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We compared Datadog and Dynatrace across tracing, root-cause analysis, alerting, pricing, and AI agent observability, and the infrastructure stuff largely goes the way you'd expect. But for teams running production AI agents, both tools share a critical blind spot: they can tell you an agent crashed or ran slowly, but not that it hallucinated, called the wrong tool, or silently abandoned a user's goal. That blind spot is the actual subject of this comparison.
Quick Comparison
| Feature | Datadog | Dynatrace |
|---|---|---|
| Primary use case | Broad cloud infrastructure observability | Deep RCA in complex enterprise environments |
| Setup complexity | Low, fast agent install, developer-friendly | Higher, more opinionated; more configuration upfront |
| OpenTelemetry support | OTel-native with Collector support | OTel supported; instrumentation is more prescriptive |
| Tracing depth | Distributed traces, LLM span support via ddtrace | Traces correlated to infrastructure topology via Davis AI |
| Session-level agent tracing | Not native, requires custom instrumentation | Not native, requires custom instrumentation |
| Root-cause analysis | Watchdog anomaly detection; manual correlation across services | Davis AI: automated dependency discovery and causal chain analysis |
| AI/LLM monitoring | LLM Observability module: token usage, basic LLM spans, some automated checks | dt-evals for hallucinations and faithfulness in CI/CD; limited production coverage |
| AI agent behavioral evaluation (hallucinations, tool failures, goal abandonment) | Not covered | Not covered |
| Full log coverage vs. sampling | Sampling by default | Sampling by default |
| Alerting | Real-time alerts, Slack integration; infrastructure-focused context | Real-time alerts, Slack integration; infrastructure-focused context |
| Pricing model | Per-host plus per-SKU; costs compound as products are added | DPU (Davis Platform Units); usage-based but complex to forecast |
| Best for AI agent teams | Partial, infrastructure health only | Partial, infrastructure health only |
That last row is the one that matters for AI agent teams. Neither tool evaluates behavioral correctness in production. As Coralogix notes, a correct agent run and an incorrect one produce traces that look identical; traditional APM can't tell them apart. Across 12 million logs we analyzed at Sentrial, 78% of agent issues were silent regressions: hallucinations, user frustration, agent forgetfulness. Not crashes or tool failures that would trip an infrastructure alert.
Datadog Is Fast to Deploy and Good at Infrastructure. That's Also Its Ceiling.
Datadog is the platform most engineering teams reach for first, and for good reason. It covers metrics, logs, traces, and synthetics in one interface, installs quickly via a lightweight agent, and integrates with basically everything in the modern cloud stack. For teams that need visibility across mixed infrastructure fast, Datadog delivers.
The developer experience is good. The UI is well-organized, onboarding friction is low, and most teams see real data within hours. For infrastructure-heavy orgs that want broad coverage without committing to a long configuration project, Datadog is the natural default.
On the AI side, Datadog has invested in an LLM Observability product. According to Datadog's own documentation, it includes end-to-end LLM tracing, datasets, experiments, offline and online evaluations, prompt workflows, human review and annotation, and production monitoring. It automatically runs checks on model inputs and outputs like "failure to answer," and surfaces latency and token usage alongside infrastructure metrics.
The practical limitation is that these capabilities are built on top of an APM platform. As GoGloby observes, Datadog's evaluation depth is constrained because it wasn't purpose-built for quality scoring. Teams that need to catch hallucination drift will likely need to add a dedicated evaluation tool alongside it.
Pricing deserves a flag too: Datadog's per-host, per-SKU pricing model doesn't map cleanly to agent telemetry volume. Spans, token counts, and eval events scale differently than hosts and services. We cover the specifics in our Datadog pricing breakdown, but the short version is that adding LLM Observability on top of an existing Datadog contract can produce cost surprises for high-volume agent teams.
None of this takes away from what Datadog does well. For teams whose primary need is infrastructure observability with AI monitoring as a secondary concern, it's a legitimate, well-supported choice.
Dynatrace Goes Deeper on RCA. The AI Agent Coverage Ceiling Is Still There.
Dynatrace occupies a different part of the market. Where Datadog optimizes for broad coverage and fast onboarding, Dynatrace optimizes for depth in complex enterprise environments. Its Davis AI engine does something most platforms only claim: it automatically discovers dependencies, maps application topology via Smartscape, and performs causal chain analysis without requiring engineers to manually wire together dashboards. For large organizations with detailed service graphs, that automation has real value.
The tradeoff is configuration investment. Dynatrace's instrumentation is more opinionated than Datadog's, and getting the full benefit of Davis AI takes time. Teams starting from scratch in a simpler environment often find Datadog faster to get running. Dynatrace earns its complexity for organizations where the alternative is spending weeks manually building the dependency maps Davis generates automatically.
On OpenTelemetry, Dynatrace supports OTel data ingestion and its Grail data lakehouse handles log volume at enterprise scale. The OTel support is real, but it's more prescriptive than Datadog's in how instrumentation is expected to be structured.
For AI and LLM monitoring, Dynatrace has been adding features at a steady clip. Its platform monitors agent protocols, command execution, tool usage, and multi-agent communications, and it observes agent execution paths, tool invocations, and inter-agent communication. It's also added dt-evals for evaluating hallucinations and faithfulness. The gap is that these evaluations are primarily designed for CI/CD gates and scheduled runs. They're built to run before deploy or on a schedule, not against every production interaction as it happens.
One enterprise infrastructure company we worked with at Sentrial ran into exactly this. Their agents were handling high-stakes workflows (vendor evaluation, RFQ generation, autonomous purchasing decisions), and their existing monitoring stack could show logs, API calls, and infrastructure metrics. What it couldn't explain was why an agent selected the wrong vendor, hallucinated context, or silently degraded after a prompt change. Traditional observability was built for deterministic software. It doesn't account for probabilistic systems where every run can take a different reasoning path.
Dynatrace is a serious tool for serious infrastructure problems. The limitation here is specific: AI agent behavioral evaluation in production, not general observability quality.
Neither Platform Traces What's Happening Inside an Agent Session
"Tracing" means something different for multi-step AI agents than it does for traditional services. A conventional distributed trace captures span boundaries, service calls, and latency. An agent trace needs to capture what the agent decided at each step, which tool it called and why, how token costs accumulated across a multi-turn session, where retrieval succeeded or failed, and how intermediate results influenced the final output.
Both tools give you span chains. Datadog captures distributed traces and has LLM span support via ddtrace. Dynatrace correlates traces to infrastructure topology through Davis AI. Neither natively surfaces session-level agent behavior at the grain agent debugging requires. As CallSphere describes it, agent observability needs to tell you: "This agent made 3 LLM calls, invoked 2 tools, consumed 12,400 tokens costing $0.037, and the second tool call failed with a timeout before the agent self-corrected." That level of attribution doesn't emerge from either platform without significant custom instrumentation.
The OpenTelemetry GenAI Semantic Conventions define attributes for LLM calls, agent invocations, tool executions, and session-level metrics, a real step toward standardizing what agent traces should contain. Both Datadog and Dynatrace accept OTel data, but neither prescribes an agent-specific schema that makes these attributes useful out of the box. Teams end up writing custom instrumentation to preserve step boundaries in LangChain or LangGraph agents, attach token usage per step, and correlate tool call results back to reasoning outcomes.
For teams exploring what agent-native tracing looks like, our AI agent tracing explainer walks through what span design requires for multi-step agents. At Sentrial, every user interaction becomes an execution graph: sessions contain traces, and traces contain spans for each LLM call, tool invocation, retrieval step, and memory access. We automatically instrument LangChain, LangGraph, and other popular frameworks while exposing low-level APIs for custom spans. Token costs are tracked at every step rather than only at the session boundary, because that's where debugging happens. General-purpose APM wasn't designed to produce that.
On RCA, Dynatrace Wins for Infrastructure. Neither Wins for Agent Behavior.
For automated RCA on infrastructure, Dynatrace is the clear winner. Davis AI detects interdependent events across time, processes, hosts, services, and applications to pinpoint true root causes of problems in infrastructure and service topology, without requiring engineers to manually build correlation logic.
Datadog's RCA is more manual. Watchdog provides anomaly detection and can surface unusual patterns, but correlating root causes across services typically requires more engineering effort. SigNoz frames the distinction well: Dynatrace aims for full automation with Davis AI, while Datadog provides powerful tools for manual investigation. The right choice depends on whether your team prefers AI-driven decisions or human-in-the-loop control.
The shared limitation for AI agent teams is that neither Davis AI nor Watchdog was built to answer agent-specific RCA questions. Which service caused a latency spike is an infrastructure question. Which intermediate step produced a wrong answer, and what in the prompt or tool-call logic caused it, is a different question entirely. As research on hallucination detection notes, LLMs produce factual inaccuracies even when they appear confident because they're inferring likely continuations rather than grounding responses in verifiable evidence. That failure mode doesn't emit an error code, doesn't breach a latency threshold, and doesn't register in infrastructure topology. It only surfaces when you evaluate the output against correctness criteria.
We've seen this play out repeatedly: everyone blames the model first, and it's usually not the model. In production, failures more often come from bad retrieval, missing context, confusing tool schemas, weak escalation logic, or messy workflows. Agent-specific RCA, finding the exact prompt or tool-call logic responsible, requires a diagnostic layer that operates on behavioral signals, not infrastructure metrics. That's what we built at Sentrial: automated root-cause analysis that looks at model behavior, tool execution, and retrieval quality together, and explains why an agent failed even though every API call succeeded.
APM Tells You the Agent Responded. It Doesn't Tell You If the Response Was Right.
This is the gap neither platform was built to close.
Both Datadog and Dynatrace detect infrastructure anomalies reliably. What they can't do is evaluate whether an AI agent gave a correct, complete, non-hallucinatory answer. APM tells you the agent responded in 800ms with a 200 status code. Behavioral evaluation tells you the answer was wrong. As Braintrust puts it: an agent that loops, calls the wrong tool, or hallucinates an answer can still return a 200 response within normal latency, so APM reports a healthy system while agent quality quietly drops.
The specific failure modes neither tool catches in production:
- • Hallucinations: confident, well-structured responses that are factually wrong
- • Bad tool calls: selecting the wrong tool or constructing malformed tool arguments
- • Agent forgetfulness: losing context across turns in multi-step sessions
- • Goal abandonment: getting 90% of the way through a workflow and stopping or drifting
- • Jailbreak attempts: users redirecting agent behavior outside intended scope
That's the 78% from earlier broken down: hallucinations most common, then user frustration, then agent forgetfulness. Only 22% were explicit tool call failures that caused the agent to stop, the kind of failure that trips an infrastructure alert.
Prediction Guard describes the operational consequence clearly: an agent can return HTTP 200 with a hallucinated response, call an unauthorized tool while latency metrics stay flat, and drift from its governance policy baseline over weeks without triggering a single alert. Better models make this worse, not better. When agents were simple chatbots, failures were obvious. Now, agents run longer, use tools, touch workflows, and make decisions across multiple steps. An agent can sound right the whole time, cite the correct sources, and still take the wrong action at the end.
The most expensive single failure we've seen at Sentrial was with a Series B finance startup using AI for accounts receivable workflows. The agent was succeeding by every infrastructure metric: approximately correct prices, latency within bounds. It was hallucinating quote values, using LLM reasoning instead of extracting the data from the source PDF. It wasn't ingesting the source document at all. That failure went undetected because every APM dashboard stayed green.
We cover the full observability architecture needed to catch this class of failure in our agentic AI observability guide.
At Sentrial, we address this with built-in classifiers for the common failure modes (hallucinations, bad tool calls, agent forgetfulness, jailbreaking), plus a custom classifier system that lets teams define any failure mode specific to their agent. A finance company we work with instantiated a mismatched GL codes classifier. DevOps agent teams have instantiated classifiers for low-level details that no general-purpose tool tracks. Deploying one takes a few example logs and under a minute. We classify every interaction rather than sampling, because low-frequency, high-impact failures are exactly what sampling misses. For a deeper look at what LLM observability requires, see our LLM observability explainer.
Alerting and Pricing Both Have Hidden Costs for AI Agent Teams
Alerting: Both Datadog and Dynatrace support real-time alerts with Slack integrations, and for infrastructure monitoring, both do this well. The distinction for AI agent teams is triage completeness: does the alert contain the failing intermediate step, the relevant trace chain, and an actionable next step? Datadog and Dynatrace alerts point to infrastructure anomalies; they don't include the agent session context needed to triage a behavioral failure. An alert that says "error rate spiked on agent-service" isn't the same as an alert that says "hallucination rate on the vendor evaluation workflow increased 3x in the last hour, originating from step 4 of the retrieval chain."
At Sentrial, we run AI-powered issue detection against completed execution traces, analyzing the entire behavioral sequence of a session. Alerts include the specific failure classification, the session context, and source-code-level failure pinpointing with fix suggestions, because an alert without actionable next steps just adds noise.
Pricing: Datadog's per-host, per-SKU model means costs compound as you add products. GetPricePulse's analysis notes that Datadog bills can run 5-10x the advertised price once APM, logs, and infrastructure monitoring are combined. Dynatrace uses a DPU (Davis Platform Units) model, usage-based and fully inclusive per monitored host, but complex to forecast. Both pricing models are built around infrastructure telemetry volume: hosts, services, request rates. They don't account for agent spans, token counts, or eval events as first-class billing dimensions. Teams with high agent interaction volume can get burned on both platforms. We cover the Datadog-specific version of this in our Datadog pricing article. For teams evaluating alternatives, our Datadog alternatives comparison also covers pricing tradeoffs.
Practical fit summary: As Confident AI summarizes, for teams already running Datadog, adding LLM monitoring means zero new vendor procurement. The tradeoff is that AI observability becomes a feature module on a general-purpose APM platform, not a dedicated AI quality tool. Dynatrace makes the same tradeoff differently: more automation, more configuration investment, same coverage ceiling for behavioral evaluation.
How to Choose Between Them
Choose Datadog if: Your team needs broad observability across cloud infrastructure and wants fast time-to-value. You have a mix of traditional services and AI agents, and infrastructure health is your primary monitoring concern. You're already in the Datadog ecosystem and want to add LLM monitoring without a new vendor relationship. Developer experience and onboarding speed matter more than deep RCA automation.
Choose Dynatrace if: You're in a complex enterprise environment where automated dependency discovery and RCA are worth the setup cost. Your infrastructure is large enough that manually building correlation logic across services isn't feasible. You have an operations or SRE team that'll use Davis AI's automated diagnostics at scale and can absorb the onboarding curve.
Consider a dedicated AI agent observability platform if: Your team's primary monitoring gap is behavioral quality: knowing what the agent did wrong, at which step, and what to change, rather than just that something failed. Neither Datadog nor Dynatrace covers this out of the box. Gartner projects that by 2028, 40% of enterprise AI failures will trace to inadequate evaluation and monitoring of agent systems rather than model capability gaps, because most teams are applying traditional software testing practices to systems that are non-deterministic by design.
This is the gap Sentrial was built to fill. We give engineering teams running production AI agents the full observability stack: session-level tracing across every tool call and LLM decision, automated behavioral evaluation (hallucinations, tool failures, goal abandonment, jailbreaks, and custom classifiers for anything else), real-time Slack alerts with source-code-level failure pinpointing, and prompt A/B testing with statistical rigor. One Fortune 1000 customer we work with, running custom Python and LangChain agents for supply chain, HR, and marketing, reduced their agent error rate from 20% to under 10% in a single week once they had full behavioral visibility. That result comes from seeing where agents fail instead of where infrastructure metrics spike.
Datadog and Dynatrace are mature, well-resourced products that do infrastructure observability well. Sentrial doesn't replace general-purpose APM. It earns a place in this comparison because it solves the specific problem both leave open for AI agent teams.
Frequently Asked Questions
Datadog vs Dynatrace: which one is better for observability overall?
It depends on your environment and priorities. Datadog is better for teams that want broad coverage across cloud infrastructure with fast onboarding and a developer-friendly UI. Dynatrace is better when your environment is complex enough that automated topology mapping and AI-driven root-cause analysis (Davis AI) justify the heavier setup. If your primary observability need is AI agent behavioral quality rather than infrastructure health, neither tool was built for that use case.
Which tool is better for root-cause analysis?
Dynatrace is the stronger choice for automated RCA in infrastructure-heavy environments. Davis AI discovers dependencies automatically and performs causal chain analysis without requiring manual dashboard configuration. Datadog's Watchdog provides anomaly detection but typically requires more manual correlation work. For AI agent teams, both tools share the same limitation: they identify infrastructure-level root causes, not behavioral ones. They can't tell you which intermediate agent step produced a hallucinated answer or why a goal was abandoned mid-session.
Which is better for OpenTelemetry integration: Datadog or Dynatrace?
Both support OpenTelemetry, but with different approaches. Datadog is OTel-native with Collector support and generally lower friction for teams adopting OTel from scratch. Dynatrace supports OTel data ingestion but is more prescriptive about instrumentation structure. For AI agent teams, the more important question is what you instrument, not whether you can. The OTel GenAI Semantic Conventions define the attributes that matter for agent observability, but neither platform operationalizes those attributes into session-level agent traces without custom work.
Is Datadog cheaper than Dynatrace?
Not straightforwardly. Datadog's per-host, per-SKU pricing can compound quickly as you add products: APM, logs, infrastructure monitoring, and LLM Observability are separate line items. Dynatrace uses a DPU model that includes full-stack coverage per monitored host, which can be more predictable for large, stable infrastructure. For AI agent teams specifically, both pricing models are built around infrastructure telemetry volume and don't account well for agent spans, token counts, or eval events. See our Datadog pricing article for a detailed breakdown.
Which tool fits a mid-market team that needs to get set up quickly?
Datadog. Its agent install is lightweight, its integrations are broad, and most teams can get meaningful visibility within hours. Dynatrace's automation is more powerful in complex environments, but that power comes with a configuration investment that can slow down smaller teams. For a mid-market team running production AI agents, we'd also recommend layering in a dedicated agent observability tool alongside Datadog. Infrastructure monitoring and behavioral evaluation solve different problems, and treating APM as a substitute for behavioral evaluation leaves a gap in production visibility.
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