Should I Build It Should I Build It
11 Jun 2025
SaaS

Apm service that is focused only on issues not the good times

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Swamp

The market has seen several mediocre solutions that nobody loves. Unless you can offer something fundamentally different, you’ll likely struggle to stand out or make money.

Should You Build It?

Don't build it.

Your are here

The idea of an APM (Application Performance Monitoring) service focused solely on issues, rather than the good times, places you in a competitive space. Our analysis found 6 similar products, suggesting a moderately crowded market. The 'Swamp' category fits because there are existing APM solutions, many of which might not be loved, indicating an opportunity to differentiate but also a risk of blending in. Engagement with similar products is low, with an average of 3 comments, which suggest people might not care enough to comment, or that existing solutions solve their problems well enough already. Because of this, there is also no signal for use or buy. Therefore, standing out requires a fundamentally different approach to gain traction.

Recommendations

  1. Begin with thorough research to understand why existing APM solutions haven't fully satisfied users, especially regarding issue focus. Many current APM tools are broad; identify gaps in their issue detection, root cause analysis, or alerting capabilities.
  2. Instead of competing head-on, consider niche markets. Focus on specific industries or application types (e.g., e-commerce, IoT, high-frequency trading) with stringent performance needs and tailor your APM to their unique issue profiles. This will allow you to go deeper on what specific issues really matter to these users.
  3. Explore building specialized tools or plugins for existing APM providers, instead of creating a standalone service. You could offer an 'issue deep-dive' module that integrates with established platforms like DataDog or New Relic, capitalizing on their existing user base.
  4. Given the low engagement and 'Swamp' category, consider adjacent problems that might be more promising. For instance, could you pivot to focus on proactive issue prediction using machine learning, or perhaps create tools that automate issue resolution based on APM data?
  5. Look at Aperture's issues, specifically its PID component implementation, and whether it supports auto-scaling and circuit breakers. These are real issues that are already being discussed in the community. Address them head on.
  6. Also consider stability issues such as those experienced by Realtime LCM tool, with alpha users complaining about server stability issues with the influx of alpha users. Make sure to load test, and benchmark your solution early and often.
  7. In the short term, conserve resources and carefully evaluate the market before committing heavily. A 'wait-and-see' approach, coupled with continuous learning, may be the most prudent strategy. Your marketing on launch should be the opposite of the description of 'Uptime System', i.e. detailed and thorough.

Questions

  1. What specific issue types are currently poorly addressed by existing APM solutions, and what evidence (e.g., user reviews, forum posts) supports this claim?
  2. How can you leverage machine learning to predict potential issues before they impact application performance, and what unique data sources or algorithms would be required?
  3. What partnerships with existing APM providers could accelerate your entry into the market, and what specific integrations would be most valuable to their users?

Your are here

The idea of an APM (Application Performance Monitoring) service focused solely on issues, rather than the good times, places you in a competitive space. Our analysis found 6 similar products, suggesting a moderately crowded market. The 'Swamp' category fits because there are existing APM solutions, many of which might not be loved, indicating an opportunity to differentiate but also a risk of blending in. Engagement with similar products is low, with an average of 3 comments, which suggest people might not care enough to comment, or that existing solutions solve their problems well enough already. Because of this, there is also no signal for use or buy. Therefore, standing out requires a fundamentally different approach to gain traction.

Recommendations

  1. Begin with thorough research to understand why existing APM solutions haven't fully satisfied users, especially regarding issue focus. Many current APM tools are broad; identify gaps in their issue detection, root cause analysis, or alerting capabilities.
  2. Instead of competing head-on, consider niche markets. Focus on specific industries or application types (e.g., e-commerce, IoT, high-frequency trading) with stringent performance needs and tailor your APM to their unique issue profiles. This will allow you to go deeper on what specific issues really matter to these users.
  3. Explore building specialized tools or plugins for existing APM providers, instead of creating a standalone service. You could offer an 'issue deep-dive' module that integrates with established platforms like DataDog or New Relic, capitalizing on their existing user base.
  4. Given the low engagement and 'Swamp' category, consider adjacent problems that might be more promising. For instance, could you pivot to focus on proactive issue prediction using machine learning, or perhaps create tools that automate issue resolution based on APM data?
  5. Look at Aperture's issues, specifically its PID component implementation, and whether it supports auto-scaling and circuit breakers. These are real issues that are already being discussed in the community. Address them head on.
  6. Also consider stability issues such as those experienced by Realtime LCM tool, with alpha users complaining about server stability issues with the influx of alpha users. Make sure to load test, and benchmark your solution early and often.
  7. In the short term, conserve resources and carefully evaluate the market before committing heavily. A 'wait-and-see' approach, coupled with continuous learning, may be the most prudent strategy. Your marketing on launch should be the opposite of the description of 'Uptime System', i.e. detailed and thorough.

Questions

  1. What specific issue types are currently poorly addressed by existing APM solutions, and what evidence (e.g., user reviews, forum posts) supports this claim?
  2. How can you leverage machine learning to predict potential issues before they impact application performance, and what unique data sources or algorithms would be required?
  3. What partnerships with existing APM providers could accelerate your entry into the market, and what specific integrations would be most valuable to their users?

  • Confidence: High
    • Number of similar products: 6
  • Engagement: Low
    • Average number of comments: 3
  • Net use signal: 0.0%
    • Positive use signal: 0.0%
    • Negative use signal: 0.0%
  • Net buy signal: 0.0%
    • Positive buy signal: 0.0%
    • Negative buy signal: 0.0%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

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