t3.medium - Bifrost

Instance Configuration

AWS t3.medium Specifications:

vCPUs: 2
Memory: 4GB RAM
Network Performance: Up to 5 Gigabit

Bifrost Configuration:

Buffer Size: 15,000
Initial Pool Size: 10,000
Test Load: 5,000 requests per second (RPS)

Performance Results

Overall Performance Metrics

Metric	Value	Notes
Success Rate	100.00%	Perfect reliability under high load
Average Request Size	0.13 KB	Lightweight request payload
Average Response Size	1.37 KB	Standard response size for testing
Average Latency	2.12s	Total end-to-end response time
Peak Memory Usage	1,312.79 MB	~33% of available 4GB RAM

Detailed Performance Breakdown

Operation	Latency	Performance Notes
Queue Wait Time	47.13 µs	Time waiting in Bifrost’s internal queue
Key Selection Time	16 ns	Weighted API key selection
Message Formatting	2.19 µs	Request message preparation
Params Preparation	436 ns	Parameter processing
Request Body Preparation	2.65 µs	HTTP request body assembly
JSON Marshaling	63.47 µs	JSON serialization time
Request Setup	6.59 µs	HTTP client configuration
HTTP Request	1.56s	Actual provider API call time
Error Handling	189 ns	Error processing overhead
Response Parsing	11.30 ms	JSON response deserialization

Bifrost’s Total Overhead: 59 µs* *Excludes JSON marshalling and HTTP calls, which are required in any implementation

Performance Analysis

Strengths on t3.medium

Perfect Reliability: 100% success rate even at 5,000 RPS
Memory Efficiency: Uses only 33% of available RAM (1,312.79 MB / 4GB)
Minimal Overhead: Just 59 µs of added latency per request
Fast Operations: Sub-microsecond performance for most internal operations

Resource Utilization

Memory Usage: Very efficient at 1,312.79 MB peak usage
CPU Performance: Handles 5,000 RPS workload effectively
Queue Management: 47.13 µs average wait time indicates good throughput

Configuration Recommendations

Optimal Settings for t3.medium

Based on test results, these configurations work well:

{
  "client": {
    "initial_pool_size": 10000,
    "buffer_size": 15000
  }
}

Tuning Opportunities

For Lower Memory Usage:

Reduce initial_pool_size to 7,500-8,000
Decrease buffer_size to 12,000-13,000
Trade-off: Slightly higher latency

For Better Performance:

Increase initial_pool_size to 12,000-13,000
Increase buffer_size to 17,000-18,000
Trade-off: Higher memory usage (monitor RAM limits)

Comparison Context

vs. t3.xlarge Performance

Metric	t3.medium	t3.xlarge	Difference
Bifrost Overhead	59 µs	11 µs	+81% slower
Queue Wait Time	47.13 µs	1.67 µs	+96% slower
JSON Marshaling	63.47 µs	26.80 µs	+58% slower
Response Parsing	11.30 ms	2.11 ms	+81% slower
Memory Usage	1,312.79 MB	3,340.44 MB	-61% usage

Key Insights:

t3.medium uses 61% less memory than t3.xlarge
Performance trade-offs are reasonable for cost savings
Most operations still complete in microseconds

Next Steps

When to upgrade to t3.xlarge:

Sustained load approaches 4,000+ RPS
Queue wait times consistently exceed 75 µs
Memory usage approaches 75% of available RAM
Run Your Own Benchmarks to test with your specific workload
Compare with t3.xlarge for performance scaling analysis

​Instance Configuration

​Performance Results

​Overall Performance Metrics

​Detailed Performance Breakdown

​Performance Analysis

​Strengths on t3.medium

​Resource Utilization

​Configuration Recommendations

​Optimal Settings for t3.medium

​Tuning Opportunities

​Comparison Context

​vs. t3.xlarge Performance

​Next Steps