# Comparing fast_plaid and stanford_plaid Implementations in PyLate: Architecture, Performance, and Use Cases

> Explore fast plaid vs stanford plaid in PyLate compare their architectures performance and use cases for efficient retrieval and indexing Discover the right PLAID backend for your needs

- Repository: [LightOn/pylate](https://github.com/lightonai/pylate)
- Tags: comparison
- Published: 2026-03-06

---

**PyLate offers two distinct PLAID index backends—FastPlaid for GPU-accelerated C++/CUDA retrieval and StanfordPlaid for research-friendly ColBERT-style indexing—each with unique embedding conversion logic, update mechanisms, and configuration parameters.**

PyLate is an open-source library that implements multi-vector retrieval for late interaction models like ColBERT. When building a PLAID index for scalable semantic search, developers must choose between two backends: `fast_plaid` and `stanford_plaid`. This comparison examines their architectural differences, API implementations, and performance characteristics based on the actual source code in the `lightonai/pylate` repository.

## Backend Architecture and Design Philosophy

### FastPlaid: High-Performance C++/CUDA Engine

**FastPlaid** wraps the `fast-plaid` library—a high-performance C++/CUDA implementation that quantizes vectors using product quantization (PQ-IVF) and optionally leverages Triton kernels for accelerated k-means clustering. This backend is optimized for production deployments requiring low-latency batch searches across massive corpora.

In [`pylate/indexes/fast_plaid.py`](https://github.com/lightonai/pylate/blob/main/pylate/indexes/fast_plaid.py), the `FastPlaid` class initializes the native index with explicit GPU-oriented parameters like `n_ivf_probe` and `n_full_scores` (lines 98-107 and 122-129).

### StanfordPlaid: Research-Friendly ColBERT Implementation

**StanfordPlaid** (exposed as `indexes.PLAID`) wraps Stanford's original ColBERT-style PLAID implementation (`stanford-nlp`). It builds on the original PLAID C++ code but exposes functionality through Python wrappers including `Indexer` and `Searcher` objects. This backend prioritizes flexibility for research prototypes and compatibility with the original ColBERT ecosystem.

The implementation in [`pylate/indexes/stanford_plaid.py`](https://github.com/lightonai/pylate/blob/main/pylate/indexes/stanford_plaid.py) uses the `Indexer` class for initial builds and `IndexUpdater` for incremental modifications (lines 99-112).

## Data Handling and Embedding Conversion

The two backends expect different embedding formats and use distinct conversion utilities.

### FastPlaid: Torch-Centric Conversion

FastPlaid accepts NumPy arrays, PyTorch tensors, or plain Python lists, converting them to a list of `torch.Tensor` objects via `convert_embeddings_to_torch`. This matches the `fast-plaid` API expectations for GPU batch processing.

```python
from pylate.indexes.fast_plaid import convert_embeddings_to_torch
import numpy as np

# FastPlaid handles 2D or 3D embeddings internally

embeddings = np.random.randn(10, 128).astype(np.float32)
torch_embeddings = convert_embeddings_to_torch(embeddings)

# Returns list of torch.Tensor objects

```

See [`pylate/indexes/fast_plaid.py`](https://github.com/lightonai/pylate/blob/main/pylate/indexes/fast_plaid.py) lines 19-44 for the implementation details.

### StanfordPlaid: NumPy Reshaping

StanfordPlaid expects embeddings shaped **(batch, tokens, dim)**—the standard ColBERT multi-vector format. The helper `reshape_embeddings` expands 2-D arrays to 3-D and converts PyTorch tensors to NumPy for the underlying C++ engine.

```python
from pylate.indexes.stanford_plaid import reshape_embeddings
import torch

# StanfordPlaid requires 3D format (batch, tokens, dim)

embeddings_2d = torch.randn(10, 128)
embeddings_3d = reshape_embeddings(embeddings_2d)

# Returns numpy array with shape (10, 1, 128) if input was 2D

```

See [`pylate/indexes/stanford_plaid.py`](https://github.com/lightonai/pylate/blob/main/pylate/indexes/stanford_plaid.py) lines 18-33 for the implementation.

## Index Management and CRUD Operations

### Creating and Updating Indexes

**FastPlaid** creates indexes via `self.fast_plaid.create()` with explicit parameters controlling k-means iterations, centroid limits, and quantization bits. The index persists under `fast_plaid_index`.

```python

# FastPlaid index creation parameters

fast_index = indexes.FastPlaid(
    index_folder="indexes",
    index_name="fast_index",
    nbits=4,                    # Quantization bits

    kmeans_niters=10,           # K-means iterations

    max_points_per_centroid=128,
    n_ivf_probe=8,              # IVF probe count for search

    use_triton=True,            # Enable Triton kernels

)

```

See [`pylate/indexes/fast_plaid.py`](https://github.com/lightonai/pylate/blob/main/pylate/indexes/fast_plaid.py) lines 98-107 and 122-129.

**StanfordPlaid** uses the `Indexer` object for initial builds and `IndexUpdater` for incremental additions. It supports updating existing indexes without full rebuilds.

```python

# StanfordPlaid uses Indexer/IndexUpdater pattern

stanford_index = indexes.PLAID(
    index_folder="indexes",
    index_name="stanford_index",
    embedding_size=128,
    nbits=2,
)

# First call creates index, subsequent calls use IndexUpdater

stanford_index.add_documents(
    documents_ids=["doc1", "doc2"],
    documents_embeddings=doc_embeddings,
)

```

See [`pylate/indexes/stanford_plaid.py`](https://github.com/lightonai/pylate/blob/main/pylate/indexes/stanford_plaid.py) lines 99-112.

### Document Removal Strategies

**FastPlaid** does not support true vector removal. The `remove_documents` method deletes ID mappings, calls `self.fast_plaid.delete(plaid_ids_to_remove)`, and re-indexes the remaining IDs locally.

```python

# FastPlaid removal re-indexes remaining documents

fast_index.remove_documents(["doc1"])  # Marks for deletion and re-indexes

```

See [`pylate/indexes/fast_plaid.py`](https://github.com/lightonai/pylate/blob/main/pylate/indexes/fast_plaid.py) lines 30-44 and 56-72.

**StanfordPlaid** uses `IndexUpdater.remove(plaid_ids)` for true removal and persists changes immediately.

```python

# StanfordPlaid supports direct removal via IndexUpdater

stanford_index.remove_documents(["doc1"])  # Calls IndexUpdater.remove()

```

See [`pylate/indexes/stanford_plaid.py`](https://github.com/lightonai/pylate/blob/main/pylate/indexes/stanford_plaid.py) lines 26-44.

## Query Execution and Retrieval

Both implementations expose a `__call__` method for search, but handle query processing differently.

**FastPlaid** converts query embeddings, optionally translates document-ID subsets to PLAID internal IDs, then runs `self.fast_plaid.search()`. Results map back to user IDs via pickle mappings.

```python

# FastPlaid search with optional document subset filtering

results = fast_index(
    query_embeddings, 
    k=10,
    documents_ids=["doc1", "doc2"]  # Optional subset filtering

)

```

See [`pylate/indexes/fast_plaid.py`](https://github.com/lightonai/pylate/blob/main/pylate/indexes/fast_plaid.py) lines 76-84 and 115-130.

**StanfordPlaid** reshapes query embeddings and calls `self.searcher.search()` for each query, building `RerankResult` objects from the ID mapping.

```python

# StanfordPlaid returns RerankResult objects

results = stanford_index(query_embeddings, k=10)
for hit in results[0]:
    print(f"{hit.id}: {hit.score}")

```

See [`pylate/indexes/stanford_plaid.py`](https://github.com/lightonai/pylate/blob/main/pylate/indexes/stanford_plaid.py) lines 52-66 and 70-82.

## Configuration Parameters and Performance Tuning

Both backends expose different tuning knobs reflecting their underlying architectures.

| Parameter | FastPlaid | StanfordPlaid |
|-----------|-----------|---------------|
| **Quantization** | `nbits` (PQ bits) | `nbits` (PQ bits) |
| **Clustering** | `kmeans_niters`, `max_points_per_centroid`, `n_samples_kmeans` | `kmeans_niters`, `ndocs` |
| **Search** | `n_ivf_probe`, `n_full_scores`, `batch_size` | `ncells`, `centroid_score_threshold`, `search_batch_size` |
| **Hardware** | `use_triton` (Triton kernels) | `use_triton`, `nranks` (GPU ranks) |
| **Dimensions** | Inferred from data | `embedding_size` (required) |

**FastPlaid** optimizes for GPU-accelerated batch searches with large IVF-PQ tables. Configure `n_ivf_probe` (number of IVF clusters to probe) and `n_full_scores` (number of candidates for full scoring) to balance recall versus latency.

**StanfordPlaid** provides research-oriented parameters like `centroid_score_threshold` for filtering and `nranks` for multi-GPU indexing. It generally exhibits higher overhead due to Python wrapper layers but offers greater flexibility for experimental configurations.

## Summary

- **FastPlaid** leverages the `fast-plaid` C++/CUDA library with Triton kernel support, converting embeddings to `torch.Tensor` lists via `convert_embeddings_to_torch` and optimizing for high-throughput GPU batch searches with parameters like `n_ivf_probe` and `n_full_scores`.
- **StanfordPlaid** wraps Stanford's ColBERT-style PLAID implementation using `Indexer` and `IndexUpdater` objects, reshaping embeddings to `(batch, tokens, dim)` via `reshape_embeddings`, and supporting true incremental updates and removal via `IndexUpdater.remove`.
- **Key architectural differences** include FastPlaid's lack of true vector removal (requiring local re-indexing) versus StanfordPlaid's native removal support, and FastPlaid's GPU-optimized IVF-PQ quantization versus StanfordPlaid's research-flexible Python wrappers.
- **Both implementations** share the same high-level API (`add_documents`, `remove_documents`, `__call__`) and maintain persistent ID-mapping pickles in [`pylate/indexes/fast_plaid.py`](https://github.com/lightonai/pylate/blob/main/pylate/indexes/fast_plaid.py) and [`pylate/indexes/stanford_plaid.py`](https://github.com/lightonai/pylate/blob/main/pylate/indexes/stanford_plaid.py) to ensure stable user-defined IDs across index rebuilds.

## Frequently Asked Questions

### What is the main performance difference between FastPlaid and StanfordPlaid?

FastPlaid is optimized for production latency and throughput, utilizing C++/CUDA kernels with optional Triton acceleration and efficient IVF-PQ quantization. StanfordPlaid, while functionally equivalent, incurs Python wrapper overhead from the original Stanford ColBERT implementation, making it more suitable for research prototyping than high-throughput serving.

### Can I switch between FastPlaid and StanfordPlaid without changing my embedding model?

Yes, both backends accept the same ColBERT-style multi-vector embeddings and share identical high-level APIs in PyLate. However, you must ensure embeddings conform to each backend's expected format: FastPlaid uses `convert_embeddings_to_torch` for list-of-tensors conversion, while StanfordPlaid uses `reshape_embeddings` to ensure `(batch, tokens, dim)` NumPy arrays.

### Does FastPlaid support incremental document removal?

No, FastPlaid does not support true vector removal. The `remove_documents` method in [`pylate/indexes/fast_plaid.py`](https://github.com/lightonai/pylate/blob/main/pylate/indexes/fast_plaid.py) (lines 30-44 and 56-72) deletes ID mappings and calls `self.fast_plaid.delete()`, but effectively requires re-indexing remaining documents locally. For use cases requiring frequent deletions, StanfordPlaid is preferable as it uses `IndexUpdater.remove()` for true incremental removal.

### Which backend should I choose for large-scale production deployment?

Choose **FastPlaid** for large-scale production deployments where query latency and throughput are critical. Configure `n_ivf_probe` and `n_full_scores` to tune the recall-speed trade-off, and enable `use_triton` for optimized GPU kernels. Choose **StanfordPlaid** if you require the specific research features of the original Stanford implementation, need true incremental updates with removal support, or are prototyping with the ColBERT ecosystem.