Migrate your vector search database without losing any data

Moves all your stored AI embeddings from one vector database to another automatically, in safe batches.

How the work actually flows

It repeats. Repeats process next batch; runs once per each vector record in batch.

Pattern: Structured Loop (21) ยท Multiple Instances without Synchronization (12)

flowchart TD trig(["scheduled migration run"]):::trigtime s0[("check last saved checkpoint")]:::store s1["pull next batch of vectors"]:::task s2[["reformat records for new database"]]:::mi s3["load batch into new database"]:::svc s4[("save checkpoint for next run")]:::store trig --> s0 s0 --> s1 s1 -->|"one per each vector record in batch"| s2 s2 --> s3 s3 --> s4 lp{"all vectors migrated"}:::gate s4 --> lp lp -. "process next batch" .-> s1 lp -->|"finished"| out out[/"fully migrated vector database"/]:::out pay{{"no data loss or recompute cost"}}:::pay out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
Starts itA stepAn outside serviceRuns once per itemA record or sheetRepeat or finishResultPayoff
Build size
Advanced

A larger build with multiple systems, AI reasoning, and custom rules.

Business functions
Knowledge Base & RAGSpreadsheet & Database Ops
Connects
PineconeWeaviateAirtable

The problem it solves

Switching vector databases usually means either paying to recompute every embedding from scratch or spending days manually exporting and re-importing records. Either way, you risk losing data or breaking the search features your product depends on.

Who it fits

A development or data team migrating an AI search or retrieval system to a new vector database provider.

How it works

  1. On a schedule, the system checks where the last migration left off
  2. It pulls the next batch of stored vectors and their metadata
  3. It reformats each record for the new database
  4. It loads the batch into the new database
  5. It saves its place so the next run picks up where it left off
What you get

Vectors moved without losing a single record

You get your entire vector database moved to its new home safely, in batches, without losing any of your stored data.

What you get

A fully migrated vector database with all embeddings and metadata intact.

What you need

Accounts with both vector database providers and an Airtable base to track progress.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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