For 18 months, our API gateway’s p99 reverse proxy latency hovered at 210ms, costing us $18k/month in wasted compute and driving a 12% churn rate among high-frequency API consumers. We replaced Apache 2.4.57 with Nginx 1.25.3, and cut that latency by 25% to 157ms, with zero unplanned downtime. Here’s exactly how we did it, with benchmarks, code, and real production numbers.
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Key Insights
- Nginx 1.25’s io_uring async event loop reduces context switching by 40% vs Apache’s prefork MPM in high-concurrency API workloads
- Nginx 1.25.3 with HTTP/3 enabled outperforms Apache 2.4.57 by 22% in p99 latency for JSON API responses under 10kB
- Migration eliminated $18k/month in overprovisioned Apache worker node costs, with 6-month ROI on engineering time
- 70% of new API gateway deployments will default to Nginx 1.25+ or compatible event-driven proxies by 2026, per Gartner
Why Apache Fell Short for Our API Workload
We ran Apache 2.4.57 with the default prefork MPM for 3 years, starting when our API had fewer than 100 daily active consumers. The prefork MPM spawns a dedicated child process for each incoming connection, which worked well for low-concurrency workloads but became a bottleneck as we scaled to 12k daily active API consumers sending 40M requests per day. Each Apache worker process consumed ~25MB of RAM, so 150 MaxRequestWorkers (the default for prefork) used 3.75GB of RAM at peak, leaving little headroom for our Node.js API services on the same EC2 instances. Context switching between 150 worker processes added 12,400 context switches per second at peak, which our profiling showed accounted for 30% of p99 latency. We tried switching to Apache’s event MPM, but it had compatibility issues with our mod_proxy and mod_rewrite rules, and only reduced p99 latency by 8%, far less than we needed.
Original Apache 2.4 Reverse Proxy Configuration
Below is the full Apache virtual host configuration we used for 18 months, serving all 127 API endpoints. It includes SSL termination, load balancing, rate limiting, and error handling.
# Apache 2.4.57 API Reverse Proxy Configuration\n# Deployed on AWS EC2 m5.large instances (2 vCPU, 8GB RAM)\n# Last updated: 2024-03-01\n\n\n ServerName api.example.com\n DocumentRoot /var/www/html\n\n # SSL Configuration (TLS 1.2+ enforced)\n SSLEngine on\n SSLCertificateFile /etc/ssl/certs/api.example.com.crt\n SSLCertificateKeyFile /etc/ssl/private/api.example.com.key\n SSLProtocol all -SSLv3 -TLSv1 -TLSv1.1\n SSLCipherSuite HIGH:!aNULL:!MD5:!3DES\n\n # Apache Prefork MPM Configuration (default for Apache 2.4)\n \n StartServers 5\n MinSpareServers 5\n MaxSpareServers 10\n MaxRequestWorkers 150\n MaxConnectionsPerChild 0\n \n\n # mod_proxy reverse proxy settings\n \n ProxyRequests Off\n ProxyPreserveHost On\n ProxyVia Off\n\n # API endpoint routing\n \n BalancerMember http://node1.internal:3000 retry=5\n BalancerMember http://node2.internal:3000 retry=5\n BalancerMember http://node3.internal:3000 retry=5\n ProxySet lbmethod=byrequests\n \n\n \n ProxyPass balancer://api_cluster/v1\n ProxyPassReverse balancer://api_cluster/v1\n # Rate limiting: 1000 requests per minute per IP\n \n SetOutputFilter RATE_LIMIT\n SetEnv rate-limit 1000\n SetEnv rate-limit-period 60\n \n \n\n \n ProxyPass http://node4.internal:3000/v2\n ProxyPassReverse http://node4.internal:3000/v2\n \n \n\n # Error handling\n ErrorDocument 500 /error/500.html\n ErrorDocument 502 /error/502.html\n ErrorDocument 503 /error/503.html\n ErrorDocument 504 /error/504.html\n\n # Logging: Combined log format with response time\n LogFormat \"%h %l %u %t \\\"%r\\\" %>s %b \\\"%{Referer}i\\\" \\\"%{User-Agent}i\\\" %D\" proxy_combined\n CustomLog /var/log/apache2/api_access.log proxy_combined\n ErrorLog /var/log/apache2/api_error.log\n\n # Security headers\n Header always set X-Content-Type-Options \"nosniff\"\n Header always set X-Frame-Options \"DENY\"\n Header always set Content-Security-Policy \"default-src 'self'\"\n\n\n# HTTP to HTTPS redirect\n\n ServerName api.example.com\n Redirect permanent / https://api.example.com/\n\n
Benchmarking Methodology
We used identical hardware for all benchmarks: AWS EC2 m5.large instances (2 vCPU, 8GB RAM) with Linux 5.15 kernels. We generated load using wrk2 with 10k concurrent connections, 1M total requests, and a target rate of 1000 RPS to avoid coordinated omission. We tested the /v1/health endpoint, which returns a 1.2kB JSON response, to isolate proxy latency from upstream API logic. All benchmarks were run 3 times, and we report the median of the 3 runs.
Apache vs Nginx 1.25 Performance Comparison
Below is the full comparison of Apache 2.4.57 and Nginx 1.25.3 across all key performance metrics. All numbers are from production benchmarks with 10k concurrent connections.
Metric
Apache 2.4.57 (Prefork MPM)
Nginx 1.25.3 (io_uring)
% Improvement
p50 Latency
89ms
62ms
30.3%
p90 Latency
140ms
98ms
30%
p99 Latency
210ms
157ms
25.2%
Requests per Second (RPS)
1240
1890
52.4%
Memory Usage (idle)
1.2GB
120MB
90% reduction
Memory Usage (peak load)
3.8GB
450MB
88.1% reduction
Context Switches per Second
12,400
7,440
40% reduction
CPU Usage (peak load)
78%
42%
46.2% reduction
TLS Handshake Time (p99)
45ms
28ms
37.8% reduction
Nginx 1.25 Replacement Configuration
Below is the Nginx 1.25.3 configuration that replaced the Apache config above. It includes io_uring event loop, HTTP/3 support, load balancing, rate limiting, and error handling. We compiled Nginx with --with-http_v3_module and --with-threads to enable all features.
# Nginx 1.25.3 API Reverse Proxy Configuration\n# Deployed on same AWS EC2 m5.large instances (2 vCPU, 8GB RAM)\n# Compiled with --with-http_v3_module --with-http_ssl_module --with-threads\n# Last updated: 2024-06-15\n\n# Enable io_uring async event loop (Nginx 1.25+ feature)\nevents {\n worker_connections 4096;\n use io_uring; # Requires Linux 5.1+ kernel\n multi_accept on;\n}\n\nhttp {\n # Basic settings\n sendfile on;\n tcp_nopush on;\n tcp_nodelay on;\n keepalive_timeout 65;\n types_hash_max_size 2048;\n server_tokens off;\n\n # SSL/TLS settings (TLS 1.3 preferred, 1.2 fallback)\n ssl_protocols TLSv1.2 TLSv1.3;\n ssl_certificate /etc/ssl/certs/api.example.com.crt;\n ssl_certificate_key /etc/ssl/private/api.example.com.key;\n ssl_ciphers HIGH:!aNULL:!MD5:!3DES:TLS_AES_256_GCM_SHA384:TLS_CHACHA20_POLY1305_SHA256;\n ssl_prefer_server_ciphers on;\n ssl_session_cache shared:SSL:10m;\n ssl_session_timeout 1d;\n\n # HTTP/3 (QUIC) settings (Nginx 1.25+ experimental)\n listen 443 quic reuseport;\n listen 443 ssl http2;\n add_header Alt-Svc 'h3=\":443\"; ma=86400' always;\n\n # Upstream API cluster\n upstream api_cluster {\n server node1.internal:3000 max_fails=3 fail_timeout=10s;\n server node2.internal:3000 max_fails=3 fail_timeout=10s;\n server node3.internal:3000 max_fails=3 fail_timeout=10s;\n server node4.internal:3000 max_fails=3 fail_timeout=10s;\n keepalive 32; # Reuse connections to upstream\n }\n\n # Rate limiting zone: 1000 requests per minute per IP\n limit_req_zone $binary_remote_addr zone=api_limit:10m rate=1000r/m;\n\n server {\n listen 80;\n server_name api.example.com;\n return 301 https://$host$request_uri; # HTTP to HTTPS redirect\n }\n\n server {\n listen 443 ssl quic;\n server_name api.example.com;\n\n # Security headers\n add_header X-Content-Type-Options \"nosniff\" always;\n add_header X-Frame-Options \"DENY\" always;\n add_header Content-Security-Policy \"default-src 'self'\" always;\n add_header Strict-Transport-Security \"max-age=63072000; includeSubDomains\" always;\n\n # Error handling\n error_page 500 502 503 504 /error/50x.html;\n location = /error/50x.html {\n root /var/www/html;\n internal;\n }\n\n # v1 API endpoint (load balanced)\n location /v1 {\n limit_req zone=api_limit burst=50 nodelay;\n proxy_pass http://api_cluster/v1;\n proxy_set_header Host $host;\n proxy_set_header X-Real-IP $remote_addr;\n proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;\n proxy_set_header X-Forwarded-Proto $scheme;\n # Proxy buffer settings to reduce latency\n proxy_buffering off;\n proxy_request_buffering off;\n proxy_http_version 1.1;\n proxy_set_header Connection \"\";\n }\n\n # v2 API endpoint (single upstream)\n location /v2 {\n proxy_pass http://node4.internal:3000/v2;\n proxy_set_header Host $host;\n proxy_set_header X-Real-IP $remote_addr;\n proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;\n proxy_set_header X-Forwarded-Proto $scheme;\n proxy_buffering off;\n proxy_request_buffering off;\n }\n\n # Logging with response time\n access_log /var/log/nginx/api_access.log combined buffer=32k flush=5m;\n error_log /var/log/nginx/api_error.log warn;\n }\n}\n
Benchmark Script for Validating Latency
We wrote the following Go benchmark script to validate latency improvements before and after migration. It supports constant-rate load generation, percentile calculation, and error handling. Compile with go build -o benchmark_proxy benchmark_proxy.go.
// benchmark_proxy.go: Benchmarks p50/p90/p99 latency for API reverse proxies\n// Compile: go build -o benchmark_proxy benchmark_proxy.go\n// Run: ./benchmark_proxy --target https://api.example.com --requests 100000 --concurrency 1000\npackage main\n\nimport (\n\t\"context\"\n\t\"crypto/tls\"\n\t\"flag\"\n\t\"fmt\"\n\t\"log\"\n\t\"math\"\n\t\"net/http\"\n\t\"os\"\n\t\"sort\"\n\t\"sync\"\n\t\"time\"\n)\n\n// Config holds benchmark parameters\ntype Config struct {\n\ttarget string\n\trequests int\n\tconcurrency int\n\ttimeout time.Duration\n}\n\nfunc main() {\n\t// Parse CLI flags\n\ttarget := flag.String(\"target\", \"https://api.example.com\", \"Target API URL\")\n\trequests := flag.Int(\"requests\", 100000, \"Total number of requests to send\")\n\tconcurrency := flag.Int(\"concurrency\", 1000, \"Number of concurrent workers\")\n\ttimeout := flag.Duration(\"timeout\", 10*time.Second, \"Request timeout per call\")\n\tflag.Parse()\n\n\tcfg := Config{\n\t\ttarget: *target,\n\t\trequests: *requests,\n\t\tconcurrency: *concurrency,\n\t\ttimeout: *timeout,\n\t}\n\n\t// Validate config\n\tif cfg.requests <= 0 || cfg.concurrency <= 0 {\n\t\tlog.Fatal(\"requests and concurrency must be positive integers\")\n\t}\n\tif cfg.concurrency > cfg.requests {\n\t\tlog.Fatal(\"concurrency cannot exceed total requests\")\n\t}\n\n\t// Create HTTP client with disabled keep-alives to simulate real client behavior\n\tclient := &http.Client{\n\t\tTimeout: cfg.timeout,\n\t\tTransport: &http.Transport{\n\t\t\tTLSClientConfig: &tls.Config{InsecureSkipVerify: true}, // For testing only\n\t\t\tMaxIdleConns: 0,\n\t\t\tDisableKeepAlives: true,\n\t\t},\n\t}\n\n\t// Channel to collect latency measurements\n\tlatencies := make(chan time.Duration, cfg.requests)\n\tvar wg sync.WaitGroup\n\n\t// Start concurrent workers\n\trequestsPerWorker := int(math.Ceil(float64(cfg.requests) / float64(cfg.concurrency)))\n\tremainingRequests := cfg.requests\n\n\tfor i := 0; i < cfg.concurrency; i++ {\n\t\twg.Add(1)\n\t\tworkerRequests := requestsPerWorker\n\t\tif remainingRequests < workerRequests {\n\t\t\tworkerRequests = remainingRequests\n\t\t}\n\t\tremainingRequests -= workerRequests\n\n\t\tgo func(numReqs int) {\n\t\t\tdefer wg.Done()\n\t\t\tfor j := 0; j < numReqs; j++ {\n\t\t\t\tstart := time.Now()\n\t\t\t\treq, err := http.NewRequestWithContext(context.Background(), \"GET\", cfg.target+\"/v1/health\", nil)\n\t\t\t\tif err != nil {\n\t\t\t\t\tlog.Printf(\"Failed to create request: %v\", err)\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\t\t\t\tresp, err := client.Do(req)\n\t\t\t\tlatency := time.Since(start)\n\t\t\t\tif err != nil {\n\t\t\t\t\tlog.Printf(\"Request failed: %v\", err)\n\t\t\t\t\tcontinue\n\t\t\t\t}\n\t\t\t\tresp.Body.Close()\n\t\t\t\tlatencies <- latency\n\t\t\t}\n\t\t}(workerRequests)\n\t}\n\n\t// Close latencies channel when all workers finish\n\tgo func() {\n\t\twg.Wait()\n\t\tclose(latencies)\n\t}()\n\n\t// Collect and sort latencies\n\tvar latencyList []time.Duration\n\tfor lat := range latencies {\n\t\tlatencyList = append(latencyList, lat)\n\t}\n\n\tif len(latencyList) == 0 {\n\t\tlog.Fatal(\"No successful requests recorded\")\n\t}\n\n\tsort.Slice(latencyList, func(i, j int) bool {\n\t\treturn latencyList[i] < latencyList[j]\n\t})\n\n\t// Calculate percentiles\n\tp50 := percentile(latencyList, 50)\n\tp90 := percentile(latencyList, 90)\n\tp99 := percentile(latencyList, 99)\n\tavg := average(latencyList)\n\n\t// Print results\n\tfmt.Printf(\"Benchmark Results for %s\\n\", cfg.target)\n\tfmt.Printf(\"Total Requests: %d\\n\", len(latencyList))\n\tfmt.Printf(\"Concurrency: %d\\n\", cfg.concurrency)\n\tfmt.Printf(\"Average Latency: %v\\n\", avg)\n\tfmt.Printf(\"p50 Latency: %v\\n\", p50)\n\tfmt.Printf(\"p90 Latency: %v\\n\", p90)\n\tfmt.Printf(\"p99 Latency: %v\\n\", p99)\n}\n\n// percentile calculates the Nth percentile of a sorted slice\nfunc percentile(sorted []time.Duration, p int) time.Duration {\n\tif len(sorted) == 0 {\n\t\treturn 0\n\t}\n\tindex := int(math.Ceil(float64(p)/100*float64(len(sorted)))) - 1\n\tif index < 0 {\n\t\tindex = 0\n\t}\n\tif index >= len(sorted) {\n\t\tindex = len(sorted) - 1\n\t}\n\treturn sorted[index]\n}\n\n// average calculates the average of a slice of durations\nfunc average(latencies []time.Duration) time.Duration {\n\tif len(latencies) == 0 {\n\t\treturn 0\n\t}\n\tvar total time.Duration\n\tfor _, lat := range latencies {\n\t\ttotal += lat\n\t}\n\treturn total / time.Duration(len(latencies))\n}\n
Case Study: Fintech API Gateway Migration
- Team size: 4 backend engineers, 1 site reliability engineer (SRE)
- Stack & Versions: Apache 2.4.57, Node.js 18.19.0 API services, PostgreSQL 15.4, Redis 7.2.4, AWS Application Load Balancer (ALB), Nginx 1.25.3
- Problem: p99 reverse proxy latency was 210ms for /v1/payment endpoints, driving 12% monthly churn among high-frequency trading API consumers; overprovisioned Apache worker nodes cost $18k/month in unused AWS EC2 capacity
- Solution & Implementation: Migrated all 127 API endpoints from Apache to Nginx 1.25.3 over 6 weeks using a canary deployment strategy: (1) Deployed Nginx alongside Apache on 10% of worker nodes, (2) Routed 5% of traffic to Nginx via ALB weighted routing, (3) Validated p99 latency, error rates, and upstream compatibility for 14 days, (4) Incremented Nginx traffic to 100% over 7 days, (5) Decommissioned Apache nodes
- Outcome: p99 latency dropped to 157ms (25.2% reduction), error rates fell from 0.8% to 0.12%, API churn dropped to 4% monthly, saving $18k/month in EC2 costs, with zero unplanned downtime during migration
Developer Tips for Migrating to Nginx 1.25
1. Enable io_uring Async Event Loop in Nginx 1.25 for High-Concurrency Workloads
Nginx 1.25 marked a major shift in event loop architecture with official support for io_uring, the Linux kernel’s high-performance async I/O interface. For API reverse proxy workloads with 10k+ concurrent connections, io_uring eliminates the overhead of traditional epoll-based event notification, reducing context switches by up to 40% compared to Nginx’s default event loop. This is a critical advantage over Apache’s prefork MPM, which spawns a dedicated process per connection, leading to massive memory bloat and context switching overhead at scale. During our migration, we found that enabling io_uring alone cut p99 latency by 12% before we made any other proxy optimizations. To use io_uring, you need a Linux 5.1+ kernel (available in Ubuntu 20.04 LTS, RHEL 8, Debian 11, and newer distributions) and Nginx 1.25+ compiled with thread support (or use official Nginx repositories which include io_uring support by default). Add the following to your nginx.conf events block to enable it:
events {\n worker_connections 4096;\n use io_uring; # Requires Linux 5.1+ kernel\n multi_accept on;\n}
Note that io_uring is not supported on Windows or macOS, so this optimization only applies to Linux-based deployments. We also recommend setting worker_processes to auto to match the number of available CPU cores, which maximizes io_uring’s efficiency.
2. Map Apache mod_proxy Directives to Nginx ngx_http_proxy_module Equivalents
Apache’s mod_proxy and Nginx’s ngx_http_proxy_module have overlapping but not identical functionality, and misconfigured migrations are a leading cause of post-migration latency spikes. Common pitfalls include forgetting to disable proxy buffering (which adds latency for streaming APIs), mismatched load balancing algorithms, and incorrect X-Forwarded-For header handling. Use the open-source nginx-proxy/mapper tool to automatically convert Apache mod_proxy rules to Nginx syntax, but always validate the output manually. For example, Apache’s ProxyPass /v1 balancer://cluster/ maps to Nginx’s proxy_pass http://cluster/v1;, but Apache’s BalancerMember retry=5 maps to Nginx’s server max_fails=3 fail_timeout=10s; (Nginx uses different retry logic). We also recommend auditing all mod_rewrite rules, as Nginx’s rewrite syntax is less permissive than Apache’s. Tool: nginx-proxy/mapper (https://github.com/nginx-proxy/mapper), Nginx rewrite documentation.
# Apache mod_proxy\n\n BalancerMember http://node1:3000 retry=5\n ProxySet lbmethod=byrequests\n\nProxyPass /v1 balancer://api_cluster/v1\n\n# Nginx equivalent\nupstream api_cluster {\n server node1:3000 max_fails=3 fail_timeout=10s;\n lbmethod=least_conn; # Nginx 1.25+ supports least_conn, ip_hash, etc.\n}\nlocation /v1 {\n proxy_pass http://api_cluster/v1;\n}
3. Benchmark with wrk2 and OpenTelemetry Distributed Tracing
Before and after migration, you need statistically significant benchmarks to validate latency improvements, not just anecdotal evidence. Use wrk2 instead of wrk for latency benchmarking, as it supports constant throughput load generation, which avoids the coordinated omission problem that skews p99 latency numbers. We used wrk2 to generate 10k concurrent connections for 10 minutes, measuring p50/p90/p99 latency, RPS, and error rates. Complement benchmarks with OpenTelemetry distributed tracing to isolate latency to the reverse proxy layer, not upstream API services. Tag spans with proxy.type: apache or proxy.type: nginx to compare proxy-specific latency in tools like Jaeger or Honeycomb. We found that 30% of our initial "Nginx latency" was actually upstream API slowdown, which we isolated using OTel traces. Tool: wrk2 (https://github.com/giltene/wrk2), OpenTelemetry (https://opentelemetry.io/).
# wrk2 benchmark command: 10k connections, 100k requests, 1000 RPS target\nwrk -t2 -c10000 -d600s -R1000 --latency https://api.example.com/v1/health\n\n# OpenTelemetry Nginx config snippet to trace proxy requests\nopentelemetry on;\nopentelemetry_trace_context on;\nlocation /v1 {\n opentelemetry_operation_name \"proxy_v1\";\n proxy_pass http://api_cluster/v1;\n}
Join the Discussion
We’ve shared our benchmarks, code, and production results from migrating 127 API endpoints from Apache to Nginx 1.25. We’d love to hear from other engineering teams who have made similar migrations, or those considering Nginx 1.25 for their API workloads.
Discussion Questions
- With Nginx 1.25’s experimental QUIC/HTTP/3 implementation stabilizing, will HTTP/3 become the default for API gateways by 2025?
- Is the 25% latency gain worth the engineering effort required to migrate legacy Apache mod_rewrite rules to Nginx syntax for teams with <5 engineers?
- How does Nginx 1.25 compare to Caddy 2.7 for API reverse proxy workloads in high-concurrency (10k+ connections) scenarios?
Frequently Asked Questions
Does Nginx 1.25’s io_uring support work on all Linux distributions?
No, Nginx 1.25’s io_uring event loop requires a Linux 5.1 or newer kernel, as io_uring was introduced in Linux 5.1. This means it works on Ubuntu 20.04 LTS (kernel 5.4), RHEL 8 (kernel 4.18, but RHEL 8.2+ backported io_uring), Debian 11 (kernel 5.10), and AWS Linux 2 (kernel 5.10+ via extras). It does not work on older distributions like Ubuntu 18.04 (kernel 4.15) or RHEL 7 (kernel 3.10). If your distribution does not support io_uring, Nginx will fall back to the default epoll event loop, which still outperforms Apache’s prefork MPM but does not deliver the full 25% latency reduction we saw.
How do we migrate legacy Apache .htaccess rules to Nginx?
Nginx does not support .htaccess files, as it parses configuration once at startup for better performance, unlike Apache which parses .htaccess per request. To migrate, you need to convert .htaccess rules to Nginx server or location blocks. Use the open-source stackbuilders/htaccess-to-nginx converter to automate most rules, but always manually validate the output, as complex mod_rewrite conditions (like %{HTTP_USER_AGENT} matching) may not convert correctly. For example, Apache’s RewriteRule ^/v1/(.*)$ /v1/$1 [L] converts to Nginx’s location ~ ^/v1/(.*)$ { try_files /v1/$1 =404; }. We recommend disabling .htaccess support in Apache before migration to ensure feature parity.
Is HTTP/3 enabled by default in Nginx 1.25?
No, HTTP/3 (QUIC) support in Nginx 1.25 is experimental and must be explicitly enabled. You need to compile Nginx with the --with-http_v3_module flag, or use prebuilt packages from Nginx Plus or the official Nginx mainline repository (which include HTTP/3 support in 1.25+). To enable HTTP/3, add listen 443 quic reuseport; to your server block, and add the Alt-Svc header to advertise HTTP/3 support to clients: add_header Alt-Svc 'h3=\":443\"; ma=86400' always;. Note that HTTP/3 is not supported in all clients (older browsers, legacy API SDKs), so we recommend keeping HTTP/2 enabled as a fallback, which we did in our configuration.
Conclusion & Call to Action
After 6 weeks of migration and 3 months of production validation, we can say with confidence: Nginx 1.25 is the best-in-class reverse proxy for high-concurrency API workloads. Apache’s prefork MPM is a relic of an era when web traffic was dominated by low-concurrency static sites, and it no longer meets the performance requirements of modern JSON APIs serving 10k+ concurrent connections. The 25% p99 latency reduction we saw is not an edge case—it’s reproducible for any team running Apache with prefork MPM for API workloads. If you’re still using Apache for your API gateway, start your migration today: use the nginx-proxy/mapper tool to convert your configs, benchmark with wrk2, and enable io_uring for maximum performance. The $18k/month in cost savings and 8% reduction in API churn are worth the effort for any team with more than 50 API consumers.
25%Reduction in p99 API reverse proxy latency
