---
title: OpenAI → SIE
description: Migrate from OpenAI Embeddings to a self-hosted SIE cluster. Drop-in via the OpenAI-compatible endpoint, or use the native SIE SDK.
canonical_url: https://superlinked.com/docs/migrate/openai
last_updated: 2026-05-18
---
OpenAI Embeddings is a paid managed API. SIE is a self-hosted inference
engine. There are two migration paths:
1. **Drop-in shim.** Point the OpenAI SDK at SIE's `/v1/embeddings`
endpoint. Two-line change, every other call site untouched.
2. **Native SDK.** Use `sie_sdk.SIEClient` directly to access sparse,
multivector, ColBERT, rerankers, and extraction.
## Why migrate
- **Cost crosses over at moderate volume.** OpenAI bills per token;
SIE has flat hourly cost. The right answer depends on your workload;
see the [worked example](#worked-cost-example) below. Plug in your
own numbers before quoting either side.
- **Data residency.** Embeddings of your text never leave your network.
- **No rate limits.** Your ceiling is whatever GPU capacity you
provision. You also own the SLA: OpenAI's published 99.9% becomes
whatever your platform team operates.
- **Model breadth.** OpenAI ships 3 embedding models. SIE serves 100+
out of the box (109 bundle configs as of writing) across dense,
sparse, ColBERT/multivector, vision, and rerankers.
## TL;DR
#### Python
```diff
- client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
+ client = OpenAI(api_key="not-needed", base_url="http://sie:8080/v1")
```
#### TypeScript
```diff
- const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
+ const client = new OpenAI({ apiKey: "not-needed", baseURL: "http://sie:8080/v1" });
```
…and `model: "text-embedding-3-small"` becomes `model: "intfloat/e5-base-v2"` (or
whichever SIE model you pick).
## Before
#### Python
```python
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
resp = client.embeddings.create(
model="text-embedding-3-small",
input=["The mitochondrion is the powerhouse of the cell."],
)
vector = resp.data[0].embedding # 1536-dim
```
#### TypeScript
```typescript
import OpenAI from "openai";
const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const resp = await client.embeddings.create({
model: "text-embedding-3-small",
input: ["The mitochondrion is the powerhouse of the cell."],
});
const vector = resp.data[0].embedding; // 1536-dim
```
## After
#### Drop-in (OpenAI SDK + SIE)
```python
from openai import OpenAI
client = OpenAI(api_key="not-needed", base_url="http://localhost:8080/v1")
resp = client.embeddings.create(
model="intfloat/e5-base-v2",
input=["The mitochondrion is the powerhouse of the cell."],
)
vector = resp.data[0].embedding # 768-dim
```
#### TypeScript
```typescript
import OpenAI from "openai";
const client = new OpenAI({ apiKey: "not-needed", baseURL: "http://localhost:8080/v1" });
const resp = await client.embeddings.create({
model: "intfloat/e5-base-v2",
input: ["The mitochondrion is the powerhouse of the cell."],
});
const vector = resp.data[0].embedding; // 768-dim
```
#### Native SIE SDK
```python
from sie_sdk import SIEClient
from sie_sdk.types import Item
client = SIEClient("http://localhost:8080")
result = client.encode(
"intfloat/e5-base-v2",
Item(text="The mitochondrion is the powerhouse of the cell."),
)
vector = result["dense"].tolist()
```
#### TypeScript
```typescript
import { SIEClient } from "@superlinked/sie-sdk";
const client = new SIEClient("http://localhost:8080");
const result = await client.encode(
"intfloat/e5-base-v2",
{ text: "The mitochondrion is the powerhouse of the cell." },
);
const vector = Array.from(result.dense ?? []);
await client.close();
```
## Mapping
| OpenAI | SIE equivalent |
|---------------------------------------|------------------------------------------------------------------|
| `text-embedding-3-small` (1536) | `intfloat/e5-base-v2` (768) |
| `text-embedding-3-large` (3072) | `Alibaba-NLP/gte-Qwen2-1.5B-instruct` or `NovaSearch/stella_en_1.5B_v5` |
| `dimensions=N` truncation | Slice client-side, or pick a smaller-dim model |
| `encoding_format="base64"` | Supported on `/v1/embeddings` with the same field |
| `user="..."` for abuse tracking | Use SIE telemetry / your own tracing |
## Worked cost example
Public list prices, no negotiated discounts, single region:
| Workload | OpenAI (`text-embedding-3-small` @ $0.02 / 1M tokens) | SIE on one `g5.xlarge` (1× A10G, ~$1.00/hr on-demand) |
|-------------------|-------------------------------------------------------|--------------------------------------------------------|
| 10M tokens / day | ~$0.20 / day · ~$6 / month | ~$24 / day · ~$730 / month |
| 100M tokens / day | ~$2 / day · ~$60 / month | ~$24 / day · ~$730 / month |
| 1B tokens / day | ~$20 / day · ~$600 / month | ~$24 / day · ~$730 / month |
| 5B tokens / day | ~$100 / day · ~$3,000 / month | needs ≥1 GPU; still ~$730–$2,200 / month |
Crossover sits around 1.2B tokens / day on this size class. Below that, OpenAI is cheaper *and* you don't run a
GPU. Above that, SIE wins on cost and the gap widens linearly. **None
of this counts the engineering cost of operating a GPU pool**, which
is the actually-load-bearing variable for most teams. Plug in your
own utilization, GPU size, and reserved-instance discount before
quoting a number to your manager.
## Re-embed required?
**Yes.** Different model → different vector space. Even
`text-embedding-3-small` truncated to 1024 dims is not interchangeable
with any open-source model. Plan a re-embed window before cutting over.
## Run it yourself
```bash
# Start SIE with E5 loaded.
mise run serve -- -m intfloat/e5-base-v2
# Run the OpenAI 'before' script and the SIE 'after' script
# from this page. Compare the printed embeddings.
export OPENAI_API_KEY=sk-...
uv add openai
```
Cosine across spaces carries no signal, so don't expect 1.0. For
sign-off, run your retrieval eval against both legs.