Overview

Edge Delta SRE Teammate
3 Popular SRE Teammate Use Cases for Observability Teams
Edge Delta SRE Teammate
Introducing Edge Delta Workflows
Redis Caching Troubleshooting with AI Teammates
Reviewing GitHub PRs using AI Teammates

Product video
Edge Delta's AI Teammates are the only agentic observability team built on top of an enterprise-grade observability stack, enabling continuous analysis of real-time production data from all your tools, environments, and data sources. These out-of-the-box AI agents automate root cause analysis, surface actionable signals from noise, and help teams significantly reduce MTTR while focusing on the creative work that fuels innovation.
To facilitate Private Offers, contact sales@edgedelta.com .
Highlights
- AI Teammates learn what "normal" looks like across your services, automatically filtering out transient issues and low-value alerts so your team spends less time on manual triage and more time on work that actually matters.
- AI Teammates automatically correlate incidents with code merges, config changes, and related telemetry data to pinpoint the most likely cause, so your team gets straight to answers with full context already in hand.
- Intelligent Telemetry Pipelines and fine-grained RBAC controls keep your data secure and compliant, while seamless integrations with your existing tools mean your team can get value from AI Teammates in minutes, without introducing risk or starting from scratch.
Details
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Pricing
Dimension | Description | Cost/12 months |
|---|---|---|
Ent - ED - Telemetry Pipelines (Edge) 5TB - 12 Months - $146,000 | Edge Delta Telemetry Pipelines (Edge). Process/forward observability and security data to any downstream destination using Edge Fleets. 5000 GB of raw data ingest daily. 12 month term. Annual discounted cost of $146,000 | $10,950.00 |
250 GB/day | Ingest up to 250 GB of raw data per day | $27,375.00 |
500 GB/day | Ingest up to 500 GB of raw data per day | $54,750.00 |
1 TB/day | Ingest up to 1 TB of raw data per day | $109,500.00 |
1GB/Day | For organizations that need control and flexibility over growing volumes of telemetry data. | $36.50 |
100 GB/day | Up to 100 GB per day raw data ingest | $3,650.00 |
36.5 TB for 12 Months | Customer has 12 months to send 36.5 TB of raw data ingest into Edge Delta Pipelines | $3,650.00 |
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Customer reviews
Centralized observability has accelerated incident resolution and currently improves release monitoring
What is our primary use case?
My primary use case for Edge Delta has been observability, log monitoring, and troubleshooting application issues. I rely on it to analyze bugs, investigate incidents, monitor system behavior after releases, and help ensure overall platform reliability. Edge Delta serves as my main tool for log monitoring, analyzing logs, investigating issues, and gaining better visibility into application and system performance.
One specific example of how I used Edge Delta for log monitoring was during a production release where we needed to verify that a new back-end feature was behaving as expected after deployment. We used Edge Delta to monitor application logs in real-time and track error patterns, API failures, and unusual spikes in warnings. Shortly after deployment, we noticed an increase in validation errors coming from a particular service. By filtering and analyzing the logs, we quickly identified the root cause and worked with the development team to resolve it before it affected a large number of users. The biggest benefit was reducing the time spent manually searching through logs and helping the team identify issues much faster during critical release windows.
I used Edge Delta not only during incidents but also for routine monitoring, release validation, and investigating performance trends. Having centralized visibility into logs and telemetry data helped us be more proactive in identifying potential issues before they became larger problems.
What is most valuable?
In my experience, the best features of Edge Delta are real-time processing, powerful filtering and search capabilities, observability across multiple data sources, and efficient telemetry data management. The feature I found most valuable was real-time log analysis, which allowed me to identify issues quickly without waiting for long logs to be processed through multiple systems. I also appreciated the filtering and querying capabilities because they made it easier to isolate specific errors, services, or time periods during troubleshooting. That significantly reduced the time needed to investigate incidents. Overall, these capabilities helped improve operational awareness and accelerated issue resolution across teams.
Edge Delta has positively impacted my organization by improving operational visibility and reducing the time required to investigate issues. Before adopting a centralized observability solution, troubleshooting often involved gathering logs from multiple sources and manually correlating information. With Edge Delta, teams have a more unified view of system activity, which makes it easier to identify and diagnose problems. We also noticed faster incident responses and smoother post-release monitoring. Engineers could detect unusual patterns, errors, or performance issues earlier, which helped reduce the time between identifying a problem and resolving it. The biggest improvements were faster troubleshooting, better operational awareness, and more proactive monitoring of application health.
What needs improvement?
Overall, Edge Delta is a strong observability platform, but there are a few areas where it could be improved. One area is dashboard and reporting customization. The platform provides useful operational insights, but having more flexibility to create highly tailored views for different teams would be beneficial. Another area is onboarding and usability. Observability platforms can be complex, especially for new users, so additional guided workflows, recommendations, and learning resources could help teams become productive more quickly.
Deeper integration visibility would be useful as well. Many organizations use multiple monitoring, logging, and incident management tools, so having even better cross-platform correlation and troubleshooting workflows would add value. On the pricing side, observability platforms can become expensive as data volume grows. More granular cost optimization insights and usage visibility would help organizations better understand how data consumption impacts cost and where optimizations can be made. The platform is strong in its core capabilities. The improvements I would prioritize would be deeper ecosystem integrations, enhanced cost visibility, and more intelligent automation to help teams act on observability data more efficiently.
For how long have I used the solution?
I have been using Edge Delta for one year.
What do I think about the stability of the solution?
Overall, I find Edge Delta to be a stable platform. In day-to-day usage, we have not experienced significant reliability issues, so the stability is one of the strengths of the platform, and I feel comfortable relying on it for production monitoring and troubleshooting activities.
What do I think about the scalability of the solution?
Scalability is one of the areas where Edge Delta performed well. I find the platform capable of supporting growth in both data volume and system complexity, which is important for organizations with expanding applications and infrastructure.
How are customer service and support?
I have not personally interacted with the customer support team, but from what I have heard from my team and other teams, they have a significantly nice customer support system. The people answering the queries are really intelligent, and they have provided responses in a very reasonable time. Overall, I would rate the customer support around eight out of ten because the team is helpful, technically competent, and responsive.
Which solution did I use previously and why did I switch?
Before Edge Delta, we primarily relied on a combination of traditional log management and monitoring tools along with some in-house dashboards. While those tools provided visibility, it often required jumping between multiple systems to gather logs, correlate events, and investigate incidents. That made troubleshooting time-consuming, especially during production issues or release validation. We evaluated Edge Delta because we sought more efficient log processing. The main reason for adopting Edge Delta was to improve visibility, reduce investigation time, and simplify how teams worked with large volumes of telemetry and log data.
How was the initial setup?
My experience with pricing and licensing has been generally positive, although I am more involved in the implementation and day-to-day usage than in procurement decisions. From an implementation perspective, the setup effort is fairly reasonable. Most of the work involves configuring data sources, integrations, log collection pipelines, and dashboards rather than deploying infrastructure. Because Edge Delta is a managed SaaS platform, there are minimal infrastructure-related setup costs on our side, which help speed up adoption. In terms of licensing, my understanding is that costs are influenced by factors such as data volume and usage. Overall, I find the setup process straightforward, and the licensing model feels aligned with the capabilities and operational benefits the platform delivers.
What was our ROI?
The biggest benefit has been the reduction in troubleshooting and incident investigation time. Based on our experience, root-cause analysis has become roughly thirty to forty percent faster, allowing engineers to spend less time searching through logs and more time resolving issues. From a quality and reliability perspective, earlier detection of issues has helped reduce the risk of prolonged production incidents, which provides value even if it is not always measured in direct financial terms. While I do not have exact dollar figures, the ROI is evident through time savings, improved operational efficiency, and better visibility into system health.
Which other solutions did I evaluate?
We looked at a few other observability and log management tools such as Datadog , Splunk, and Elastic Observability . Each of those products has its own strengths, but what stood out about Edge Delta was its approach to processing and analyzing telemetry data efficiently and helping reduce operational overhead.
What other advice do I have?
My advice would be to start by clearly defining your observability goals before implementation. Understanding what you want to monitor, whether it is application performance, log analytics, incident response, or operational visibility, will help you get the most value from the platform. I suggest starting with a focused use case and expanding gradually. Once teams become comfortable with the workflows and dashboards, it is much easier to scale observability practices across additional services and environments. I would rate this review an eight overall.
Good Experience
As on investigating with customer support tickets, it helps on properly see the information on a good speed.
