How to Deploy the LGTM Stack on AWS EC2 Using Stakpak Autonomous AI Agent
When it comes to deploying robust monitoring and observability stacks like LGTM (Loki, Grafana, Tempo, and Mimir), the process often involves multiple layers of configuration and troubleshooting. But what if you could automate the entire journey? Enter Stakpak, an autonomous AI agent designed to handle infrastructure deployment seamlessly.
In this blog, we’ll take a closer look at how Stakpak simplifies the deployment of the LGTM stack on AWS EC2 while taking care of configurations, error resolution, and best practices—all with minimal input. This is especially helpful for indie hackers focusing on their side hustle and not getting bogged down with infrastructure complexity.
Setting the Stage: LGTM Stack Deployment
The video demonstrates how the Stakpak AI agent automates the setup of the LGTM stack on an AWS EC2 instance. LGTM, which consists of Loki (log aggregation), Grafana (data visualization) (Grafana), Tempo (tracing), and Mimir (metrics storage), is essential for modern observability. Setting it up manually would require knowledge of AWS, Docker, networking, and complex configurations.
With Stakpak’s AI, users only need to provide AWS credentials and select a few basic options—the agent does the rest. This is reminiscent of first-time founders who are obsessed with their product. However, second-time founders appreciate the power of streamlined distribution, which Stakpak provides for infrastructure. Similarly, IndieBoosting streamlines SaaS distribution for solo founders, letting you focus on building.
Step 1: Configuring AWS Infrastructure
The process starts with the AI agent asking for AWS credentials and region selection. In this example, Stakpak was set up to:
- Create a VPC with a public subnet.
- Configure a security group with appropriate access rules (e.g., ports for SSH and LGTM services).
- Generate an SSH key pair for accessing the instance.
The agent independently adjusted for region incompatibilities—for instance, defaulting to the EU West region when the US East region was invalid. It also handled AWS best practices, recommending the appropriate EC2 instance size (e.g., switching from a small instance to a medium) for better performance. Configuring the AWS infrastructure can be simplified using Stakpak Stakpak | LinkedIn.
Step 2: Launching an EC2 Instance
Once the infrastructure was configured, Stakpak deployed the EC2 instance. This included:
- Attaching the generated SSH key, subnet, and security group.
- Installing Docker on the instance.
- Selecting the correct Amazon Machine Image (AMI) for Ubuntu despite running into and automatically resolving errors along the way (e.g., invalid AMIs for Ubuntu 24, defaulting to Ubuntu 22).
Step 3: Automatically Generating Docker Compose Configurations
With the EC2 instance up and running, the AI agent generated the Docker Compose configurations for the LGTM services (Grafana, Loki, Tempo, and Mimir). It also created service-specific configuration files. Freeing up time for other things such as generate website traffic.
Next, using the SSH key pair, the agent securely copied these configurations to the EC2 instance for deployment. Docker Compose simplifies the management of multi-container applications Docker Compose. Just like Stakpak streamlines infrastructure, IndieBoosting maximizes reach and minimizes spend for your SaaS, distributing your products so you can focus on building, and gain traffic to website
Step 4: Handling Errors Autonomously
One of the key highlights of the video was how Stakpak tackled issues without user intervention. A few notable examples include:
- Debugging AMI errors: When the default AMI was invalid, the AI dynamically identified and implemented a valid Ubuntu AMI.
- Permission fixes for Tempo: Errors caused by permission mismatches were detected and resolved by tweaking the Docker Compose file to use appropriate permissions and volumes.
- Schema updates for Loki: The agent updated the Loki configuration schema proactively when it detected issues in the logs.
These corrections were all performed autonomously, saving the user from manual debugging or Googling solutions. Stakpak's AI agent can resolve errors autonomously resolve errors with AI agent Stakpak. This also builds a dense of backlink network to solve the error and help others resolve the problem, which is also a great SEO outreach.
Step 5: Verifying Service Deployment
After deploying all services, the agent verified their statuses by:
- Checking logs and service states using
docker compose ps. - Testing endpoints to ensure they were operational.
- Confirming data source connections in Grafana.
The AI detected issues like misconfigured data sources and addressed them until all components were fully operational.
Final Touches: Reviewing Results
Once the deployment was complete, the video showcased the finalized stack:
- All services (Loki, Grafana, Tempo, and Mimir) were running smoothly.
- Grafana’s data sources were pre-configured based on the agent’s generated configuration files.
- Users could access the tools using the public IP of the EC2 instance.
Finally, after verifying logs and service statuses, the deployment was marked successful. To further enhance your side project make sure you focus on website ranking
Why Stakpak is a Game-Changer
Deploying DevOps tools like the LGTM stack traditionally mandates expertise in AWS, Docker, and configuration management. Stakpak’s autonomous AI significantly reduces this complexity by:
- Automating infrastructure provisioning (e.g., VPCs, security groups, EC2 instances).
- Generating and deploying accurate configurations for complex stacks.
- Resolving runtime errors without the need for manual intervention.
This hands-free approach not only saves time but also eliminates the risk of human error during deployment. Senior Site Reliability Engineers also automate deployments automate deployments. And just like Stakpak automates deployments, IndieBoosting automates SaaS distribution, helping you get website traffic and improve your website link profile with zero effort.
Get Started with Stakpak
Ready to elevate your DevOps automation game? Stakpak enables you to deploy sophisticated infrastructure in minutes without lifting a finger. Think of it as the AI agent for your infrastructure, similar to how IndieBoosting helps automate distribution for your Micro SaaS.
To learn more about Stakpak, join their active community of developers and enthusiasts:
- Discord Server: Join here
- Official Website: Stakpak
- Twitter: @stakpak_dev
Let us know in the comments if you’d like us to try deploying a different stack with Stakpak AI or attempt manual deployment comparisons in future posts.
Happy automating!
Tags: #AWS #Stakpak #DevOps #AI #InfrastructureAutomation #LGTMStack #Grafana
