Balancing Precision and Performance: How Zilliz Cloud's New Parameters Help You Optimize Vector Search

Introduction

Over the past years, large language models (LLMs) and vector databases have become the backbone of countless AI-powered applications—from e-commerce recommendations to facial recognition and RAG systems. However, these diverse use cases present a common challenge: how do you balance search accuracy (recall rate) against performance (latency and throughput)?

This is precisely why we're excited to introduce two powerful new features in the latest release of Zilliz Cloud, our fully managed vector database built on Milvus:

• The level parameter - A simple yet powerful knob to fine-tune search accuracy

• The enable_recall_calculation parameter - A built-in tool to estimate and validate recall rates

These additions empower developers to find the perfect balance for their specific use cases—whether you need lightning-fast recommendations or highly accurate security applications. In this blog, we'll show you exactly how to leverage these features to optimize your vector search implementations.

Different Use Cases, Different Requirements

As vector search proliferates across industries, we've observed that requirements for recall, latency, and queries per second (QPS) vary dramatically between applications. Let's take a look at two contrasting examples:

Recommendation Systems: Speed over Perfect Accuracy

Recommendation systems filter vast content libraries to suggest relevant items based on user preferences. In these systems, recall rate isn't the top priority. While recommendations should be relevant, introducing some variety often enhances user discovery and engagement.

Instead, these systems must handle thousands of concurrent requests in real-time, requiring:

• High QPS to serve many users simultaneously

• Very low latency for responsive user experiences

• Moderate recall (85-95%) with tolerance for some imperfect matches

The business impact of slow recommendations usually outweighs occasional imperfect suggestions, making performance optimization critical.

Facial Recognition: Accuracy is Non-Negotiable

Facial recognition systems, especially in security contexts, have entirely different requirements. They must accurately identify authorized users to prevent both false positives (security breaches) and false negatives (legitimate users being denied).

These systems need:

• Very high recall (99%+) for accurate identification

• Tolerance for moderate latency (users accept a short verification delay)

• Lower QPS demands (verification is a relatively infrequent task)

The consequences of misidentification are significant, making accuracy the non-negotiable priority even at the cost of some performance.

The Common Thread: Finding Your Balance

These contrasting examples highlight why a one-size-fits-all approach to vector search configuration falls short. Every application sits somewhere on this spectrum, requiring thoughtful optimization of recall, latency, and QPS based on business needs.

This is precisely why we've introduced the level and enable_recall_calculation parameters—to give developers the tools to find their optimal balance.

Introducing Zilliz Cloud's Precision Tuning Features

The level Parameter: Fine-Tuning Search Accuracy

The level parameter offers simple yet powerful control over search accuracy, with values ranging from 1 to 10:

Level Value	Ideal For	Typical Recall	Performance Impact
Lower (1-3)	Performance-focused applications	90-97%	Minimal latency impact, highest QPS
Medium (4-7)	Balanced applications	97-99.5%	Moderate latency impact, good QPS
Higher (8-10)	Accuracy-critical applications	99.5%+	Higher latency, reduced QPS

We've extended the upper limit from 5 to 10 based on user feedback, enabling even higher precision for security, risk control, and many other accuracy demanding scenarios.

Please note that a higher recall rate doesn’t always good for your use cases. For example, if level=3 or 5 satisfies the needs, further increasing the level would only lead to unnecessary resource usage and higher latency.

The enable_recall_calculation Parameter: Validating Your Accuracy

While the level parameter adjusts search accuracy, how do you verify you're hitting your target recall rate? That's where enable_recall_calculation comes in.

When enabled during a search operation, this parameter:

• Estimates the actual recall rate of your current configuration

• Returns this value alongside your search results

• Enables data-driven decisions about configuration changes

This one-time calculation helps you validate whether your current settings meet your accuracy requirements without requiring external benchmarking tools.

Practical Implementation: A Step-by-Step Guide

Let's walk through a real-world example of optimizing a vector search implementation with these new features.

Prerequisites

• A Zilliz Cloud instance (sign up here if you haven't already)

• PyMilvus 2.5.4 or later

• Your test dataset (we'll use the cohere-1M dataset in this example)

Step 1: Define Your Requirements

Before diving into parameter tuning, clearly define your requirements:

• Target recall rate: 99.5%

• Maximum acceptable latency: 5ms

• Other considerations: QPS, resource utilization

• Query pattern: Pure vector search

• TopK: 16,000

Step 2: Import Test Data

For accurate testing, you should use data that closely resembles your production environment. In this example, we'll use the cohere-1M dataset and import it into Zilliz Cloud via VectorDBBench.

Step 3: Estimating Baseline Recall Rate

Let's first check how the default settings (level=1) perform:

search_params = {
    "params": {
        "level": 1,
        "enable_recall_calculation": True
    }
}

res = client.search(
    collection_name = "ZillizCloudVectorDBBench", 
    # test data 
    data = [
        [0.22252834,0.26758388,0.3414864,0.31775144,0.25819996,-0.06176423,0.60313016,-0.31930527,-0.05070293,0.80085576,-0.7066278,-0.14704825,0.07324219,-0.051405758,0.24823247,0.20365287,-0.005265507,0.24754052,0.06302843,-0.24397966,-0.2805658,0.543768,0.018544307,0.14154078,0.03093845,-0.25058296,-0.61569184,0.08389459,-0.27519965,0.2121497,0.26527727,-0.03291658,-0.2631627,0.026973603,-0.22165383,0.38862047,0.012616088,-0.066382475,-0.013436819,-0.59001106,-0.08682751,0.13704056,0.08583454,0.0802483,0.01096984,0.20214474,0.11156094,0.5482859,-0.0807617,-0.16539982,-0.29261217,0.08269717,0.03385099,-0.48874223,-0.013168857,0.01616468,-0.6270225,0.13169415,1.0166928,0.6573267,0.40487188,0.2235163,-0.68331105,-0.24911362,-0.1763628,0.34692895,0.077760294,0.96388775,-0.10841275,0.3977706,0.08965021,0.29019687,0.106024966,0.11854912,-0.070255764,-0.24960922,-0.5354312,-0.70186704,0.25364435,0.43369204,0.42516047,0.15078346,-0.35151976,0.4886603,0.37026608,0.39485633,-0.046821307,0.18807457,0.13850202,-0.15630293,0.1469321,0.13219471,-0.31201053,0.22278261,-0.23063508,0.42379102,0.66762435,-0.11903275,0.22101034,-0.10102379,0.21675844,-0.2571628,-0.26546052,0.6185626,0.241754,-0.15792729,-0.37045696,0.23996626,-0.27011713,-0.11512929,0.31577003,0.11118326,-0.76019096,-0.1682175,0.5329204,0.3614348,-0.2348311,0.108711846,0.068240434,-1.3124024,0.20385003,0.6049856,-0.2924265,-0.07293733,0.5181119,0.15742214,0.7537572,-0.656809,0.57201636,0.09775318,0.5414663,-0.53804034,-0.0802571,0.62367904,0.023681821,0.6950041,0.3207407,0.36089638,0.53875273,-0.7288189,-0.12956187,0.15076943,0.057313107,0.41065657,-0.00928412,0.090415776,0.18091775,-0.010793066,-0.010142676,-0.20986095,-0.3740831,0.25086942,-0.31494206,0.17761512,0.04850758,-0.06098805,0.7605751,0.3038707,0.5178377,-0.5769507,0.80365956,0.22879237,-0.34868854,-0.2688102,-0.20910782,0.3392469,0.2990533,0.5502763,0.6561665,0.04177933,-0.45408615,-0.055697974,0.05596695,-0.22720425,0.63778013,-0.1921504,-0.16227654,-0.053658817,0.04536426,-0.28570235,0.30350783,0.5217574,0.0025516534,0.10135456,0.5973671,0.09276529,0.7803261,0.45648357,-0.21722879,-0.3496141,0.18574907,0.1729008,0.6754883,-0.5101994,0.16308193,-0.32053986,-0.0013728795,0.0371755,-0.114131995,0.19870742,0.3973309,0.17016156,0.016581284,0.3074155,0.32889378,-0.6682561,0.36933577,0.45571226,-0.19315217,-0.5065343,0.15996625,0.026897952,0.046443015,0.2667398,-0.18946062,-0.4283052,-0.44281873,-0.24062063,0.41703427,-0.30064407,0.35975343,0.31060407,0.18125875,0.14912511,0.10962614,-0.06901708,-0.2846222,-0.027887726,0.037055127,0.031954445,0.56672156,-0.0863331,0.1497875,-0.1635759,-0.25121027,0.6303942,0.17385906,0.4313834,0.15800661,-0.6267578,-0.03539913,0.32520285,0.42759246,0.24401832,0.2115575,-0.8652025,0.13317755,-0.5719402,0.17294376,-0.12595764,0.34818307,0.24802469,0.05904272,0.1538172,-0.57994705,0.2582915,0.45511153,-0.44164076,-0.074042775,0.04943926,-0.1648779,0.3280813,0.5601293,-0.0018850226,-0.140464,-0.07845455,0.44026145,0.56197315,0.1462102,-0.18595229,0.014953136,0.46956787,-0.14819877,0.1859354,0.019512085,-0.01712815,0.5366789,0.7835224,-0.7423546,0.6503855,0.44647282,0.3631722,-0.66614413,0.10151727,-0.1348695,0.32992417,0.10387001,-0.26746857,-0.33413792,-0.5662058,0.36110422,0.7741211,-0.039930806,-0.15249825,0.09454683,0.4891987,0.0062028184,0.06745152,0.55465925,-0.06739082,0.5588079,-0.43696547,0.555966,0.56702,0.056295626,-0.62005293,-0.3722073,0.21030217,-0.017268468,0.95288086,0.51696795,-0.25066343,-0.3169728,0.42543235,0.31396082,0.17551036,0.3922707,0.07407632,0.91187936,0.38888615,-0.12070266,0.011815081,-0.45720986,0.04727247,0.62094855,-0.45443076,0.16062841,-0.40287957,-0.55417335,-0.3830013,0.055438586,0.1718703,-0.6422826,0.22917171,-0.5290951,0.1585279,0.07934802,0.50577295,-0.035466444,0.088082545,0.5693788,0.11773129,0.1821725,0.41347143,-0.2278498,0.50422746,0.29794943,-0.9369089,0.47065943,0.28594327,-0.6866015,-0.63375616,-0.15243755,-0.46409172,-0.4630497,-0.25025153,0.6375835,0.54886156,0.19831929,-0.03725618,0.20592122,0.36338213,0.31409082,-0.05410012,0.14887711,0.09740482,0.05067692,0.14775206,0.28025475,-0.34377113,-0.27423778,-0.354568,-0.20043556,0.3899774,-0.19812085,-0.36292082,-0.18255037,0.07038237,0.642794,-0.060884897,0.2948623,0.68766963,0.6928454,-0.3849391,0.17996079,0.12549743,0.10299729,-0.25861496,-0.09246836,-0.32353002,-0.01378604,-0.095313616,-0.04558251,0.20014873,-0.4066689,-0.08052519,-0.4618455,0.37693843,0.45283204,-0.114583425,0.050728872,0.13196129,-0.1941961,-0.11727777,3.9586966,0.05150596,0.11701303,0.5739518,0.07567582,0.48247826,0.25156844,0.38180268,0.12796494,0.009531708,-0.04081167,-0.30954623,-0.035167653,0.43064785,0.24091315,-0.11113215,0.027972942,0.3501582,0.54151994,0.14281327,-0.6420307,0.48611403,0.5221564,0.47878447,0.8510151,0.5528693,0.27463847,0.7548287,0.760392,0.4057206,0.5247366,0.6815815,0.46189928,-0.0665814,0.29575244,-0.13240056,0.44400057,-0.26975283,-0.15510945,0.15475176,-0.46221858,0.054507546,0.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    ], 
    limit = 16000, 
    search_params = search_params
)

print(f"recall: {res.recalls}")

The output is:

recall: [0.9886875152587891]   

With a recall of 98.87%, we're below our target of 99.5%. Let's measure latency too (optional):

search_params = {
    "params": {
        "level": 1,
#        "enable_recall_calculation": True
    }
}

start_time = time.perf_counter()
client.search(collection_name = "ZillizCloudVectorDBBench",
    data = data,
    limit = limit,
    search_params = search_params
)
end_time = time.perf_counter()

elapsed_time = end_time - start_time
print(f"latency at level=1: {elapsed_time:.2f} seconds")

And the output is:

latency at level=1: 0.03 seconds

Step 4: Find the Optimal Level Value

Now, let's systematically test different level values to find the optimal configuration:

recall at level=1: [0.9886875152587891]
latency at level=1: 0.03 seconds
...
recall at level=6: [0.9947500228881836]
latency at level=6: 0.04 seconds
recall at level=7: [0.9961249828338623]
latency at level=7: 0.04 seconds
...
recall at level=10: [1.0]
latency at level=10: 0.06 seconds

The experiments show that level=7 meets our recall target (99.61% > 99.5%) while keeping latency within acceptable limits.

Step 5: Monitoring System Performance (Optional)

Zilliz Cloud offers a comprehensive set of system metrics to help you monitor performance effectively. When adjusting the level parameter and applying new settings, Zilliz Cloud provides real-time tracking of key metrics like QPS and latency. This allows you to assess the impact of parameter adjustments on overall system performance, enabling more informed decision-making.

Step 6: Validate with Production Workload

For production validation, we recommend:

1. Running tests with representative query vectors

2. Monitoring the Zilliz Cloud dashboard for:

   • Average latency

   • QPS

   • Resource utilization

Step 7: Conduct Comprehensive Benchmarking (Optional)

For a more rigorous evaluation, we used VectorDBBench, an open-source benchmarking tool, to test various configurations with 1,000 search operations:

level	Avg latency(ms)	QPS	Recall from VectorDBBench	Recall from Zilliz Cloud
1	3.1	1266	0.9519	0.953
2	3.2	1080	0.9644	0.9669
3	3.4	972	0.9728	0.9755
4	3.6	814	0.9816	0.9846
5	3.9	704	0.9871	0.99
6	4.4	549	0.9916	0.995
7	5	448	0.9936	0.9971
8	5.4	375	0.9945	0.9983
9	5.9	340	0.9952	0.9991
10	6.3	296	0.9958	0.9995

Table 1: Test Results based on 1,000 search operations from VDBBench

These benchmarking results confirm that:

1. The enable_recall_calculation estimates closely match actual recall performance

2. Higher recall comes at the cost of increased latency and reduced QPS

3. For our target requirements, level=7 provides the optimal balance

It is important to note that improvements in recall generally come at the cost of increased latency and reduced QPS. If the current recall meets your business needs but QPS is suboptimal, you may consider scaling up CU resources or adding replicas to enhance throughput.

Step 8: Confirm the Optimal Configuration

Based on the experimental results, the optimal configuration in this example is level=7, which delivers a 99.6% recall while maintaining acceptable latency.

Conclusion

Finding the perfect balance between search accuracy and performance has long been a challenge for vector database users. With Zilliz Cloud's new level and enable_recall_calculation parameters, developers now have powerful tools to optimize their vector search implementations for their specific requirements.

Whether you're building recommendation systems that prioritize speed, security applications that demand high accuracy, or anything in between, these features enable you to:

1. Precisely tune search accuracy to meet your requirements

2. Validate actual recall rates with built-in estimation

3. Make data-driven decisions about configuration trade-offs

We're committed to continuing to enhance Zilliz Cloud with features that make vector search more powerful, flexible, and easy to optimize. These new parameters are just the beginning of our journey to empower developers to build exceptional AI-powered applications.

Ready to start optimizing your vector searches? Sign up for Zilliz Cloud today or log in to your existing account to explore these new features.