Overview
Asserts.ai built a contextual layer for monitoring data. It used metrics from Prometheus-compatible systems to map relationships among application and infrastructure components, produce dashboards, and surface potential problems. Grafana Labs acquired the company in 2023 and brought its work into Grafana Cloud.[1][2]
Founding Story
Manoj Acharya founded Asserts in 2020 after serving as an early engineer and executive at AppDynamics. He wanted a way to make system relationships and operational expectations visible in monitoring data. Grafana later described his early work on a common model for Prometheus metrics and the relationships among system entities.[3]
A chart can show that a service is slow without showing which related component changed. Asserts sought to make those connections easier to explore. The connection is a starting point for investigation, not proof of a root cause.
Timeline
- 2020: Acharya founds Asserts.ai.[2]
- 2022: The company announces its product and a16z announces its investment.[1][4]
- 2023: Grafana Labs announces it has acquired Asserts.ai and plans a private preview in Grafana Cloud.[2]
- 2024 and later: Grafana describes integrated troubleshooting workflows and continues to document Asserts in Grafana Cloud.[5][6]
What They Built
A customer connected a Prometheus-compatible metrics source. Asserts scanned those metrics, inferred a map of application and infrastructure components, and created dashboards and checks called assertions. An assertion represented a condition worth investigating, with surrounding context to help the operator navigate related systems.[1][7]
For example, an engineer investigating errors in an application could inspect related services and infrastructure rather than opening unrelated charts one at a time. This approach depends on the quality and completeness of the underlying metrics and labels. Missing or incorrect telemetry can leave relationships incomplete.
Grafana described Asserts as a contextual layer for Prometheus and planned to use its entity relationship graph in Grafana Cloud. Its current documentation describes an Asserts Entity Explorer and knowledge graph-generated metrics, showing a continuing product lineage.[3][6][8]
Market Position
Target Customers
Asserts targeted engineers and operations teams responsible for distributed applications. Grafana's acquisition announcement included a testimonial from Heka.ai about correlating cluster problems and using ready-made alerts. It is a named customer example, not independent evidence of average savings.[2]
Market Size
The reviewed sources do not establish a reliable market size for this specific contextual monitoring layer.
Competition
Observability vendors and open-source tools already offered metrics, dashboards, alerts, and service maps. Asserts built on Prometheus and Grafana rather than requiring customers to replace the whole monitoring stack. Grafana Cloud now offers related Asserts workflows, making it an active successor for anyone considering a new product.[1][5]
Business Model
Asserts offered software to operations teams. The available sources do not establish a consistent price, revenue, margin, or renewal rate.
Traction
The a16z investment and a named customer testimonial show financing and a documented customer relationship. They do not establish the number of paying customers or financial performance.[4][2]
Outcome and evolution
A contextual layer joined its existing ecosystem
Grafana Labs announced the acquisition in November 2023. It said the company's model and graph would help connect infrastructure and application observability. The reviewed sources do not disclose the deal price or separately establish a closing day.[2][3]
Grafana's documentation now describes using Asserts to explore dependencies and investigate issues. The standalone company's identity ended, while its approach remains visible in Grafana Cloud.[6]
Key Lessons
- Connect the measurements. A relationship map can make a metric more useful during an investigation.
- Start with existing data. Asserts built on Prometheus-compatible metrics and the Grafana ecosystem.
- Include defaults. Ready-made checks and dashboards reduced the work needed to begin exploring data.
- Treat a suspected cause as a lead. The graph depends on source data and does not by itself prove causation.