Many observability tools that promised to bring clarity to production systems have largely multiplied the noise with endless dashboards, alert fatigue, and pricing that feels like a puzzle.
When an issue occurs, engineers tend to spend more time wrangling their monitoring stack than fixing what’s actually broken. And now, AI has entered the scene, promising to help fix the mess.
Nearly every major vendor is rolling out an AI-powered assistant that claims to think for you—co-pilots, agents, digital teammates—all offering instant answers and root cause analysis. But beneath the marketing gloss, there’s a huge difference in how these systems actually work.
A clear split is emerging. Legacy vendors are layering AI on top of rigid, proprietary platforms, creating smarter but even more confining systems. Meanwhile, newer entrants are taking an open, AI-native approach—built to collaborate with engineers, not trap them.
In this article, we’ll compare the top 7 AI-powered observability platforms to find out what the real trade-offs are. Which are truly autonomous? Which are just chatbots? And most importantly, which one is actually here to help you resolve issues faster?
Let’s dive in and see.
1. Agent0 by Dash0
Dash0 is an OpenTelemetry-native observability platform built from the ground up to challenge the existing paradigm of how engineers understand and interact with their systems.
At its core is Agent0, a collection of specialized AI agents designed to work with engineers, not replace them. Each one acts like an expert teammate that handles the heavy lifting of triaging incidents, finding root causes, writing queries, or guiding instrumentation so SREs can focus on higher-level reasoning and decision-making.
Agent0 isn’t just a chatbot bolted onto the corner of a dashboard. The “guild” of specialized agents is deeply woven into the entire product experience, analyzing data and surfacing in context, right where you’re working—from the alert notification itself to the trace view and the query builder.
What’s good
Each agent in Dash0 is transparent about its reasoning. You can see exactly what data it analyzed, which tools it used, and how it reached its conclusions. That visibility builds trust and helps engineers learn from the AI’s decision-making process.
This level of clarity is possible because Dash0 was built around OpenTelemetry and other open standards from the start. It deeply understands OpenTelemetry semantics and requires no translation layers or proprietary formats.
The benefit of this OpenTelemetry-native design is that lock-in is non-existent. When “The Oracle” helps you write a query, it’s writing PromQL. When “The Artist” builds you a dashboard, it’s a Perses-compatible dashboard. When “The Pathfinder” guides your instrumentation, it’s generating a standard OpenTelemetry Collector configuration.
So even if you decide to discontinue your use, you’ll retain all your assets. You can simply point your Collectors to a new backend and import your existing alerts and dashboards seamlessly.
The catch
Agent0 is designed as a human-in-the-loop partner. It doesn’t act on its own but waits for your prompt to build, query, or investigate. This approach is excellent for teams who want to direct the workflow, but it’s a different model for those seeking a fully autonomous, “hands-off” agent.
The verdict
Agent0 represents a new model for observability that’s collaborative and deeply transparent. Instead of making you interpret dashboards, it tells the story behind it by narrating what’s happening, why it’s happening, and how to fix it.
This approach finally shifts the industry from mere data collection to genuine understanding. The goal isn’t to replace engineers but to amplify them by helping every developer reason about production systems with the confidence of a senior SRE.
The result isn’t automation for automation’s sake; it’s enablement. Agent0 lightens the cognitive load of troubleshooting and helps every engineer, from new hire to veteran SRE, get to the root cause faster. For teams ready to move beyond the dashboard, Agent0 is leading that future.
2. Bits AI by Datadog
Datadog remains the heavyweight of observability, covering everything from infrastructure and APM to logs, security, and RUM. Its latest addition, Bits AI, takes a bold step into autonomous operations.
Rather than a simple assistant, it’s a collection of agents designed to act like digital teammates—an AI SRE for on-call, a Dev Agent for coding, and a Security Analyst for incident response.
When an alert fires, Bits AI begins investigating on its own, aiming to have a root cause hypothesis ready before an engineer even checks in.
What’s good
Bits AI shines in triage and coordination. The AI SRE jumps on alerts instantly, gathers telemetry, reads runbooks, and tests multiple hypotheses to narrow down causes. It also coordinates incidents, posting updates in Slack and drafting stakeholder summaries.
Beyond ops, the Dev Agent can detect critical errors and propose code-level fixes, while the Security Analyst helps with Cloud SIEM investigations. It learns from feedback too, refining its responses based on past incidents.
The trade-offs
All that autonomy comes at a cost—literally and strategically. Datadog’s pricing model is already known for being complex and expensive, and Bits AI adds a new layer of continuous analysis on top. Because it runs queries and investigations autonomously every time an alert fires, costs can rise quickly.
At the same time, its deep integration means you’re committing even further to Datadog’s ecosystem. Once your team’s incident response workflow is built around an autonomous AI that triages alerts and coordinates incidents, migrating to another platform becomes almost unthinkable.
You’re not just rebuilding dashboards; you’re firing your entire AI SRE team and starting from scratch. This AI is designed to make the Datadog ecosystem stickier than ever.
The verdict
Bits AI is one of the most ambitious examples of AI-driven observability to date. It delivers genuine automation and insight, showing what’s possible when an AI operates like a true teammate.
But it also doubles down on Datadog’s closed approach. The AI is built to analyze Datadog data, query Datadog metrics, and ultimately lock you in even deeper.
It solves the “too much data” problem not by embracing open standards or fixing the cost, but by selling you an even more expensive, autonomous AI to manage the complexity.
It’s an ideal solution for teams already all-in on Datadog and willing to pay the premium. For everyone else, it’s a powerful example of the future, but one that comes at the non-negotiable price of total vendor lock-in.
3. Davis AI by Dynatrace
Dynatrace has been doing AI-driven observability long before it became trendy.
Its core engine, Davis, is built on causal AI, designed not to chat but to think. When an issue arises, it automatically analyzes dependencies across its “Smartscape” topology to pinpoint the exact root cause and business impact, grouping hundreds of noisy alerts into a single, actionable problem.
Recently, Dynatrace expanded this with Davis CoPilot, a generative AI layer that pairs with its causal and predictive AI to form what it calls its “Hypermodal AI”.
What’s good
Davis’s strength lies in its deterministic root-cause analysis. It doesn’t guess or summarize—it tells you why something broke, and traces issues directly to the code, service, or deployment responsible.
The CoPilot layer builds on that foundation, giving natural-language summaries and guided remediation steps backed by Davis’s verified insights. The UI integration is also strong: when Davis finds a problem, the platform switches into an interactive troubleshooting mode that visually highlights the most relevant data.
The catch
Dynatrace’s power depends on its closed ecosystem. Its causal AI works only because it controls every layer—data ingestion, storage, and topology—through its OneAgent and Grail data lake.
OpenTelemetry is supported but secondary, and you lose much of Davis’s magic without full platform adoption. Add in the proprietary DQL query language, high cost, and complex setup, and it’s clear you’re buying into a tightly coupled, enterprise-scale stack.
The verdict
Davis is, without a doubt, the OG of AIOps. Its causal engine is a proven powerhouse that defined what deterministic root-cause analysis should look like for a decade.
But it is the very definition of an old-guard, monolithic approach. Its brilliance is inseparable from its proprietary OneAgent and its closed data model.
For teams looking to build on the open, flexible, and portable standards of OpenTelemetry and related projects, Dynatrace’s model represents a step back into a locked-down world.
4. Grafana Assistant
Grafana has long been the open-source standard for observability dashboards, and the broader LGTM stack (Loki, Grafana, Tempo, and Mimir) provides a powerful, open-source alternative to proprietary vendors.
Its new Grafana Assistant brings AI into that familiar workflow as a context-aware co-pilot built directly into Grafana Cloud. It helps with tasks like creating dashboards, writing queries, and troubleshooting issues, all through natural language.
Ask it to build a dashboard for Kafka and Postgres, and it scaffolds it instantly, complete with sensible alerts and explanations.
What’s good
Grafana Assistant is a true productivity booster. It removes the need to be a PromQL expert, letting engineers focus on analysis rather than syntax. Because it understands your actual data sources (in Loki, Mimir, Tempo) its recommendations are grounded in your live telemetry.
It can even review your Grafana Alloy configuration to trim high-cardinality metrics and reduce ingestion costs. For incident response, the new “Assistant Investigations” feature coordinates multiple specialized agents to analyze metrics, logs, and traces in parallel and summarize the findings.
The catch
The main catch is the Grafana platform itself. It’s not one unified system, but the fragmented “LGTM stack”. Your metrics, logs, and traces live in separate, siloed databases, which means engineers need to understand multiple complex query languages (PromQL, LogQL, TraceQL) just to correlate data.
The Grafana Assistant makes this easier, but it’s essentially a conversational band-aid on top of this fundamental fragmentation. While it helps you write the different queries, the AI’s effectiveness is ultimately limited because it can’t solve the fundamental problem: your data isn’t unified.
Finally, while Grafana’s open-source roots remain strong, the most capable version of the Assistant lives inside Grafana Cloud. The OSS version only offers a lightweight plugin that connects to external LLMs, not the fully integrated experience.
The verdict
Grafana Assistant is a smart, accessible evolution for Grafana users. It brings genuine AI assistance to the daily flow of observability work, helping teams build faster and operate more efficiently.
It’s the best possible AI for the “LGTM” way of doing things, but its effectiveness is ultimately capped by that fragmented model.
5. Observe AI SRE & o11y.ai
Observe approaches AI observability from two sides: production and development.
At the core is the Observe AI SRE, an always-on reliability agent built on two proprietary layers—the O11y Data Lake for unified telemetry storage and the O11y Knowledge Graph, which maps relationships across services, infrastructure, and business data. This foundation lets the AI correlate signals, pinpoint causes, and suggest remediations through natural language.
For developers, o11y.ai brings observability into your development flow to help with automatically instrumenting apps before they ever reach production.
What’s good
The Knowledge Graph is Observe’s real advantage. Because it understands how systems connect, the AI SRE can perform sharp, context-rich root cause analysis that goes beyond chart correlation.
The o11y.ai tool is equally interesting: it scans GitHub repos, instruments them with OpenTelemetry, scores their observability coverage, and can even generate pull requests to fix detected issues.
On the business side, Observe links technical data to KPIs, letting you ask questions like “how much revenue loss did this outage cause?” And unlike many platforms, its AI runs directly on a low-cost, unified data lake, avoiding the extra expense of stacking AI on top of fragmented tools.
The catch
The O11y Knowledge Graph is both the secret sauce and the risk. The AI’s insights are only as good as the opaque, proprietary, auto-generated abstraction of your system
You have to place 100% trust in this black box. If the graph gets a dependency wrong, the AI won’t just be unhelpful—it will be confidently leading you down the wrong path, and you’ll have no way to audit its reasoning beyond that abstraction
Meanwhile, o11y.ai is still early in scope, and its current TypeScript focus makes it limited for teams running on other languages.
The verdict
Observe delivers one of the most integrated AI visions in observability, connecting developer experience and production insight in a single, graph-driven model.
But to unlock its full potential, you must commit to its data lake and trust the accuracy of its knowledge graph. It’s elegant, cost-aware, and tightly unified—a closed ecosystem that rewards total buy-in.
6. OpsAI by Middleware
Middleware is a full-stack observability platform built around OpsAI, its autonomous co-pilot. Unlike most assistants that stop at analysis, OpsAI aims to take action!
It detects production issues across APM, RUM, and Kubernetes, gathers stack traces and logs, and links directly to your GitHub repo to locate the root cause.
When confident, it can generate a pull request with a proposed code fix—or, in Kubernetes environments, apply a fix automatically with user approval.
What’s good
OpsAI’s standout feature is its ability to move from diagnosis to repair. It doesn’t just point out what’s wrong; it drafts a fix and opens a PR, claiming over 95% confidence before doing so.
For Kubernetes, its “Auto Fix” mode takes this further, applying corrections in real time. By connecting to your source code, it can correlate telemetry with specific files and lines, offering highly targeted remediation.
The catch
To function, OpsAI requires deep access to your GitHub repositories. That level of integration delivers powerful insights but also raises real trust and security considerations.
The platform is also fully proprietary, relying on Middleware’s own SDKs rather than OpenTelemetry. If your stack isn’t among the supported languages, you’re left out. And for now, GitHub is the only supported code host, with GitLab and Bitbucket still pending.
The verdict
OpsAI represents one of the boldest steps toward autonomous observability. The idea of an AI that not only detects but fixes production issues is futuristic and exciting.
Yet it comes with significant trade-offs—tight lock-in, deep code access, and limited ecosystem reach. It’s a glimpse of where the industry is headed, but one that demands full trust in a closed ecosystem.
7. New Relic AI & AIOps
New Relic, one of the original APM pioneers, now pairs its mature AIOps engine with a new generative assistant called New Relic AI.
The AIOps side, rebranded as “Applied Intelligence”, focuses on anomaly detection and alert correlation, while New Relic AI brings natural language interaction directly into the UI.
Like the other tools on this list, its goal is to make observability more accessible by turning plain-English questions into NRQL queries and readable summaries.
What’s good
New Relic AI is a useful co-pilot that lowers the barrier for anyone unfamiliar with NRQL. You can ask it to summarize dashboards, interpret stack traces, or identify missing alerts, and it returns clear explanations.
Behind it, the Applied Intelligence engine remains one of the most reliable systems for detecting anomalies and cutting through alert noise. It’s a battle-tested feature many enterprises still rely on.
The catch
The AI experience feels more bolted on than built in. The co-pilot and AIOps layers work side by side rather than as one unified system, giving it the feel of a legacy platform trying to modernize.
It’s also tightly coupled to New Relic’s proprietary data and agents. While OpenTelemetry data is accepted, it isn’t the native format, and the assistant’s insights are strongest when you’re fully inside the New Relic ecosystem.
The verdict
New Relic’s AI features are dependable and genuinely helpful, especially for teams already invested in its platform. But it’s an incremental evolution, not a reinvention. It makes a legacy system easier to use, not fundamentally smarter. For teams looking for open, AI-native observability, it’s solid but still built on old foundations.
Final thoughts
AI is reshaping observability, but not all innovation is equal. What we’re seeing today isn’t just a race to build smarter assistants—it’s a split between two philosophies.
On one side are the legacy giants. They’ve built powerful, enterprise-grade systems, but their AI features mostly serve to make already complex platforms easier to use. They deliver automation, but at the cost of deeper lock-in and rising bills.
On the other side are the new players. Platforms like Dash0 are rethinking the entire experience from the ground up to be AI-native, OpenTelemetry-first, and transparent by design.
They’re not layering AI on top of old data models but building on a foundation of 100% native OpenTelemetry, PromQL, and Perses. This proves you no longer have to choose between a powerful AI and an open, flexible ecosystem.
The direction of travel is clear: the future of observability isn’t about collecting more data or stacking on more dashboards. It’s about context, reasoning, and collaboration. The winning tools will be those that make engineers feel amplified, not replaced.
Thanks for reading!
