Scaling Vector Search with MongoDB Atlas Quantization & Voyage AI Embeddings
Key Takeaways
Vector quantization fundamentals:
A technique that compresses high-dimensional embeddings from 32-bit floats to lower precision formats (scalar/int8 or binary/1-bit), enabling significant performance gains while maintaining semantic search capabilities
Performance vs. precision trade-offs:
Binary quantization provides maximum speed (80% faster queries) with minimal resources; scalar quantization offers balanced performance and accuracy; float32 maintains highest fidelity at significant resource cost
Resource optimization:
Vector quantization can reduce RAM usage by up to 24x (binary) or 3.75x (scalar); storage footprint decreases by 38% using BSON binary format
Scaling benefits:
Performance advantages multiply at scale; most significant for vector databases exceeding 1M embeddings
Semantic preservation:
Quantization-aware models like Voyage AI's retain high representation capacity even after compression
Search quality control:
Binary quantization may require rescoring for maximum accuracy; scalar quantization typically maintains 90%+ retention of float32 results
Implementation ease:
MongoDB's automatic quantization requires minimal code changes to leverage quantization techniques
As vector databases scale into the millions of embeddings, the computational and memory requirements of high-dimensional vector operations become critical bottlenecks in production AI systems. Without effective scaling strategies, organizations face:
Infrastructure costs that grow exponentially with data volume
Unacceptable query latency that degrades user experience and limits real-time applications
Limited and restricted deployment options, particularly on edge devices or resource-constrained environments
Diminished competitive advantage as AI capabilities become limited by technical constraints and bottlenecks rather than use case innovation
This technical guide demonstrates advanced techniques for optimizing vector search operations through precision-controlled quantization—transforming resource-intensive 32-bit float embeddings into performance-optimized representations while preserving semantic fidelity. By leveraging
MongoDB Atlas Vector Search
’s automatic quantization capabilities with Voyage AI's quantization-aware embedding models, we'll implement systematic optimization strategies that dramatically reduce both computational overhead and memory footprint.
This guide provides an empirical analysis of the critical performance metrics:
Retrieval latency benchmarking:
Quantitative comparison of search performance across binary, scalar, and float32 precision levels with controlled evaluation of HNSW(hierarchical navigable small world) graph exploration parameters and k-retrieval variations.
Representational capacity retention:
Precise measurement of semantic information preservation through direct comparison of quantized vector search results against full-fidelity retrieval, with particular attention to retention curves across varying retrieval depths.
We'll present implementation strategies and evaluation methodologies for vector quantization that simultaneously optimize for both computational efficiency and semantic fidelity—enabling you to make evidence-based architectural decisions for production-scale AI retrieval systems handling millions of embeddings. The techniques demonstrated here are directly applicable to enterprise-grade RAG architectures, recommendation engines, and semantic search applications where millisecond-level latency improvements and dramatic RAM reduction translate to significant infrastructure cost savings.
The full end to end implementation for automatic vector quantization and other operations involved in RAG/Agent pipelines can be found on our
Github repository
.
Auto-quantization of Voyage AI embeddings with MongoDB
Our approach addresses the complete optimization cycle for vector search operations, covering:
Generating embeddings with quantization-aware models
Implementing automatic vector quantization in MongoDB Atlas
Creating and configuring specialized vector search indices
Measuring and comparing latency across different quantization strategies
Quantifying representational capacity retention
Analyzing performance trade-offs between binary, scalar, and float32 implementations
Making evidence-based architectural decisions for production AI retrieval systems
Figure 1.
Vector quantization architecture with MongoDB Atlas and Voyage AI.
Using text data as an example, we convert documents into numerical vector embeddings that capture semantic relationships. MongoDB then indexes and stores these embeddings for efficient similarity searches. By comparing queries run against float32, int8, and binary embeddings, you can gauge the trade-offs between precision and performance and better understand which quantization strategy best suits large-scale, high-throughput workloads.
One key takeaway from this article is that representational capacity retention is highly dependent on the embedding model used. With quantization-aware models like Voyage AI’s voyage-3-large at appropriate dimensionality (1024 dimensions), our tests demonstrate that we can achieve 95%+ recall retention at reasonable numCandidate values. This means organizations can significantly reduce memory and computational requirements while preserving semantic search quality, provided they select embedding models specifically designed to maintain their representation capacity after quantization.
For more information on why vector quantization is crucial for AI workloads, refer to this
blog post
.
Dataset information
Our quantization evaluation framework leverages two complementary datasets designed specifically to benchmark semantic search performance across different precision levels.
Primary Dataset (
Wikipedia-22-12-en-voyage-embed
):
Contains approximately 300,000 Wikipedia article fragments with pre-generated 1024-dimensional embeddings from Voyage AI’s voyage-3-large model. This dataset serves as a diverse vector corpus for testing vector quantization effects in semantic search. Throughout this tutorial, we'll use the primary dataset to demonstrate the technical implementation of quantization.
Embedding generation with Voyage AI
For generating new embeddings for AI Search applications, we use Voyage AI's
voyage-3-large
model, which is specifically designed to be quantization-aware. The voyage-3-large model generates 1024-dimensional vectors and has been specifically trained to maintain semantic properties even after quantization, making it ideal for our AI retrieval optimization strategy. For more information on how MongoDB and Voyage AI work together for optimal retrieval, see our previous article,
Rethinking Information Retrieval with MongoDB and Voyage AI
.
import voyageai
# Initialize the Voyage AI client
client = voyageai.Client()
def get_embedding(text, task_prefix="document"):
"""
Generate embeddings using the voyage-3-large model for AI Retrieval.
Parameters:
text (str): The input text to be embedded.
task_prefix (str): A prefix describing the task; this is prepended to the text.
Returns:
list: The embedding vector (1024 dimensions).
"""
if not text.strip():
print("Attempted to get embedding for empty text.")
return []
# Call the Voyage API to generate the embedding
result = client.embed([text], model="voyage-3-large", input_type=task_prefix)
# Return the first embedding from the result
return result.embeddings[0]
Converting embeddings to BSON BinData format
A critical optimization step is converting embeddings to
MongoDB's BSON BinData format
, which significantly reduces storage and memory requirements. The BinData vector format provides significant advantages:
Reduces disk space by approximately 3x compared to arrays
Enables more efficient indexing with alternate types (int8, binary)
Reduces RAM usage by 3.75x for scalar and 24x for binary quantization
from bson.binary import Binary, BinaryVectorDtype
def generate_bson_vector(array, data_type):
return Binary.from_vector(array, BinaryVectorDtype(data_type))
# Convert embeddings to BSON BinData vector format
wikipedia_data_df["embedding"] = wikipedia_data_df["embedding"].apply(
lambda x: generate_bson_vector(x, BinaryVectorDtype.FLOAT32)
)
Vector index creation with different quantization strategies
The cornerstone of our performance optimization framework lies in creating specialized vector indices with different quantization strategies. This process leverages MongoDB for general-purpose database functionalities, more specifically, its high-performance vector database capabilities of efficiently handling million-scale embedding collections.
This implementation step focuses on how to set up MongoDB's vector search capabilities with automatic quantization, focusing on two primary quantization strategies: scalar (int8) and binary. Two indices are created to measure and evaluate the retrieval latency and recall performance of various precision data types, including the full fidelity vector representation.
The MongoDB database uses the vector index HNSW, which is a graph-based indexing algorithm that organizes vectors in a hierarchical structure of layers. In this structure, vector data points within a layer are contextually similar, while higher layers are sparse compared to lower layers, which are denser and contain more vector data points.
The code snippet below showcases the implementation of two quantization strategies in parallel; this enables the systematic evaluation of the latency, memory usage, and representational capacity trade-offs across the precision spectrum, enabling data-driven decisions about the optimal approach for specific application requirements.
MongoDB Atlas automatic quantization is activated entirely through the vector index definition. By including the "quantization" attribute and setting its value to either "scalar" or "binary", you enable automatic compression of your embeddings at index creation time. This declarative approach means no separate preprocessing of vectors is required—MongoDB handles the dimensional reduction transparently while maintaining the original embeddings for potential rescoring operations.
from pymongo.operations import SearchIndexModel
def setup_vector_search_index(collection, index_definition, index_name="vector_index"):
"""Setup a vector search index with the specified configuration"""
...
# 1. Scalar Quantized Index (int8)
vector_index_definition_scalar_quantized = {
"fields": [{
"type": "vector",
"path": "embedding",
"quantization": "scalar", # Uses int8 quantization
"numDimensions": 1024,
"similarity": "cosine",
}]
}
# 2. Binary Quantized Index (1-bit)
vector_index_definition_binary_quantized = {
"fields": [{
"type": "vector",
"path": "embedding",
"quantization": "binary", # Uses binary (1-bit) quantization
"numDimensions": 1024,
"similarity": "cosine",
}]
}
# 3. Float32 ANN Index (no quantization)
vector_index_definition_float32_ann = {
"fields": [{
"type": "vector",
"path": "embedding",
"numDimensions": 1024,
"similarity": "cosine",
}]
}
# Create the indices
setup_vector_search_index(
wiki_data_collection,
vector_index_definition_scalar_quantized,
"vector_index_scalar_quantized"
)
setup_vector_search_index(
wiki_data_collection,
vector_index_definition_binary_quantized,
"vector_index_binary_quantized"
)
setup_vector_search_index(
wiki_data_collection,
vector_index_definition_float32_ann,
"vector_index_float32_ann"
)
Implementing vector search functionality
Vector search serves as the computational foundation of modern generative AI systems. While LLMs provide reasoning and generation capabilities, vector search delivers the contextual knowledge necessary for grounding these capabilities in relevant information.
This semantic retrieval operation forms the backbone of RAG architectures that power enterprise-grade AI applications, such as knowledge-intensive chatbots and domain-specific assistants. In more advanced implementations, vector search enables agentic RAG systems where autonomous agents dynamically determine what information to retrieve, when to retrieve it, and how to incorporate it into complex reasoning chains.
The implementation below provides the technical overview that transforms raw embedding vectors into intelligent search components that move beyond lexical matching to true semantic understanding.
Our implementation below supports both approximate nearest neighbor (ANN) search and exact nearest neighbor (ENN) search through the
use_full_precision
parameter:
Approximate nearest neighbor (ANN) search:
When
use_full_precision = False
, the system performs an approximate search using:
The specified quantized index (binary or scalar)
The HNSW graph navigation algorithm
A controlled exploration breadth via
numCandidates
This approach sacrifices perfect accuracy for dramatic performance gains, particularly at scale. The HNSW algorithm enables sub-linear time complexity by intelligently sampling the vector space, making it possible to search billions of vectors in milliseconds instead of seconds. When combined with quantization, ANN delivers order-of-magnitude improvements in both speed and memory efficiency.
Exact nearest neighbor (ENN) search:
When
use_full_precision = True
, the system performs exact search using:
The original float32 embeddings (regardless of the index specified)
An exhaustive comparison approach
The
exact = True
directive to bypass approximation techniques
ENN guarantees finding the mathematically optimal nearest neighbors by computing distances between the query vector and every single vector in the database. This brute-force approach provides perfect recall but scales linearly with collection size, becoming prohibitively expensive as vector counts increase beyond millions.
We include both search modes for several critical reasons:
Establishing ground truth:
ENN provides the "perfect" baseline against which we measure the quality degradation of approximation techniques. The representational retention metrics discussed later directly compare ANN results against this ENN ground truth.
Varying application requirements:
Not all AI applications prioritize the same metrics. Time-sensitive applications (real-time customer service) might favor ANN's speed, while high-stakes applications (legal document analysis) might require ENN's accuracy.
def custom_vector_search(
user_query,
collection,
embedding_path,
vector_search_index_name="vector_index",
top_k=5,
num_candidates=25,
use_full_precision=False,
):
"""
Perform vector search with configurable precision and parameters for AI Search applications.
"""
# Generate embedding for the query
query_embedding = get_embedding(user_query, task_prefix="query")
# Define the vector search stage
vector_search_stage = {
"$vectorSearch": {
"index": vector_search_index_name,
"queryVector": query_embedding,
"path": embedding_path,
"limit": top_k,
}
}
# Configure search precision approach
if not use_full_precision:
# For approximate nearest neighbor (ANN) search
vector_search_stage["$vectorSearch"]["numCandidates"] = num_candidates
else:
# For exact nearest neighbor (ENN) search
vector_search_stage["$vectorSearch"]["exact"] = True
# Project only needed fields
project_stage = {
"$project": {
"_id": 0,
"title": 1,
"text": 1,
"wiki_id": 1,
"url": 1,
"score": {"$meta": "vectorSearchScore"}
}
}
# Build and execute the pipeline
pipeline = [vector_search_stage, project_stage]
...
# Execute the query
results = list(collection.aggregate(pipeline))
return {"results": results, "execution_time_ms": execution_time_ms}
Measuring the retrieval latency of various quantized vectors
In production AI retrieval systems, query latency directly impacts user experience, operational costs, and system throughput capacity. Vector search operations typically constitute the primary performance bottleneck in RAG architectures, making latency optimization a critical engineering priority.
Sub-100ms response times are often necessary for interactive applications and mission-critical applications, while batch processing systems may tolerate higher latencies but require consistent predictability for resource planning.
Our latency measurement methodology employs a systematic, parameterized approach that models real-world query patterns while isolating the performance characteristics of different quantization strategies. This parameterized benchmarking enables us to:
Construct detailed latency profiles across varying retrieval depths
Identify performance inflection points where quantization benefits become significant
Map the scaling curves of different precision levels as the data volume increases
Determine optimal configuration parameters for specific throughput targets
def measure_latency_with_varying_topk(
user_query,
collection,
vector_search_index_name,
use_full_precision=False,
top_k_values=[5, 10, 50, 100],
num_candidates_values=[25, 50, 100, 200, 500, 1000, 2000],
):
"""
Measure search latency across different configurations.
"""
results_data = []
for top_k in top_k_values:
for num_candidates in num_candidates_values:
# Skip invalid configurations
if num_candidates < top_k:
continue
# Get precision type from index name
precision_name = vector_search_index_name.split("vector_index")[1]
precision_name = precision_name.replace("quantized", "").capitalize()
if use_full_precision:
precision_name = "_float32_ENN"
# Perform search and measure latency
vector_search_results = custom_vector_search(
user_query=user_query,
collection=collection,
embedding_path="embedding",
vector_search_index_name=vector_search_index_name,
top_k=top_k,
num_candidates=num_candidates,
use_full_precision=use_full_precision,
)
latency_ms = vector_search_results["execution_time_ms"]
# Store results
results_data.append({
"precision": precision_name,
"top_k": top_k,
"num_candidates": num_candidates,
"latency_ms": latency_ms,
})
print(f"Top-K: {top_k}, NumCandidates: {num_candidates}, "
f"Latency: {latency_ms} ms, Precision: {precision_name}")
return results_data
Latency results analysis
Our systematic benchmarking reveals dramatic performance differences between quantization strategies across different retrieval scenarios. The visualizations below capture these differences for top-k=10 and top-k=100 configurations.
Figure 2.
Search latency vs the number candidates for top-k=10
Figure 3.
Search latency vs the number of candidates for top-k=100.
Several critical patterns emerge from these latency profiles:
Quantization delivers exponential performance gains:
The float32_ENN approach (purple line) demonstrates latency measurements an order of magnitude higher than any quantized approach. At top-k=10, ENN latency starts at ~1600ms and never drops below 500ms, while quantized approaches maintain sub-100ms performance until extremely high candidate counts. This performance gap widens further as data volume scales.
Scalar quantization offers the best performance profile:
Somewhat surprisingly, scalar quantization (orange line) consistently outperforms both binary quantization and float32 ANN across most configurations. This is particularly evident at higher num_candidates values, where scalar quantization maintains near-flat latency scaling. This suggests scalar quantization achieves an optimal balance in the memory-computation trade-off for HNSW traversal.
Binary quantization shows linear latency scaling:
While binary quantization (red line) starts with excellent performance, its latency increases more steeply as num_candidates grows, eventually exceeding scalar quantization at very high exploration depths. This suggests that while binary vectors require less memory, their distance computation savings are partially offset by the need for more complex traversal patterns in the HNSW graph and rescoring.
All quantization methods maintain interactive-grade performance:
Even with 10,000 candidate explorations and top-k=100, all quantized approaches maintain sub-200ms latency, well within interactive application requirements. This demonstrates that quantization enables order-of-magnitude increases in exploration depth without sacrificing user experience, allowing for dramatic recall improvements while maintaining acceptable latency.
These empirical results validate our theoretical understanding of quantization benefits and provide concrete guidance for production deployment: scalar quantization offers the best general-purpose performance profile, while binary quantization excels in memory-constrained environments with moderate exploration requirements.
In the images below we employ logarithmic scaling for both axes in our latency analysis because search performance data typically spans multiple orders of magnitude.
When comparing different precision types (scalar, binary, float32_ann) across varying numbers of candidates, the latency values can range from milliseconds to seconds, while candidate counts may vary from hundreds to millions.
Linear plots would compress smaller values and make it difficult to observe performance trends across the full range(as we see above). Logarithmic scaling transforms exponential relationships into linear ones, making it easier to identify proportional changes, compare relative performance improvements, and detect patterns that would otherwise be obscured.
This visualization approach is particularly valuable for understanding how each precision type scales with increasing workload and for identifying the optimal operating ranges where certain methods outperform others(as shown below).
Figure 4.
Search latency vs the number of candidates (log scale) for top-k=10.
Figure 5.
Search latency vs the number of candidates (log scale) for top-k=100.
The performance characteristics observed in the logarithmic plots above directly reflect the architectural differences inherent in binary quantization's two-stage retrieval process. Binary quantization employs a coarse-to-fine search strategy: an initial fast retrieval phase using low-precision binary representations, followed by a refinement phase that rescores the top-k candidates using full-precision vectors to restore accuracy.
This dual-phase approach creates a fundamental performance trade-off that manifests differently across varying candidate pool sizes. For smaller candidate sets, the computational savings from binary operations during the initial retrieval phase can offset the rescoring overhead, making binary quantization competitive with other methods. However, as the candidate pool expands, the rescoring phase—which must compute full-precision similarity scores for an increasing number of retrieved candidates—begins to dominate the total latency profile.
Measuring representational capacity retention
While latency optimization is critical for operational efficiency, the primary concern for AI applications remains semantic accuracy. Vector quantization introduces a fundamental trade-off: computational efficiency versus representational capacity. Even the most performant quantization approach is useless if it fails to maintain the semantic relationships encoded in the original embeddings.
To quantify this critical quality dimension, we developed a systematic methodology for measuring representational capacity retention—the degree to which quantized vectors preserve the same nearest-neighbor relationships as their full-precision counterparts. This approach provides an objective, reproducible framework for evaluating semantic fidelity across different quantization strategies.
def measure_representational_capacity_retention_against_float_enn(
ground_truth_collection,
collection,
quantized_index_name,
top_k_values,
num_candidates_values,
num_queries_to_test=1,
):
"""
Compare quantized search results against full-precision baseline.
For each test query:
1. Perform baseline search with float32 exact search
2. Perform same search with quantized vectors
3. Calculate retention as % of baseline results found in quantized results
"""
retention_results = {"per_query_retention": {}}
overall_retention = {}
# Initialize tracking structures
for top_k in top_k_values:
overall_retention[top_k] = {}
for num_candidates in num_candidates_values:
if num_candidates < top_k:
continue
overall_retention[top_k][num_candidates] = []
# Get precision type
precision_name = quantized_index_name.split("vector_index")[1]
precision_name = precision_name.replace("quantized", "").capitalize()
# Load test queries from ground truth annotations
ground_truth_annotations = list(
ground_truth_collection.find().limit(num_queries_to_test)
)
# For each annotation, test all its questions
for annotation in ground_truth_annotations:
ground_truth_wiki_id = annotation["wiki_id"]
...
# Calculate average retention for each configuration
avg_overall_retention = {}
for top_k, cand_dict in overall_retention.items():
avg_overall_retention[top_k] = {}
for num_candidates, retentions in cand_dict.items():
if retentions:
avg = sum(retentions) / len(retentions)
else:
avg = 0
avg_overall_retention[top_k][num_candidates] = avg
retention_results["average_retention"] = avg_overall_retention
return retention_results
Our methodology takes a rigorous approach to retention measurement:
Establishing ground truth:
We use float32 exact nearest neighbor (ENN) search as the baseline "perfect" result set, acknowledging that these are the mathematically optimal neighbors.
Controlled comparison:
For each query in our annotation dataset, we perform parallel searches using different quantization strategies, carefully controlling for top-k and num_candidates parameters.
Retention calculation:
We compute retention as the ratio of overlapping results between the quantized search and the ENN baseline: |quantized_results ∩ baseline_results| / |baseline_results|.
Statistical aggregation:
We average retention scores across multiple queries to account for query-specific variations and produce robust, generalizable metrics.
This approach provides a direct, quantitative measure of how much semantic fidelity is preserved after quantization. A retention score of 1.0 indicates that the quantized search returns exactly the same results as the full-precision search, while lower scores indicate divergence.
Representational capacity results analysis
The findings from the representational capacity retention evaluation provide empirical validation that properly implemented quantization—particularly scalar quantization—can maintain semantic fidelity while dramatically reducing computational and memory requirements.
Note that in the chart below, the scalar curve (yellow) exactly matches the float32_ann performance (blue)—so much so that the blue line is completely hidden beneath the yellow. The near-perfect retention of scalar quantization should alleviate concerns about quality degradation, while binary quantization's retention profile suggests it's suitable for applications with higher performance demands that can tolerate slight quality trade-offs or compensate with increased exploration depth.
Figure 6.
Retention score vs the number of candidates for top-k=10.
Figure 7.
Retention score vs the number of candidates for top-k=50.
Figure 8.
Retention score vs the number of candidates for top-k=100.
Scalar quantization achieves near-perfect retention:
The scalar quantization approach (orange line) demonstrates extraordinary representational capacity preservation, achieving 98-100% retention across nearly all configurations. At top-k=10, it reaches perfect 1.0 retention with just 100 candidates, effectively matching full-precision ENN results while using 4x less memory. This remarkable performance validates the effectiveness of int8 quantization when implemented with MongoDB's automatic quantization.
Binary quantization shows retention-exploration trade-off:
Binary quantization (red line) exhibits a clear correlation between exploration depth and retention quality. At top-k=10, it starts at ~91% retention with minimal candidates but improves to 98% at 500 candidates. The effect is more pronounced at higher top-k values (50 and 100), where initial retention drops to ~74% but recovers substantially with increased exploration. This suggests that binary quantization's information loss can be effectively mitigated by exploring more of the vector space.
Retention dynamics change with retrieval depth:
As top-k increases from 10 to 100, the retention patterns become more differentiated between quantization strategies. This reflects the increasing challenge of maintaining accurate rankings as more results are requested. While scalar quantization remains relatively stable across different top-k values, binary quantization shows more sensitivity, indicating it's better suited for targeted retrieval scenarios (low top-k) than for broad exploration.
Exploration depth compensates for precision loss:
A fascinating pattern emerges across all quantization methods: increased num_candidates consistently improves retention. This demonstrates that reduced precision can be effectively counterbalanced by broader exploration of the vector space. For example, binary quantization at 500 candidates achieves better retention than scalar quantization at 25 candidates, despite using 32x less memory per vector.
Float32 ANN vs. scalar quantization:
The float32 ANN approach (blue line) shows virtually identical retention to scalar quantization at higher top-k values, while consuming 4x more memory. This suggests scalar quantization represents an optimal balance point, offering full-precision quality with significantly reduced resource requirements.
Conclusion
This guide has demonstrated the powerful impact of vector quantization in optimizing vector search operations through MongoDB Atlas Vector Search and automatic quantization feature, using Voyage AI embeddings. These findings provide empirical validation that properly implemented quantization—particularly scalar quantization—can maintain semantic fidelity while dramatically reducing computational and memory requirements.
The near-perfect retention of scalar quantization should alleviate concerns about quality degradation, while binary quantization's retention profile suggests it's suitable for applications with higher performance demands that can tolerate slight quality trade-offs or compensate with increased exploration depth.
Binary quantization achieves optimal latency and resource efficiency, particularly valuable for high-scale deployments where speed is critical.
Scalar quantization provides an effective balance between performance and precision, suitable for most production applications.
Float32 maintains maximum accuracy but incurs significant performance and memory costs.
Figure 9.
Performance and memory usage metrics for binary quantization, scalar quantization, and float32 implementation.
Based on the image above our implementation demonstrated substantial efficiency gains:
Binary Quantized Index achieves the most compact disk footprint at 407.66MB, representing approximately 4KB per document. This compression comes from representing high-dimensional vectors as binary bits, dramatically reducing storage requirements while maintaining retrieval capability.
Float32 ANN Index requires 394.73MB of disk space, slightly less than binary due to optimized index structures, but demands the full storage footprint be loaded into memory for optimal performance.
Scalar Quantized Index shows the largest storage requirement at 492.83MB (approximately 5KB per document), suggesting this method maintains higher precision than binary while still applying compression techniques, resulting in a middle-ground approach between full precision and extreme quantization.
The most striking difference lies in memory requirements. Binary quantization demonstrates a 23:1 memory efficiency ratio, requiring only 16.99MB in RAM versus the 394.73MB needed by float32_ann. Scalar quantization provides a 3:1 memory optimization, requiring 131.42MB compared to float32_ann's full memory footprint.
For production AI Retrieval implementation, general guidance is as follows:
Use scalar quantization for general use cases requiring good balance of speed and accuracy.
Use binary quantization for large-scale applications (1M+ vectors) where speed is critical.
Use float32 only for applications requiring maximum precision, where accuracy is paramount.
Vector quantization becomes particularly valuable for databases exceeding 1M vectors, where it enables significant scalability improvements without compromising retrieval accuracy. When combined with
MongoDB Atlas Search Nodes
, this approach effectively addresses both cost and performance constraints in advanced vector search applications.
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June 10, 2025
