---
title: "SIE vs OpenAI: Use OpenAI for frontier models, SIE for private open-model inference"
description: OpenAI provides managed frontier models with no infrastructure to run. SIE serves open models inside your cloud when control, data boundaries and sustained inference volume matter.
canonical_url: https://superlinked.com/blog/sie-vs-openai
last_updated: 2026-08-26
---

Short version: choose OpenAI when you need its frontier models, broad multimodal APIs or the fastest route to a managed production endpoint. Choose SIE when open models can do the task and inference must run inside your cloud, share one GPU pool or avoid a per-token provider bill.

Many teams should use both. Run high-volume embeddings, reranking, extraction and small-model generation on SIE; reserve OpenAI for requests where a frontier model earns its higher variable cost.

[OpenAI](https://developers.openai.com/api/docs/models/all) is a hosted model provider. [SIE](https://github.com/superlinked/sie) is an open-source inference engine that you deploy locally, in Kubernetes or through SIE Cloud. The products overlap on embeddings and text generation. OpenAI also provides image, audio, realtime and other managed APIs that SIE does not replace.

## The decision is model access versus deployment control

OpenAI gives an application immediate access to models operated by OpenAI. There are no GPU drivers, model servers or Kubernetes nodes to manage. Usage is metered by tokens or another model-specific unit.

SIE gives an application one endpoint for more than 100 configured open models. It covers encoding, scoring, extraction, OCR and generation. Models load on demand, share worker capacity and can be changed without replacing the serving layer.

That control creates work. Your team owns capacity, upgrades and incidents for a self-hosted SIE deployment. OpenAI owns those concerns for its API.

## OpenAI manages the models; SIE runs models you choose

| Decision | SIE | OpenAI API |
|---|---|---|
| Model source | Open-weight models selected by your team | OpenAI models selected and operated by OpenAI |
| Main tasks | Embeddings, sparse retrieval, reranking, extraction, OCR and open-model generation | Frontier text generation, reasoning, embeddings, vision, audio, image generation and realtime APIs |
| Deployment | Laptop, your cloud, Kubernetes, on-premises or air-gapped | OpenAI-managed infrastructure |
| API surface | Native SIE SDKs plus OpenAI-compatible endpoints | Native OpenAI SDKs and APIs |
| Billing | Infrastructure and operational cost | Usage-based API pricing |
| Scaling | Capacity and autoscaling configured by your team | Managed by OpenAI within account rate limits |
| Data boundary | Requests stay within the infrastructure running SIE | Requests are processed by OpenAI under its platform data controls |
| Operations | Your team or SIE Cloud owns the serving stack | OpenAI owns the serving stack |
| Best fit | Private, sustained or multi-model inference | Frontier capability and managed delivery |

## OpenAI wins when the model is the differentiator

OpenAI is the better choice when the application depends on an OpenAI model's reasoning, tool use, multimodal input or generation quality. Swapping in a smaller open model to save money is a false economy if task success drops.

The API also suits uncertain demand. A team can send ten requests this week and ten million next week without buying a GPU first. OpenAI absorbs provisioning, model loading and fleet management.

And new OpenAI models arrive behind the same account and SDK. Teams that want rapid access to each new release will move faster on the managed platform than by validating and operating an open substitute.

## SIE wins when inference needs to stay yours

SIE runs in infrastructure you control. Prompts, documents, embeddings and extracted entities do not need to cross an external inference boundary. That matters for air-gapped deployments and internal policies that require application data to remain inside a specific account or network.

The engine also covers specialist retrieval work that does not map neatly to an OpenAI endpoint. One SIE cluster can serve dense encoders, sparse models, ColBERT-style late-interaction models, rerankers, OCR models and entity extractors. OpenAI's embedding API returns dense vectors; it does not provide the same retrieval model menu.

SIE makes model choice an infrastructure decision your team can revisit. You can pin weights, test a new checkpoint against saved evaluations and keep the API around it stable.

## OpenAI embeddings are cheaper until a self-hosted GPU stays busy

[OpenAI lists](https://developers.openai.com/api/docs/models/text-embedding-3-small) `text-embedding-3-small` at **$0.02 per million tokens** and `text-embedding-3-large` at **$0.13 per million tokens** on 26 August 2026.

For `text-embedding-3-small`, the token bill is straightforward:

| Embedded volume | OpenAI list-price cost |
|---:|---:|
| 10 million tokens | $0.20 |
| 100 million tokens | $2.00 |
| 1 billion tokens | $20.00 |
| 10 billion tokens | $200.00 |

At low volume, this is difficult to beat with an always-on GPU. The [OpenAI to SIE migration guide](/docs/migrate/openai) uses a roughly $1-per-hour A10G as a worked example. That GPU costs about $24 per day before cluster overhead, equal to 1.2 billion daily `text-embedding-3-small` tokens at list price.

Utilization changes the result. The [SIE hosted-API benchmark](/blog/sie-vs-hosted-embedding-apis) measures sustained inference near each GPU's throughput knee and reports much lower unit costs for open embedding models. That is a high-utilization floor. A GPU serving occasional queries will cost more per million tokens because most paid time is idle.

Use this calculation with your own traffic:

```text
OpenAI monthly cost = (tokens / 1,000,000) × model price

SIE monthly cost = GPU hours + CPU + storage + cluster overhead + operating time
```

Add model count. A shared SIE pool can run embeddings, reranking and extraction on the same infrastructure. Comparing that combined workload with one OpenAI embedding line item understates the value of the GPU and overstates the simplicity of the hosted bill.

## Quality has to be measured per task

OpenAI offers a small embedding model and a larger one. The choice is short, the API is consistent and OpenAI owns model operation.

SIE offers many open embedding families with different languages, dimensions, context lengths and retrieval modes. That choice can produce a better task fit. It also means the team has to evaluate candidates.

There is no universal quality winner. Test the models on queries and documents from the application. For retrieval, record NDCG, recall and latency together; a leaderboard average cannot tell you whether a model understands your product names, legal vocabulary or language mix.

The [OpenAI embedding alternatives guide](/blog/best-openai-embeddings-alternatives-you-can-run-in-your-own-cloud) provides a shortlist. The [SIE model catalogue](/models) contains first-party quality, latency, throughput and cost data for supported models.

## OpenAI has strong data controls, but it remains an external processor

[OpenAI's platform documentation](https://platform.openai.com/docs/models/default-usage-policies-by-endpoint) says API data is not used to train its models unless the customer opts in. Abuse-monitoring logs can contain customer content and are retained for up to 30 days by default. Eligible customers can apply for Modified Abuse Monitoring or Zero Data Retention; the embeddings endpoint is eligible for Zero Data Retention.

OpenAI also offers regional storage and, in some regions, regional processing. Eligibility and feature restrictions vary.

SIE keeps request processing inside the environment where you deploy it. Your security team controls network paths, encryption, logs and retention. The responsibility moves with the data: a self-hosted deployment has no provider operating the controls for you.

## API compatibility shortens migration, model changes still require testing

SIE exposes OpenAI-compatible `/v1/embeddings`, `/v1/chat/completions`, `/v1/completions` and `/v1/responses` endpoints. Existing OpenAI clients can point at SIE by changing the base URL and model identifier:

```python
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="not-needed",
)

response = client.embeddings.create(
    model="intfloat/e5-base-v2",
    input="One client surface, a different model and deployment.",
)
```

The compatible response shape does not make the models interchangeable. A new embedding model creates a different vector space and usually requires re-embedding the index. Generation models differ in tool calling, structured output, tokenization and prompt behaviour. Run application evaluations before moving traffic.

Sparse vectors, multi-vector retrieval, reranking and extraction use the native SIE SDK because OpenAI has no equivalent wire format for those operations. The [OpenAI to SIE migration guide](/docs/migrate/openai) shows both paths.

## Pick OpenAI when managed frontier capability matters most

OpenAI is the better choice when:

- The task needs an OpenAI frontier model and open alternatives fail the evaluation.
- The workload is small, new or unpredictable enough that a GPU would sit idle.
- The application needs OpenAI-specific image, audio or realtime capabilities.
- The team wants a managed API and accepts its data-processing boundary.

## Pick SIE when open models meet the quality bar

SIE is the better choice when:

- Prompts and documents must remain inside your cloud account or network.
- Sustained inference keeps provisioned GPUs busy enough to justify ownership.
- Several specialist models need one serving, monitoring and autoscaling layer.
- The team wants to pin model weights and avoid dependence on one hosted model vendor.

## A hybrid stack often gives the best result

Route by task instead of forcing one provider to handle everything.

An agent can use SIE for embeddings, reranking, OCR and structured extraction, then call an OpenAI frontier model for the small share of requests that need deeper reasoning. This keeps high-volume support work on predictable infrastructure while preserving access to OpenAI where quality changes the outcome.

Measure the routing policy. A cheap local call that creates a bad result is wasted compute; an expensive frontier call for a deterministic extraction task is wasted budget.

## FAQ

### Is SIE a drop-in replacement for OpenAI?

It is wire-compatible for several common endpoints, including embeddings and chat completions. It serves different models and does not reproduce every OpenAI API. Treat migration as a model change even when the client code barely moves.

### Is SIE always cheaper than OpenAI?

No. OpenAI embeddings are inexpensive at low volume. SIE becomes cost-competitive when GPUs stay busy, multiple workloads share them or external API pricing covers more than embeddings.

### Are OpenAI API requests used for training?

OpenAI says API data is not used for training unless the customer opts in. Default abuse-monitoring retention and feature-specific application state still matter; review the current platform data-controls page for the endpoints you use.

### Do I have to choose one platform?

No. A task router can send routine, high-volume inference to SIE and reserve OpenAI for model-dependent requests. This is often the most practical production design.

## Sources

- [OpenAI model catalogue](https://developers.openai.com/api/docs/models/all)
- [OpenAI text-embedding-3-small pricing](https://developers.openai.com/api/docs/models/text-embedding-3-small)
- [OpenAI platform data controls](https://platform.openai.com/docs/models/default-usage-policies-by-endpoint)
- [SIE repository and production architecture](https://github.com/superlinked/sie)
- [OpenAI to SIE migration guide](/docs/migrate/openai)
- [SIE versus hosted embedding APIs](/blog/sie-vs-hosted-embedding-apis)
- [OpenAI embedding alternatives](/blog/best-openai-embeddings-alternatives-you-can-run-in-your-own-cloud)