# Asama vs LogicMonitor + Edwin AI: A component-by-component comparison

> LogicMonitor + Edwin AI compared with Asama AI & scored on Infrastructure State, Detection and Remediation Capability.

- URL: https://blog.asama.ai/blog/01-asama-vs-logicmonitor/
- Published: 2026-08-23
- Tags: Articles

## Executive summary

LogicMonitor with Edwin AI scores higher than any other commercial incumbent in this comparison set. It scores 2.2 on Infrastructure State and 2.7 on detection, and its correlation engine is the best-engineered thing any monitoring vendor is currently shipping. If your problem is alert sprawl across five monitoring tools, this is the right architecture and the ROI numbers are believable.

| | LogicMonitor + Edwin AI      | Asama     |
|---|---|---|
| **Infrastructure State** | **2.2** / 4 | **4.0** / 4 |
| **Detection** | **2.7** / 4 | **4.0** / 4 |
| **Remediation** | **2.0** / 4 | **4.0** / 4 |

Every score on this page is out of 4, and every component behind these averages is shown below.

**Read the remediation average carefully. It is 2.0, and act is 0.** Edwin AI scores 3 on planning, the joint-highest of any platform we have scored, and that score is weighted three times, so it carries the whole average on its own. Underneath it, LogicMonitor changes nothing on any machine: LogicModules are documented as making no modifications to target systems, and the AI Automation page states that Red Hat Ansible Automation Platform applies the changes. Edwin AI decides which of your playbooks to fire; Ansible touches the machine.

The other number worth holding is the price of the gap. Zabbix wired to a frontier model scores 2.3 on detection for an afternoon's work. LogicMonitor at its top tier, with Edwin AI bundled, runs to roughly $636 per device per year at list and scores 2.7. Four tenths of a level.

---

## The Evaluation Framework

Every component is scored 0 to 4, and the number is the level. As it rises, work moves from your engineers to the product. **L0** not addressed. **L1** raw material. **L2** consolidated, you reason. **L3** reasons within limits. **L4** reasons across the fleet and down the stack, you approve. Set out in full in [the four levels of detection maturity](https://blog.asama.ai/blog/four-levels-of-detection-maturity).

A group's score is the **weighted average of its components**, on the same 0 to 4 scale, with the pivot component counting three times. Every component score is published below, so you can see exactly which cells produced the average and re-score any of them.

And **Infrastructure State bounds both loops from above**: no engine can correlate across a layer that was never captured, or plan against state that was never recorded.

---

## Part 1: Infrastructure State

What the platform knows about your fleet before anything goes wrong.

| Component | L0 | L1 | L2 | L3 | L4 |
|---|---|---|---|---|---|
| **Breadth** | reachability only | major layers, one OEM | major layers, across OEMs | a band of adjacent layers with depth, connected, across OEMs | interconnect and components through firmware, BMC, BIOS, drivers, kernel, OS to hypervisor, across OEMs |
| **Depth** | nothing | detects events | + live telemetry | + clustered inventory and golden baselines held as text | + config and tuning state, multi-vendor, drift classified against an evolving reference |
| **Normalisation** | content written per model | industry-standard formats only: MIB-II, IPMI, Redfish | a few aspects of major components, across OEMs, one layer | the same, across a few layers | every aspect of every component across every layer and OEM; one component is one object |
| **Topology** | a flat list | the relationships you declared | discovered within one domain | discovered across adjacent layers, kept current | live model of assets, layers, change history and dependencies, fleet-wide |
| **History** | current state only | raw series and events; you must know what to query | history plus reporting | indexed and searchable; you must know to ask | prior faults, RMA history and config changes for node and cohort, surfaced into the diagnosis |

### Breadth - LogicMonitor + Edwin AI 2, Asama 4

Collectors reach the OS, virtualisation, cloud, Kubernetes and network across OEMs, and the supported Redfish package adds multi-vendor BMC visibility for processors, accelerators, memory, storage controllers, drives and PCIe devices. It is an optional module, so check it is in your quote.

Level 2 because reading a BMC sensor and a component inventory is the bottom of the reach, not a step below the OS. No module ships for BIOS configuration capture, kernel parameter or module baselining, firmware tuple tracking, PCIe link-width monitoring, per-DIMM error trending or DCGM-level accelerator telemetry. Without the management controller, server health needs the OEM agent stack.

Asama covers interconnect and components through firmware and BMC, BIOS, drivers, kernel and OS to the hypervisor, across Dell, HPE, Lenovo, Supermicro and NVIDIA hardware in one model.

**The gap costs you:** the failure signatures that cost the most live one layer below where the collectors stop.

### Depth - LogicMonitor + Edwin AI 2, Asama 4

Events, telemetry and inventory, all strong. ConfigSources capture device configuration files over SSH with change detection, genuinely more than most monitoring platforms hold. Level 3 needs a golden baseline: a reference the configuration is evaluated against. ConfigSources diff a device against its own previous version, which tells you something changed rather than that it is wrong.

The extensibility deserves credit. Scripted DataSources run Groovy, PowerShell or external programs on the collector, so a skilled engineer could pipe SMART output or Redfish BIOS attributes in as custom modules. Three things limit that: it is content you author per SKU and firmware generation, it lands as numeric time series rather than comparable state, and no analytics exist to consume it. **Collection is scriptable; comprehension is not.**

Asama holds configuration and tuning state across BIOS, kernel, firmware, drivers and device config for a multi-vendor estate, and classifies drift against a reference that evolves with the fleet.

**The gap costs you:** a BIOS parameter scraped by a script becomes a graph line with no change history and nothing to diff against.

### Normalisation - LogicMonitor + Edwin AI 2, Asama 4

The Redfish package normalises hardware sensors across OEMs at the BMC layer, hand-modelled by LogicMonitor's own specialists. Real work, and one layer. OS metrics are normalised separately and the two are not joined: BMCs are modelled as resources distinct from their OS hosts, so an OS-visible device and its physical representation are not reliably the same object. Two one-layer efforts is level 2.

Asama proves that the drive the kernel enumerates, the drive the controller reports and the entry in the chassis inventory are one object.

**The gap costs you:** the integration count in the datasheet is a State number, not a normalisation number, and it never converges on identity.

### Topology - LogicMonitor + Edwin AI 2, Asama 4

Auto-maintained maps, dependency suppression and the 2026 AI Topology Intelligence release are a genuine discovered relationship model, kept current. Within the estate this map covers, interconnect through hypervisor, it discovers virtualisation containment and network reachability: one domain.

Asama maintains a live model of assets, components, layers, change history and dependencies across the fleet.

**The gap costs you:** the graph does not carry configuration state, so it can tell you what is connected to what and not what changed on either end.

### History - LogicMonitor + Edwin AI 3, Asama 4

The highest component score on this axis. History is indexed and retrievable, and the Edwin AI agent surfaces past-incident resolutions in the console, Slack, Teams and PagerDuty. Level 3 exactly: searchable, conversational, dependent on the engineer knowing to ask.

Asama surfaces prior faults, RMA history and firmware and configuration changes for the node and its cohort into the diagnosis without being asked.

**The gap costs you:** level 3 is honestly level 2 with a search box. The institutional memory exists and is not easy to retrieve at the moment it would change a decision.

---

## Part 2: Detection Capability

From something changing to somebody knowing what is wrong. **Correlation is weighted three times**, because that is where the hours go.

| Component | L0 | L1 | L2 | L3 | L4 |
|---|---|---|---|---|---|
| **Symptom** | stores data; a person notices | a fixed limit somebody chose, crossed at or after impact | the signal's own history: fires when the node departs its own norm | own history qualified by seasonality, change windows, workload phase | the node against comparable nodes under comparable conditions, before any threshold of its own |
| **Correlation (x3)** | every alert stands alone | within one device, or a relationship you declared | unified dashboards, time-window mapping, anomalies vs projections; grouped, not assembled | AI search across tools and domains, assembling the case: scoped logs, change records, impacted config | end-to-end causal chain across the whole stack including BMC, BIOS, firmware, kernel and driver state, on a live topology |
| **Root cause** | nothing; the engineer forms the claim | orders the signals without asserting a cause | orders and selects the signals that bear on this incident | several probable causes, with the evidence for each | evidence for the causal chain, one root cause, a confidence level and the blast radius |
| **Learning** | nothing changes | records acknowledgments and alert volumes, manually | detects patterns from recorded failure confirmations and baselines | incorporates human verdicts into what it detects | live inventory of confirmations and baselines, written back into infrastructure state |

### Symptom detection - LogicMonitor + Edwin AI 2, Asama 4

Dynamic Thresholds baseline each metric against its own history and raise early warnings on anything the platform collects. The feature is gated to higher pricing tiers, so check yours. LogicMonitor's own material is clear that the comparison is self-referential: each node against its own past.

Asama compares each node against comparable nodes under comparable conditions and fires when it departs the cohort's norm, before it crosses any threshold of its own.

**The gap costs you:** a node that has always been slow, a repair batch reintroduced degraded, or a fleet drifting together never deviates from its own history. It deviates only from its peers, and that comparison never happens. Typical fleets carry roughly 65 silently degraded servers per 1,000 for exactly this reason.

### Correlation - LogicMonitor + Edwin AI 3, Asama 4

**The strongest correlation engine of any monitoring vendor in this set.** Edwin AI Event Intelligence correlates and dedupes across LogicMonitor plus 3,000 integrated tools, enriches with topology, CMDB and metadata, publishes noise-compression figures of 90 to 97 percent, and attaches scoped log events and change requests to the incident. Level 3, and only four entries in our whole set reach it.

Level 4 needs the chain to span BMC, BIOS, firmware, kernel and driver state. Every Edwin AI capability is a function over the alert stream, and an alert is something a monitor decided to emit.

Asama assembles an end-to-end causal chain across those layers on a live topology, across compute, storage and network.

**The gap costs you:** an incident that never fires an alert in any connected tool never enters the pipeline. An L3 assistant with no input is not an assistant.

### Root cause and evidence - LogicMonitor + Edwin AI 3, Asama 4

AI Investigation auto-generates a title, summary, probable root cause, impact and remediation guidance on major and critical insights, derived from alert data, log events and change requests scoped to the impacted configuration items. The emphasis on evidence-backed analysis over speculation is a credible design choice and earns [level 3 on the ladder](https://blog.asama.ai/blog/four-levels-of-detection-maturity).

It stops short of level 4 on two counts: the cause is offered as probable rather than declared with a confidence level, and there is no blast radius, because nothing models which other nodes share the at-risk state.

Asama presents the evidence for the chain and declares one root cause with a confidence level and the blast radius.

**The gap costs you:** it can conclude that a change request caused an alert storm. It cannot conclude that a bus link renegotiated to a lower generation after a component reseat, because that telemetry is not in the substrate.

### Learning - LogicMonitor + Edwin AI 2, Asama 4

Correlation improves on historical sequences and past incidents are retrievable, which is level 2. Tunable correlation thresholds are configuration rather than learning from verdicts, which is what level 3 requires.

Asama maintains a live inventory of failure confirmations and fleet baselines, and writes expected anomalies back into the infrastructure state so they are recalled during the next investigation.

**The gap costs you:** the grouping gets better; what gets detected does not.

---

## Part 3: Remediation Capability

From knowing what is wrong to the fleet being provably healthy. **Plan is weighted three times**, because acting is easy and knowing how to act is the hard part.

| Component | L0 | L1 | L2 | L3 | L4 |
|---|---|---|---|---|---|
| **Plan (x3)** | nothing; the engineer works it out from scratch | suggestions on troubleshooting this kind of issue | predefined runbooks for known patterns, bound to what triggers them | understands the problem and maps it to the matching procedure, with a target outcome | composes the plan at incident time against this node's exact hardware, firmware, BIOS and kernel |
| **Act** | does not execute; notifies, tickets or hands off | executes a single step against the target | multi-step with scheduling, change windows and audit | multi-step with failure handling: halts cleanly, restores a prior state | executes its own plan, staged, rollback pre-computed, maintenance policies honoured |
| **Verification** | nothing; the engineer checks by hand | reports whether the command ran and exited clean | a defined post-change check: compliance re-check, re-scan | re-compares against expected behaviour and state, surfacing degradation | against the pre-incident baseline, re-validated against peers |
| **Learning** | nothing persists | retains a retrospective a person wrote | stores the resolution as retrievable content | surfaces past resolutions inside a new incident of the same class | updates the procedure database so the next plan is better |

### Plan - LogicMonitor + Edwin AI 3, Asama 4

**The joint-highest planning score of any platform we have scored.** Edwin AI identifies the most relevant existing playbooks, recommends them from the context of the investigation, and can generate a net-new playbook where no automation exists. Understanding a problem and mapping it to the matching procedure with a target outcome is level 3 precisely.

Level 4 needs the plan composed against this node's exact hardware, firmware, BIOS and kernel. Edwin AI maps into a library, and the library is yours.

Asama composes the plan at incident time against that machine's exact state, with rollback computed before execution.

**The gap costs you:** the mapping is only as good as the playbooks in your ecosystem, and the procedure does not vary with the target's firmware or driver stack.

### Act - LogicMonitor + Edwin AI 0, Asama 4

**The number that sets the remediation grade, and LogicMonitor's own documentation is the source.** LogicModules are documented as requiring no write access and making no modifications to target systems. The AI Automation page describes Edwin AI as delivering remote execution and event-driven triggers across the automation ecosystem, and states in the same breath that Red Hat Ansible Automation Platform applies changes reliably at scale. Edwin AI decides which of your playbooks to fire; Ansible touches the machine. Governed handoff is still handoff.

Asama executes the plan it composed, staged, with rollback pre-computed and maintenance policies honoured; a failure leaves the fleet at the last verified step.

**The gap costs you:** the capability leaves with your Ansible licence, not your LogicMonitor one. A platform that markets Autonomous IT cannot, on its own, change anything.

### Verification - LogicMonitor + Edwin AI 1, Asama 4

LogicMonitor states that automated health checks confirm remediation before tickets close. Scored at 1 because the check is scoped to the alert that opened the ticket, which is alert clearance under another name; nothing compares the node against a declared expectation of what it should be. If a pilot demonstrates a genuine post-change check against a defined check set, this cell becomes 2.

Asama compares the node against its pre-incident baseline and re-validates it against its peers.

**The gap costs you:** "the alert cleared" is not evidence when the alert could not see the fault in the first place.

### Learning - LogicMonitor + Edwin AI 2, Asama 4

Solid, and the strongest of the incumbents. LogicMonitor documents that resolved incidents become reusable patterns and successful fixes are stored in an expanding knowledge base. That is content the platform can retrieve, which is level 2.

Asama updates its procedure database with every resolution, so the next generated plan is better.

**The gap costs you:** stored is not surfaced, and retrievable is not applied. Somebody still has to go and find the pattern.

---

## Placement

Among the 24 platforms and combinations we have scored, LogicMonitor with Edwin AI leads the commercial incumbents: first on detection at 2.7, joint-first on planning at 3, one of only four platforms at level 3 on correlation.

It is also the clearest illustration of the substrate ceiling. Ten of its thirteen components sit at 2 or 3 and none reaches 4. The two holding it back, symptom detection at 2 and act at 0, are architectural rather than engineering gaps: a self-referential baseline is not fixed by better analytics, and a read-only collector is not given a write path by adding an assistant. For context, Zabbix wired to a frontier model reaches 2.3 on detection for almost nothing; the four tenths LogicMonitor adds are correlation quality, not new visibility.

The framework is ours, and every anchor above is published so you can re-score any cell. Across 50-plus induced failures on Dell, HPE and Lenovo hardware, Asama's detection took 8 to 50 seconds.

---

## Conclusion

LogicMonitor has built the strongest alert-reasoning engine in this comparison set. If your estate produces alerts from five tools and your engineers spend their week triaging duplicates, buy it. The compression figures are real, the correlation is explainable, and the investigation output is evidence-backed rather than speculative.

**Then notice what it reasons about.** Every Edwin AI capability is a function over the alert stream, so an incident that never fires an alert never enters the pipeline. The failure classes that cost the most, a drive slow since installation, a server back from repair with settings reset, a link renegotiated to a lower generation, fire nothing anywhere. The engine is excellent and the fuel is metrics.

**And notice where the loop stops.** Plan 3, act 0. LogicMonitor works out what to do better than almost anyone and cannot do it. That is a coherent product decision, not an oversight, but it means the platform you are buying for autonomous operations is one component of an autonomous operation, and you own and licence the rest.

**The choice is not which correlation engine is better.** LogicMonitor's may well be. It is whether the substrate underneath it holds what your machines are supposed to be, and whether the platform that decides on the fix is the one that carries it out.

**Settle it on your own fleet.** Run both read-only for a month and measure three things: detections LogicMonitor did not raise, especially configuration drift and pre-threshold degradation; the clock split in two, from alert to assembled evidence and then evidence to cause, on incidents whose cause sits below the OS; and what actually executes when Edwin AI recommends a remediation, and whose licence it runs on.

*LogicMonitor material is drawn from public documentation, product pages and published announcements, current as of April 2026. Pricing is list and varies by tier and packaging.*