Benchmarking AI Retrieval Latency Across Fabric IQ and Vector Stores

This prevents the benchmark from optimizing performance at the expense of retrieval quality.

Recall@K

Suppose the evaluation dataset contains the expected source document for each query.

If the correct document appears in the top five results:

Recall@5 = 1

Otherwise:

Recall@5 = 0

Across many queries:

Recall@5 =
Queries with relevant result in top 5
-------------------------------------
Total queries

This can be implemented in a benchmark harness:

bool ContainsExpectedDocument(
    IReadOnlyList<RetrievalResult> results,
    string expectedDocumentId)
{
    return results.Any(
        x => x.DocumentId == expectedDocumentId);
}

For more complex questions, there may be multiple acceptable source documents.

Measuring Ranking Quality

Recall does not tell you where the relevant document appeared.

For example:

Query A:
1. Correct
2. Irrelevant
3. Irrelevant

is preferable to:

Query B:
1. Irrelevant
2. Irrelevant
3. Correct

Mean Reciprocal Rank can capture this difference.

For a relevant result at position r:

MRR Contribution = 1 / r

The average across queries gives the MRR.

Metadata Filtering

Enterprise retrieval frequently requires filtering.

For example:

Tenant = Contoso
Department = Finance
Region = EU
Classification = Internal

Filtering can affect latency because the retrieval engine has additional constraints to evaluate.

Therefore, benchmark at least two categories:

Unfiltered Retrieval
Filtered Retrieval

The difference can reveal whether metadata filtering creates a significant performance cost.

Corpus Size Scaling

Run the same query set across increasing corpus sizes.

For example:

10K documents
100K documents
1M documents

Then compare:

Corpus Size vs p95 Latency

The objective is not to assume a specific scaling curve.

It is to observe how each architecture behaves as the workload grows.

Query Length Scaling

Query size can also affect performance.

Test:

Short:
"authentication timeout"

Medium:
"How is authentication timeout configured?"

Long:
"Explain how authentication timeout is configured
for services deployed behind the enterprise gateway."

This helps determine whether query complexity affects retrieval latency or quality.

Network Latency

Managed knowledge services and external vector stores can introduce network overhead.

The benchmark environment should therefore record:

Application Region
Service Region
Network Path
Protocol
Connection Reuse

If the application and retrieval service are deployed in different regions, the measured latency includes that network distance.

That is valid if it reflects production architecture, but it should be documented.

Connection Reuse

HTTP connection management can influence latency.

A benchmark that creates a new client for every request can measure connection setup rather than the actual retrieval system.

Prefer a long-lived client:

public sealed class RetrievalClient
{
    private readonly HttpClient _httpClient;

    public RetrievalClient(HttpClient httpClient)
    {
        _httpClient = httpClient;
    }
}

Use the application's normal HTTP-client lifecycle when benchmarking an HTTP-based vector store.

Retry Effects

Retries can distort latency.

Suppose a request takes:

Primary attempt = 200 ms
Retry = 500 ms

The final request latency may become:

700+ ms

The benchmark should record:

Attempt Count
Final Latency
Failure Reason

Do not hide retries inside an aggregate latency number.

Timeout Testing

A benchmark should also determine how systems behave near timeout boundaries.

For example:

using var timeout =
    new CancellationTokenSource(
        TimeSpan.FromSeconds(2));

await retriever.SearchAsync(
    query,
    options,
    timeout.Token);

Record:

Timeout Rate
Error Rate
p95 Latency

A system with excellent average latency but frequent timeout behavior under load may not be suitable for an interactive workload.

Benchmark Harness Design

A useful benchmark result model is:

public sealed record BenchmarkResult(
    string Retriever,
    string QueryId,
    TimeSpan Latency,
    int ResultCount,
    bool ContainsExpectedResult,
    int AttemptCount,
    bool Success);

After execution, calculate:

p50
p95
p99
Recall@K
Error Rate
Average Result Count

Keep the raw per-request records rather than storing only aggregate numbers.

Raw measurements make later analysis possible.

Comparing the Results

A final report might look like:

The values should come from the benchmark environment.

Avoid statements such as "System A is 40% faster" unless the benchmark actually produced that result under a clearly defined workload.

Interpreting Benchmark Results

Suppose the results show:

System A:
Lower p50
Higher p95
Higher Recall

System B:
Higher p50
Lower p95
Lower Recall

There is no single winner.

The correct choice depends on workload requirements.

For interactive applications, p95 may matter more than p50.

For high-volume batch processing, throughput and cost may matter more.

For enterprise knowledge systems, retrieval quality may outweigh a small latency difference.

Cost Per Retrieval

Latency should also be considered alongside operational cost.

Capture:

Infrastructure Cost
Embedding Cost
Query Cost
Network Cost
Operational Complexity

For managed services, pricing models may differ significantly from self-managed vector stores.

The benchmark should therefore include a cost model rather than assuming that the fastest system is the cheapest system.

Common Benchmarking Mistakes

Using Different Datasets

If the systems receive different documents, the comparison is invalid.

Measuring Only Average Latency

Average values hide tail behavior.

Ignoring Warm-Up

Cold-start behavior can distort steady-state measurements.

Running Only One Query

One query cannot represent an enterprise workload.

Ignoring Retrieval Quality

Fast incorrect results are not useful.

Mixing LLM Latency With Retrieval Latency

If the goal is to compare retrieval systems, measure retrieval independently.

Hiding Retries

Retries can significantly affect observed latency.

Using Production Traffic Without Controls

Production benchmarks can affect users and introduce uncontrolled variables.

Changing Multiple Variables at Once

If chunking, embeddings, Top-K, and retrieval infrastructure all change simultaneously, it becomes difficult to identify the cause of performance differences.

Best Practices

1. Define the exact benchmark boundary before testing.

2. Use the same document corpus across systems.

3. Use the same evaluation queries.

4. Measure p50, p95, and p99 latency.

5. Test both cold and warm behavior.

6. Test controlled concurrency levels.

7. Measure throughput and error rates.

8. Evaluate Recall@K and MRR alongside latency.

9. Test metadata-filtered retrieval separately.

10. Record retries and timeout behavior.

11. Keep the network topology consistent or document differences.

12. Reuse clients and connections as production code would.

13. Preserve raw benchmark measurements.

14. Test multiple corpus sizes.

15. Include cost and operational complexity in the final decision.

16. Repeat benchmarks after significant architecture or configuration changes.

Frequently Asked Questions

Is Fabric IQ faster than a vector database?

There is no universal answer. Retrieval latency depends on corpus size, configuration, network topology, filtering, query characteristics, concurrency, and the specific vector-store implementation.

Should I benchmark retrieval separately from the LLM?

Yes. If the objective is to compare retrieval technologies, isolate retrieval latency from LLM generation latency. End-to-end latency can then be measured separately.

Is p95 more important than average latency?

For many interactive applications, p95 is more useful because it exposes the slower requests that affect a meaningful portion of users. Average latency should still be reported.

How many queries should a benchmark contain?

There is no universal minimum. The evaluation set should be large and diverse enough to represent the production workload and cover different query types.

Should I benchmark filtered retrieval?

Yes. Enterprise applications frequently apply tenant, role, department, region, or classification filters. These conditions can change both performance and retrieval quality.

Can a vector store be faster simply because it is closer to the application?

Yes. Network distance can significantly affect observed latency. The benchmark should use deployment topology representative of the intended production architecture.

What is the most important retrieval metric?

There is no single metric. A useful evaluation combines latency, tail latency, throughput, reliability, and retrieval quality such as Recall@K and MRR.

Conclusion

Benchmarking AI retrieval systems requires more discipline than measuring how quickly a single search request returns.

A meaningful comparison between Fabric IQ and vector-store-based architectures should use the same corpus, equivalent queries, controlled retrieval parameters, consistent network conditions, and clearly defined measurements. It should separately evaluate cold and warm behavior, sequential and concurrent workloads, filtered retrieval, corpus scaling, and failure conditions.

Most importantly, latency must be evaluated together with retrieval quality. A system that returns results quickly but consistently misses the correct information is not necessarily better for an enterprise RAG workload.

The best benchmark therefore produces a multidimensional view:

Latency
   +
Throughput
   +
Reliability
   +
Retrieval Quality
   +
Cost
   +
Operational Complexity

That approach turns a technology comparison into an engineering decision based on measurable workload characteristics rather than assumptions about which retrieval architecture should be faster.