: "new_model"
  • Swap alias to new collection once satisfied

Co-locating large multi-vectors (especially ColBERT) with dense vectors degrades ALL queries, even those only using dense. At millions of points, users report 13s latency dropping to 2s after removing ColBERT. Put large vectors on disk during side-by-side migration.

If you anticipate future model migrations, define both vector fields upfront at collection creation.

Dense to Hybrid Search Migration

Use when: adding sparse/BM25 vectors to an existing dense-only collection. Most common migration pattern.

You cannot add sparse vectors to an existing collection that uses a default (unnamed) dense vector. Must recreate:

• Create new collection with both dense and sparse vector configs defined
• Re-embed all data with both dense and sparse models
• Migrate payloads, swap alias

If the collection already uses named dense vectors and is on v1.18+, add the sparse vector field directly without recreating Update vector schema.

Sparse vectors at chunk level have different TF-IDF characteristics than document level. Test retrieval quality after migration, especially for non-English text without stop-word removal.

Re-embedding Is Too Slow

Use when: dataset is large and re-embedding is the bottleneck.

• Use update_mode: insert (v1.17+) for safe idempotent migration Update mode
• Scroll the old collection with with_vectors=False, re-embed in batches, upsert into new collection
• Upload in parallel batches (64-256 points per request, 2-4 parallel streams) Bulk upload
• Disable HNSW during bulk load (set indexing_threshold_kb very high, restore after)
• For Qdrant Cloud inference, switching models is a config change, not a pipeline change Inference docs

For 400GB+ datasets, expect days. For small datasets (<25MB), re-indexing from source is faster than using the migration tool.

What NOT to Do

• Assume you can add named vectors to an existing collection on v1.17 or earlier servers; check your server version first
• Delete the old collection before verifying the new one
• Forget to update the query embedding model in your application code
• Skip payload migration when using alias swap (aliases redirect queries, they do not copy data)
• Keep ColBERT vectors co-located with dense vectors during a long migration (I/O cost degrades all queries)
• Migrate to hybrid search without testing BM25 quality at chunk level