fastembed vs sentence-transformers: a light ONNX runtime against the full PyTorch stack

One runs ONNX graphs, the other runs PyTorch models

Both libraries exist to turn text into vectors and then into similarity scores, and the mechanism underneath differs more than the import line suggests.

FastEmbed's README says the library runs models on the ONNX Runtime rather than a deep learning framework, that it does not require a GPU, and that it does not download gigabytes of PyTorch dependencies. The models it exposes are pre-packaged: TextEmbedding for dense text, SparseTextEmbedding for sparse vectors, LateInteractionTextEmbedding for ColBERT-style models, ImageEmbedding for pictures, and LateInteractionMultimodalEmbedding for models such as ColPali that embed text and images separately. The README also claims the ONNX Runtime is faster than PyTorch for this work, which is the project's own statement about its runtime.

Sentence-transformers sits on top of transformers and PyTorch and wraps four families of model in four classes: SentenceTransformer for embeddings, CrossEncoder for rerankers, SparseEncoder for sparse vectors, and MultiVectorEncoder for token-level vectors in late-interaction retrieval. Its README says the same four families can also be trained or fine-tuned. So the structural difference is that FastEmbed hands you a runtime with a fixed catalogue, while sentence-transformers hands you a framework that can also produce models.

The install: fastembed against sentence-transformers and its PyTorch floor

The two installation paths set the ceiling on where each library can run.

FastEmbed installs with `pip install fastembed`, and a separate `pip install fastembed-gpu` gives GPU support. Our analysis of fastembed adds a practical instruction that is worth following before you do anything else: read the version matrix in the packaging file rather than assuming a plain install resolves cleanly, because specific inference runtime versions are excluded per Python version. That is a detail that decides whether your install works on your interpreter.

Sentence-transformers installs with `pip install -U sentence-transformers` and asks for more around it. The README recommends Python 3.10 or newer, PyTorch 2.2 or newer, and transformers v5.0 or newer, and the documentation covers uv, conda, source and editable installs, CUDA setup, and a set of extras: `[image]`, `[audio]`, `[video]`, `[train]`, `[onnx]`, `[openvino]` and `[dev]`. The weight of PyTorch is the difference that matters, and it is the price of the training path.

First-run behaviour is similar in both. FastEmbed downloads and initialises the model when you first create a TextEmbedding, and sentence-transformers fetches the checkpoint when you construct SentenceTransformer with a model id such as `sentence-transformers/all-MiniLM-L6-v2`.

Model coverage across dense, sparse, late interaction and images

For retrieval work, the catalogue is where the two libraries differ most.

FastEmbed covers the main retrieval shapes with named entry points. Dense text comes from TextEmbedding, and the README says the default text model is Flag Embedding, with support for query and passage prefixes on the input text, which is the convention that keeps a query and a passage in the same space. Sparse vectors come from SparseTextEmbedding with a SPLADE++ model such as `prithivida/Splade_PP_en_v1`. Late interaction comes from LateInteractionTextEmbedding with `colbert-ir/colbertv2.0`, ImageEmbedding covers CLIP-style image vectors with `Qdrant/clip-ViT-B-32-vision`, and LateInteractionMultimodalEmbedding handles models such as `Qdrant/colpali-v1.3-fp16` where the document is a set of page images. Rerankers live under `fastembed.rerank`.

Sentence-transformers covers the same four families from the checkpoint side. The README points to more than 15,000 pretrained models on Hugging Face, including many from the MTEB leaderboard, and its four classes line up one for one with the families FastEmbed exposes as separate model types. It also handles the training and fine-tuning routes for all four.

So on coverage of published models, sentence-transformers is the wider list by a long way. On covering several retrieval shapes behind one small API, FastEmbed is the tidier surface.

Where fastembed stops: a fixed catalogue and a manual model registration

FastEmbed's boundary is the catalogue it ships with, and the escape hatch costs you a few declarations.

The models it does not ship can still be used. The README shows `TextEmbedding.add_custom_model` with a model name, a pooling type from `PoolingType`, a normalization flag, a `ModelSource` pointing at a Hugging Face repository, a dimension and a model file such as `onnx/model.onnx`, after which the custom model can be loaded by name. Our analysis of fastembed spells out the cost: adding a model is a supported path, but it does require naming the pooling and dimension yourself. Pooling in particular is not a detail you can leave at its default, because a wrong pooling choice produces vectors that look fine and rank badly.

Two other limits sit alongside that. There is no training path, which is by design rather than an omission, and the version matrix in the packaging file, where particular inference runtime versions are excluded for particular Python versions, means an install that works on a colleague's machine may not resolve on yours.

The default model is also worth pinning rather than trusting. The README names Flag Embedding as the default text embedding model, while the quickstart prints `BAAI/bge-small-en-v1.5` when the model is ready, so a project that does not name its model explicitly can change its vectors when the default does.

Where sentence-transformers stops: Python only, and a pinned dependency range

Sentence-transformers' limits are about reach and dependency control.

Our analysis of sentence-transformers says not to adopt it if you need a language runtime other than Python. That is a hard boundary: the library is a Python package that drives PyTorch models, so a service written in Go, Rust or Java has to call it across a process boundary or through a separate inference service.

The dependency range is the other one. The README recommends transformers v5.0 or newer, and our analysis states that the dependency is pinned to >=5.0.0,<6.0.0 and that a v6.0.0 model cache can change scoring behaviour. A cache that changes scores is worth understanding before it changes a ranking in production: the same checkpoint can score differently depending on what is in the cache, which makes a reranking result harder to reproduce across environments.

There is also the ordinary cost of a framework with this many options. The README recommends PyTorch 2.2 or newer and offers extras for image, audio and video work as well as training, so the install size and the surface area both grow with what you enable. And our analysis tells you to confirm that your torch build matches the CUDA or CPU target you actually deploy on, which is a check worth doing once and writing down.

Deployment: a serverless footprint against a training path

The two libraries were shaped by different constraints, and the shape shows in how they are deployed.

FastEmbed was built for constrained environments. The README lists three reasons and gives the mechanism for each: it is light, with few external dependencies and no gigabyte-scale PyTorch download; it is fast, using the ONNX Runtime and data parallelism for encoding large datasets; and it does not need a GPU, which is what makes it a candidate for serverless runtimes such as AWS Lambda. A service that has to scale to zero or start per request is the case the library was designed around, and the ONNX Runtime is the reason it fits there.

Sentence-transformers was built for the research and production model workflow. Its README describes training and fine-tuning for embedding models, reranker models, sparse encoder models and multi-vector encoder models, and it links to a training overview for each. That is the part FastEmbed cannot do at all.

Neither library is a serving layer. Neither one gives you an HTTP endpoint, a request queue or a batching policy, so both end up inside a service you write, and the difference in what that service needs is large. FastEmbed's service is small and starts quickly. Sentence-transformers' service is bigger, depends on your torch build, and can carry a training job in the same environment.

Licences, release rhythm and the choice for concrete situations

Both projects are under Apache-2.0, so licensing does not separate them. FastEmbed's last push to the repository was on 2026-10-15, with tagged releases v0.8.0 on 2026-03-23, v0.7.4 on 2025-12-05 and v0.7.2 on 2025-08-25. Sentence-transformers' last push was on 2026-10-21, with v6.1.0 on 2026-09-18, v6.0.1 on 2026-08-31 and v6.0.0 on 2026-08-18. Neither repository is archived, and the release rhythms differ in a way that matters for planning: sentence-transformers shipped three versions inside a month, while FastEmbed's latest tag is dated 2026-03-23.

The concrete situations separate like this.

Your embedding code runs in a Lambda function or a small container with a cold start budget. Fastembed, because it runs on ONNX with no GPU and no PyTorch download.

You need one library for dense text, sparse vectors, late interaction and image embeddings. Fastembed, which exposes each as its own class.

You want to fine-tune an embedding model on your own data next quarter. Sentence-transformers, which documents training for all four families.

You want a specific checkpoint from the Hugging Face Hub that is not in any curated list. Sentence-transformers, whose README points to more than 15,000 pretrained models.

Your service is not Python. Neither library in-process; Fastembed is still the smaller dependency if you wrap it in a service.

You need to reproduce a reranking score exactly in a second environment. Sentence-transformers with the dependency range and the model cache pinned and verified.

Before committing, check the fastembed version matrix in the packaging file against your Python version, and confirm the sentence-transformers checkpoint you intend to ship is listed on the Hugging Face models page filtered by library=sentence-transformers. The judgement to keep is that FastEmbed is the runtime you want when running the model is the whole job, while sentence-transformers is the framework you want when the model is something you may later change.

Bottom line

FastEmbed is the right choice when you are running models rather than building them, because the ONNX Runtime, the absence of a PyTorch download and the choice of dense, sparse, late-interaction and image classes under one small API let an embedding service start fast and run without a GPU; sentence-transformers is the right choice when you may later fine-tune what you run, because it covers the same four model families and documents training for each of them, at the cost of a Python-only runtime and a pinned transformers range. Verify first: for fastembed, the version matrix in the packaging file against your Python version and the pooling and dimension you declare when you register a custom model, for sentence-transformers, that the checkpoint you plan to ship is listed on the Hugging Face models page filtered by library=sentence-transformers and that your torch build matches the target you deploy on. The judgement worth keeping is that these are not interchangeable defaults, since one is a runtime with a catalogue and the other a framework with a training path, so the deciding question is whether the model itself will ever change.

Sources

1. qdrant/fastembed repository
2. qdrant/fastembed README
3. huggingface/sentence-transformers repository
4. huggingface/sentence-transformers README