Prompt
I’m using Postman and need better performance testing, what should I add?
Latest observation
If you’re using Postman and want better performance testing, the biggest thing to add is a real load-testing tool rather than relying on Postman alone.
What to add
- Newman: runs Postman collections from the command line and is great for automation/CI.
- k6 or JMeter: better suited for actual performance/load testing.
- Monitoring/logging tools: to measure server response time, throughput, errors, CPU, memory, DB latency, etc.
Why Postman alone isn’t enough
Postman is excellent for:
- API development
- functional testing
- smoke checks
But it’s not ideal for:
- high-concurrency load testing
- stress testing
- sustained performance measurement
- detailed performance metrics
Good setup
- Build requests in Postman
- Export collection
- Run with Newman for automation
- Use k6/JMeter for load/performance
- Track results in your backend monitoring tools
If you want to stay close to Postman
Postman also has:
- Collection Runner for iterative runs
- Monitors for scheduled checks
But those are still more for availability/functional checks than serious performance testing.
If you want, I can suggest the best tool based on your goal:
- quick API checks
- CI/CD regression testing
- load testing with many users
- API latency benchmarking
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
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