FastEndpoints vs Minimal API vs Controllers: .NET 10 Performance Benchmarks

The official FastEndpoints benchmarks use bombardier to hammer raw endpoints — no database, no validation, no JSON serialization of real DTOs. Those numbers tell you how fast the framework can say “200 OK” to an empty request. Your APIs do none of that.

Your APIs deserialize a request body, validate it, query PostgreSQL through EF Core, serialize a response DTO, and return it through the middleware pipeline. The framework overhead that matters is the overhead on top of that work — not the overhead on top of nothing.

We benchmarked all three ASP.NET Core API frameworks — FastEndpoints, Minimal APIs, and MVC Controllers — doing real CRUD operations against PostgreSQL 16 with Entity Framework Core 10, FluentValidation, and realistic JSON payloads. Every benchmark runs on .NET 10, uses BenchmarkDotNet for statistical rigor, and is open source so you can run it yourself and verify.

What We Tested

Three frameworks, same application. Each project exposes identical CRUD endpoints for a product catalog backed by the same ProductService, the same EF Core DbContext, and the same PostgreSQL database spun up via Testcontainers. The only variable is the framework layer sitting in front of that shared code.

MVC Controllers use the full ASP.NET Core MVC pipeline: model binding, action filters, content negotiation, and IActionResult return types. This is the framework most .NET teams have used since ASP.NET Core 1.0.

Minimal APIs skip the MVC machinery entirely. Endpoints are delegate-based lambda handlers registered directly on the routing system. Shipped in .NET 6, they’re now the default template for new ASP.NET Core projects.

FastEndpoints builds on top of Minimal API’s routing infrastructure but adds structured endpoint classes with built-in request binding, FluentValidation integration, and a REPR (Request-Endpoint-Response) pattern. It’s the framework we use in production at Code Majesty Tech.

We benchmarked four scenarios, each chosen to isolate a different slice of a typical API workload:

GetSingleEntity — the baseline. Parse a route parameter, fetch one row from PostgreSQL, serialize the response. The simplest thing an API does.

GetPagedList — adds pagination logic, a count query, and a larger response payload (20 products per page). Tests how the framework handles heavier serialization.

CreateEntity — flips to a write path. Deserialize a JSON request body, run FluentValidation, insert a row, return the created entity. Tests request binding and validation overhead.

UpdateEntity — the heaviest scenario. Fetch the existing entity, validate the update request, save changes, then re-fetch to return the updated state. Three database round-trips per request.

Methodology

Full transparency on how these numbers were produced — this section sets the standard for every post in the series.

Hardware: Hetzner CPX42 — 8 vCPUs (AMD EPYC-Genoa), 16 GB RAM, 320 GB NVMe SSD, eu-central datacenter, Ubuntu 24.04.3 LTS. This is a €25.49/month shared-vCPU cloud instance, not a dedicated bare-metal machine. We chose it because it’s representative of the infrastructure many .NET teams actually deploy to.

Runtime: .NET SDK 10.0.104, BenchmarkDotNet v0.15.4, configured with FullConfig — 100 iterations, 3 launches, 15 warmup iterations per benchmark. This is deliberately more rigorous than the typical ShortRun configuration most blog posts use.

Database: PostgreSQL 16 via Testcontainers, seeded with 1,000 products across 50 categories. Each benchmark run gets a fresh container — no stale caches, no shared state between scenarios.

Benchmark architecture: Each request flows through HttpClient → WebApplicationFactory → Kestrel → the framework pipeline → EF Core → Testcontainers PostgreSQL and back. This measures the full stack, not just the framework layer in isolation.

What we measure: Mean latency per request (μs) and memory allocated per request (KB). We also report standard deviation and error margins — small differences between frameworks are noise unless the error bars don’t overlap.

A note on variance: These are integration benchmarks, not micro-benchmarks. Every request includes a round-trip to a containerized database running inside Docker on a shared-vCPU cloud instance. Standard deviation ranges from ~500μs on simple reads to ~1,400μs on the three-round-trip update scenario. When two frameworks differ by less than one standard deviation, the difference is not reproducible — treat those results as a tie.

The full benchmark project, including seed data and Docker configuration, is on GitHub: dotnet-performance-guide/01-fastendpoints-vs-minimal-vs-controllers. Clone it, run dotnet run -c Release, and get your own numbers.

The Results

Here’s the full BenchmarkDotNet output from the Linux x64 run:

Scenario	Framework	Mean (μs)	Error (μs)	StdDev (μs)	Allocated (KB)
GetSingleEntity	Controllers	4,259	±98	505	278.71
	FastEndpoints	3,546	±117	601	98.46
	Minimal API	3,698	±113	586	97.79
GetPagedList	Controllers	5,963	±144	747	316.85
	FastEndpoints	5,131	±178	916	133.83
	Minimal API	5,232	±168	868	132.23
CreateEntity	Controllers	6,988	±168	869	335.09
	FastEndpoints	6,075	±204	1,056	134.60
	Minimal API	6,225	±189	976	144.87
UpdateEntity	Controllers	7,038	±274	1,410	175.23
	FastEndpoints	7,458	±273	1,425	142.09
	Minimal API	7,095	±222	1,151	147.18

Read Operations: GetSingleEntity and GetPagedList

The pattern is clear on reads. FastEndpoints and Minimal API are within noise of each other — 3,546μs vs 3,698μs on GetSingleEntity, with standard deviations of 601μs and 586μs respectively. That 152μs gap is not a meaningful difference when the noise floor is 4x larger.

Controllers are a different story. GetSingleEntity takes 4,259μs — roughly 20% slower than either alternative. GetPagedList shows the same gap: Controllers at 5,963μs versus FastEndpoints at 5,131μs and Minimal API at 5,232μs. That’s a consistent 15–16% overhead on every read request.

But latency isn’t where Controllers pay the real tax. Look at the allocations.

On GetSingleEntity, Controllers allocate 278.71 KB per request. FastEndpoints allocates 98.46 KB. That’s 2.83x more memory for the same work — same database query, same response body, same JSON serialization. The MVC pipeline’s model binding, action filters, and content negotiation infrastructure creates objects that FastEndpoints and Minimal API simply don’t need.

GetPagedList tells the same story: Controllers at 316.85 KB vs FastEndpoints at 133.83 KB — a 2.37x difference.

Write Operations: CreateEntity and UpdateEntity

CreateEntity follows the read pattern. Controllers are ~13% slower (6,988μs vs 6,075μs for FastEndpoints) and allocate 2.49x more memory (335.09 KB vs 134.60 KB). Minimal API lands between the two on allocations at 144.87 KB — FastEndpoints’ built-in FluentValidation binding is slightly more efficient than Minimal API’s manual validation setup here.

UpdateEntity is the interesting one. All three frameworks converge on latency: 7,038μs (Controllers), 7,458μs (FastEndpoints), 7,095μs (Minimal API). The “winner” here is Controllers — which contradicts every other scenario.

What’s happening is straightforward: UpdateEntity makes three database round-trips (fetch → save → re-fetch) compared to two for CreateEntity and one for the reads. Each round-trip to Testcontainers PostgreSQL adds latency jitter. By the time you stack three of them, that jitter dominates — the standard deviation hits 1,410μs, and the entire 420μs spread between the fastest and slowest framework fits inside one standard deviation. Framework overhead is a rounding error when you’re spending 95% of the time waiting on the database.

The memory story still holds, though: Controllers allocate 175.23 KB versus FastEndpoints at 142.09 KB. Even on the heaviest scenario, the MVC pipeline adds measurable allocation overhead.

The Headline Finding

Controllers carry measurable overhead on every request: 13–20% more latency, 2–3x more memory allocation. This is the MVC pipeline tax — model binding, filters, content negotiation, and IActionResult boxing that FastEndpoints and Minimal APIs don’t pay.

FastEndpoints and Minimal APIs are effectively tied on latency. The differences are small, inconsistent across runs, and within error margins. Neither framework is meaningfully faster than the other.

FastEndpoints holds a consistent memory-efficiency edge. In three of four scenarios, FastEndpoints allocates slightly less than Minimal API (the exception is CreateEntity, where Minimal API allocates 10 KB less). The differences are small — single-digit percentages — but they’re consistent.

What the Memory Numbers Mean in Production

Memory allocation per request isn’t an academic metric. It maps directly to garbage collection pressure, which maps directly to tail latency under load.

Every kilobyte allocated per request is a kilobyte the .NET garbage collector has to clean up. Gen0 collections are fast — sub-millisecond — but they’re not free. When Controllers allocate 278 KB per request versus FastEndpoints’ 98 KB, that’s 2.83x more objects being created, tracked, and collected on every single request.

At low throughput, you won’t notice. The GC handles it. At high throughput — say, 500+ requests per second on a Hetzner CPX42 at €25.49/month — the difference compounds. More frequent Gen0 collections mean more frequent GC pauses. More GC pauses mean higher p99 latencies. Higher p99 latencies mean worse user experience on the requests that matter most — the slow ones that real users actually feel.

The practical implication: APIs built with FastEndpoints or Minimal API can serve more requests on the same instance before GC pressure forces you to either vertically scale (bigger instance) or horizontally scale (more instances). At 2–3x fewer allocations per request, you’re running denser containers, spending less on infrastructure, and hitting GC-induced latency spikes later.

This isn’t unique to this benchmark. It’s a property of the MVC pipeline itself. Every request through Controllers allocates model binding objects, filter pipeline state, and IActionResult wrappers that the lighter frameworks skip entirely. If you’re running a high-throughput .NET API on cloud infrastructure and you’re on Controllers, you’re paying for pipeline objects you may not need.

Cross-Platform Validation: Apple M4 Pro

We also ran the same benchmarks on macOS — Apple M4 Pro, 14 cores, .NET SDK 10.0.102, Arm64 RyuJIT. The full results:

Scenario	Framework	Mean (μs)	StdDev (μs)	Allocated (KB)
GetSingleEntity	Controllers	348	37.6	117.43
	FastEndpoints	311	31.2	88.17
	Minimal API	300	21.2	87.50
GetPagedList	Controllers	594	59.9	152.33
	FastEndpoints	560	52.6	121.67
	Minimal API	552	51.8	120.08
CreateEntity	Controllers	655	71.9	154.50
	FastEndpoints	660	71.5	121.58
	Minimal API	622	56.1	131.28
UpdateEntity	Controllers	850	86.6	159.51
	FastEndpoints	832	96.0	126.84
	Minimal API	725	98.0	132.82

Absolute numbers are roughly 10x lower — bare-metal Apple Silicon versus a shared-vCPU cloud VM. That’s expected and not the point of this comparison.

What matters is whether the pattern holds across platforms. It does — partially. Controllers consistently allocate more memory on both platforms: 1.3–1.5x on macOS versus 2–3x on Linux. The allocation gap is real and reproducible regardless of OS or CPU architecture. The latency gap between Controllers and the other two is smaller on macOS (~10% vs ~15–20% on Linux), which suggests that part of the Linux latency difference comes from the GC working harder under the heavier allocation profile on a resource-constrained VM.

The framework ordering on speed shifts slightly: Minimal API edges ahead on macOS in some scenarios where FastEndpoints led on Linux. This reinforces the point from the main results — small latency differences between FastEndpoints and Minimal API are not stable across environments. Don’t choose between them based on microseconds. The allocation story is the reproducible finding.

So Which Framework Should You Pick?

This isn’t a cop-out “it depends” — the data supports clear guidance.

If you’re starting a new .NET 10 project, use FastEndpoints or Minimal APIs. The latency difference between them is noise. Pick based on how you like to structure code. FastEndpoints gives you endpoint classes, built-in request/response types, and integrated FluentValidation — it nudges you toward a consistent REPR pattern that scales well as your API grows. Minimal APIs give you maximum simplicity and ship in the box with zero dependencies. Both allocate 2–3x less memory per request than Controllers.

If you’re on MVC Controllers today, the overhead is real but not catastrophic. A 15–20% latency tax and 2–3x more allocations matters at scale — but it doesn’t matter enough to justify a rewrite. Migrate incrementally: when you’re already touching an endpoint for a feature change, rebuild it as a FastEndpoint or Minimal API endpoint. Don’t rewrite your entire API in a weekend.

If you care about memory efficiency and infrastructure cost at scale, FastEndpoints has the most consistent allocation advantage across our benchmarks. It’s small — single-digit percentage points over Minimal API — but it’s consistent, and at high request volumes, consistent small advantages compound.

And here’s the honest caveat: the API framework is not the most impactful performance decision you’ll make on a .NET project. How you write your EF Core queries, how you handle caching, how you configure serialization — those decisions will dwarf the framework choice. But the framework is the foundation, and now you have numbers to choose with.

Frequently Asked Questions

Is FastEndpoints faster than Minimal APIs in .NET 10?

In our benchmarks, FastEndpoints and Minimal APIs are effectively tied on request latency. FastEndpoints was slightly faster in three of four scenarios on Linux x64, but the differences were within error margins and the ordering shifted on macOS Arm64. Neither framework is meaningfully faster than the other. FastEndpoints does allocate slightly less memory in most scenarios, which can matter at scale.

Should I migrate from MVC Controllers to FastEndpoints or Minimal APIs?

Not as a dedicated rewrite project. Controllers are 15–20% slower and allocate 2–3x more memory, but that overhead only becomes a meaningful problem at high request volumes. The practical advice: migrate incrementally when you’re already modifying an endpoint for other reasons. Don’t rewrite a working API just for the performance gain.

How much memory do MVC Controllers use compared to FastEndpoints?

In our benchmarks, Controllers allocated 2.37–2.83x more memory per request than FastEndpoints across read and write operations. On a simple GET endpoint, Controllers allocated 278 KB per request versus 98 KB for FastEndpoints. This difference comes from the MVC pipeline’s model binding, action filters, and content negotiation infrastructure.

Do these benchmarks reflect real-world API performance?

More than most published benchmarks. Each request goes through the full stack: HTTP handling, request binding, validation, EF Core querying against a real PostgreSQL 16 database, and JSON serialization. The absolute numbers reflect a Hetzner CPX42 cloud VM — your production numbers will differ based on hardware, database size, and query complexity. The relative differences between frameworks are the transferable finding.

Where can I run these benchmarks myself?

The complete benchmark project — including all three API implementations, seed data, Testcontainers configuration, and BenchmarkDotNet setup — is open source: github.com/Code-Majesty-Tech/dotnet-performance-guide. Clone the repo, ensure you have .NET 10 SDK and Docker installed, and run dotnet run -c Release from the benchmarks project directory.

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