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Most teams shopping for Dynatrace alternatives are shopping for two different things at once and don't realize it. One is a better APM platform. The other is observability for production AI agents, which Dynatrace was never built to handle and isn't rushing to fix. We've seen this confusion send teams to the wrong tool repeatedly, so this article covers both tracks separately.
Why Teams Start Looking in the First Place
Dynatrace earns its reputation. The autodiscovery engine handles complex enterprise environments without manual instrumentation, Davis AI surfaces anomalies before you go looking for them, and the full-stack coverage from infrastructure through application traces is impressive at scale. For traditional software observability, it's near the top of its category.
The friction points are specific. Licensing at enterprise scale gets expensive and hard to forecast as workloads grow. For smaller teams or startups, the onboarding curve is steep relative to what they need. And for teams building and running production AI agents, Dynatrace hits a structural ceiling: no session-level LLM tracing, no automated evaluations for hallucinations or tool failures, no prompt A/B testing. That gap isn't closing, because Dynatrace was built for a different problem entirely.
So there are two kinds of switchers. Infra and APM teams want something more cost-predictable or OpenTelemetry-native. AI engineering teams need agent observability that traditional APM tools were never designed to deliver. Our data across 12 million logs shows roughly 78% of agent failures aren't crashes or API errors. They're silent regressions: hallucinations (the top failure mode), user frustration, and agent forgetfulness or laziness. Traditional observability tools, including Dynatrace, miss this entire category because they were built for deterministic software. An agent's prompts, memory, and retrieval context vary on every run, so the same input can fail twice in two different ways.
What to Evaluate, by Track
For the infra and APM track, the standard criteria apply: OpenTelemetry and OTLP compatibility, deployment model, ingest and retention pricing, integration breadth, and alerting flexibility.
For the AI agent track, the criteria are different, and most comparisons don't weight them at all.
Session-level trace fidelity means capturing inputs, outputs, token costs, and latency at every step, rather than generic spans that only show an LLM call happened. Without this, you can't replay what the agent did or diagnose why a specific run went wrong.
Automated evaluation coverage means the platform runs classifiers against completed traces to detect hallucinations, bad tool calls, goal abandonment, retrieval failures, and behavioral drift, without requiring you to write every rule by hand.
Behavioral alerting means alerts that carry context about what the agent did wrong, instead of a bare threshold breach. Infrastructure alerting without behavioral context sends you to the wrong place every time.
Code-level debugging with fix suggestions means the gap between "something went wrong" and "here is the exact code and prompt change to fix it" is closed inside the platform rather than assembled by hand across three tools.
"OTel-native for agents" also needs defining precisely. It means step-level input/output capture, token accounting per step, cost attribution, and trace continuity across tool calls and model invocations. It's not the same as an APM tool that says it traces your app and happens to log HTTP calls to an LLM API.
Figure out which track you're on before reading further; the recommendations diverge completely.
Sentrial: Purpose-Built for Production AI Agent Observability
Best for: Engineering teams running production AI agents who need tracing, evaluations, alerting, and debugging in one platform without stitching tools together.
We built Sentrial specifically because traditional observability tooling fails production AI agents in a predictable and costly way. We've seen this firsthand with Fortune 1000 customers running LangChain and custom Python agents for supply chain, HR, and marketing workflows. Their existing monitoring stacks could show logs, API calls, and infrastructure metrics, but couldn't explain why an agent selected the wrong vendor, hallucinated context, skipped workflow objectives, or silently degraded after a prompt change.
In Sentrial, every user interaction becomes an execution graph. Sessions contain traces that represent execution flows, and traces contain spans for each operation: LLM calls, tool invocations, retrieval steps, retries, and the rest. We capture inputs, outputs, token costs, and latency at every step.
On top of tracing, we run automated evaluations against completed execution traces. We ship built-in classifiers for hallucinations, bad tool calls, agent forgetfulness, and jailbreaking. We also support custom classifier instantiation: teams check three or four example logs, and we deploy a fine-tuned classifier in under a minute. One finance customer instantiated a mismatched GL codes classifier this way, a failure mode no generic classifier would ever catch. We classify every interaction, not a sample, because missing a percentage of logs is exactly how silent failures compound undetected.
When failures occur, real-time Slack alerts include source-code-level failure pinpointing and fix suggestions. Our GitHub-aware debugging workflow turns a production failure into a diff, a suggested patch, or an open pull request. We also support replay and fork from any intermediate step in an agent run, so engineers can isolate exactly where reasoning diverged without reproducing the issue from scratch. One customer reduced their error rate from 20% to under 10% in a single week using this workflow.
Prompt A/B testing with statistical rigor runs in production, not in a pre-release eval environment. We integrate in minutes via OpenTelemetry, LangChain, LangGraph, or custom Python agents with five lines of instrumentation code.
Pricing: Usage-based. See sentrial.com for current plans.
Cons: Sentrial is built for AI agent observability, not as a Dynatrace replacement for pure infrastructure or APM monitoring. If your stack has no LLM workloads, this isn't the right fit. We're also newer than the established APM players, which matters to teams that need a long vendor history for procurement.
Datadog: Worth It If You're Already in Their Ecosystem
Best for: Teams with existing Datadog infrastructure instrumentation who want to add LLM observability without introducing a new vendor.
Datadog's LLM Observability product extends its unified platform to cover LLM traces alongside infrastructure metrics, APM, and logs. For teams already operating within the Datadog ecosystem, the integration argument is strong: one dashboarding layer, one alert workflow, one vendor relationship.
The broader platform strengths carry over here. Datadog has one of the widest integration libraries in the market, strong enterprise compliance features, and alerting that most ops teams already know how to configure. OTel compatibility has improved, and the deployment model is SaaS with enterprise agreements for large accounts.
For AI agent teams, the gaps show up fast. LLM evals in Datadog are less mature than specialized tools. There's no built-in prompt A/B testing beyond manual comparison, and evaluation coverage for hallucinations, tool failures, and behavioral drift requires significant custom configuration to approach what specialized platforms provide out of the box. Pricing complexity at scale is a known friction point, covered in depth in our Datadog pricing article. Our Datadog alternatives roundup goes further on what Datadog misses for AI agent workloads.
New Relic: The Clearest Upgrade If Pricing Is Your Main Complaint
Best for: Infra and APM teams switching from Dynatrace primarily for pricing model reasons, who want full-stack coverage in one platform at a predictable cost.
New Relic's user-based pricing model is the sharpest contrast to Dynatrace's licensing structure. A generous free tier covers up to 100GB of data ingest per month, and the all-in-one platform spans infrastructure monitoring, APM, distributed tracing, log management, and browser monitoring without requiring a separate product for each layer.
For traditional observability workloads, New Relic is a strong contender. OTel support is solid, the alerting system is mature, and the platform is generally easier to onboard than Dynatrace for teams without dedicated observability engineers.
For AI agent workloads, New Relic's capabilities are shallow. There's an AI monitoring product that captures LLM call traces and token usage, but evaluation depth, automated classifiers for behavioral failures, and agent-specific alerting are all limited compared to dedicated agent-observability platforms. For teams whose primary use case is infrastructure with some LLM API calls to monitor, this may be enough. For teams running complex multi-step agents in production, it isn't.
Grafana Stack: The Right Call If You Want Full Control
Best for: Infrastructure teams who want full control over their observability stack, are comfortable with operational overhead, and want to avoid vendor lock-in entirely.
The Grafana stack, combining Grafana dashboards with Prometheus for metrics, Tempo for distributed tracing, and Loki for log aggregation, is the leading open-source path away from Dynatrace. Every component is OTel-native, the community is massive, and teams that already use Prometheus face a familiar migration. No vendor lock-in, data lives where you choose, and cost is a function of your own infrastructure rather than a vendor's pricing model.
Grafana Cloud provides a managed option for teams that want the same stack without the operational overhead, with a free tier and usage-based pricing beyond it.
You pay for that control in operational overhead. Self-hosting the full stack requires dedicated engineering time to operate reliably. And for AI agent observability, every capability from the criteria list above is a build-it-yourself project. The Grafana stack can show you that something happened. It doesn't tell you whether what happened was correct.
Elastic Observability: Strong If Logs Are Your Primary Workflow
Best for: Organizations already running Elastic or the ELK stack where log search, correlation, and analytics are the primary observability workflow.
Elastic Observability extends the Elastic platform into APM, distributed tracing, and infrastructure monitoring, with OTel support and an ML-based anomaly detection layer built on the underlying Elasticsearch engine. For teams that live in Kibana and whose primary workflow involves searching and correlating high-volume logs, this is a natural extension rather than a replacement.
Elastic Cloud provides a managed deployment option. Self-hosted deployments remain common in enterprises with strict data residency requirements, though operational complexity for self-hosted clusters is a well-documented challenge.
For AI agent observability, Elastic isn't a first-class fit. None of the agent-track criteria are covered, same as Grafana. The ML anomaly detection works well for infrastructure and application signals but doesn't model the behavioral quality signals specific to LLM outputs. Teams evaluating Elastic as a Dynatrace alternative should treat it as an infra-track option only.
Langfuse: The Easiest Entry Point for LLM Tracing on a Tight Budget
Best for: Early-stage AI teams who need LLM-specific tracing and prompt management with minimal upfront cost, including self-hosted deployment options.
Langfuse is the gentlest on-ramp from generic APM tooling to LLM-specific observability. It's open-source, self-hostable, and offers a free cloud tier that covers most early-stage usage. Community adoption among teams building their first production LLM features is strong, and the trace/session/span hierarchy is designed for LLM workflows rather than adapted from infrastructure tooling.
Core capabilities include session-level tracing, prompt management with versioning, and basic scoring and evals. For teams that need to start capturing what their LLM is doing in production without budget for a paid platform, Langfuse is a reasonable starting point.
It runs out of road as agent complexity grows. Automated classifiers for hallucinations, tool failures, or behavioral drift aren't built in. Alerting capabilities are limited. There's no source-code-level debugging or fix suggestions, and prompt comparisons aren't statistically testable against production traffic. Our Sentrial vs Langfuse comparison walks through the production-depth gap. For Langfuse pricing specifics, we have a dedicated breakdown.
Pricing: Free self-hosted tier. Langfuse Cloud has a free tier with paid plans beyond it.
Arize Phoenix: The Right Choice If Eval Depth Is What You're After
Best for: Teams whose primary need is rigorous LLM evaluation, dataset management, and LLM-as-a-judge workflows, particularly in research-adjacent or pre-production contexts.
Arize Phoenix is open-source and provides strong tracing, an evaluation framework, and dataset management tooling. The LLM-as-a-judge workflows are more developed than most alternatives at a comparable price point, and OTel compatibility means integration with existing instrumentation is straightforward. Arize AX, the enterprise tier, extends this into production monitoring with more reliable management features.
For teams that need to build and iterate on evaluation datasets, benchmark model versions, or run systematic evals across retrieval pipelines, Phoenix is a serious contender.
Production alerting depth and code-level debugging are less developed than platforms built for production monitoring. The trace-to-fix workflow requires more manual steps, and real-time behavioral alerting on production agent runs is limited compared to what we offer in Sentrial. Our Arize vs Sentrial comparison breaks this down head to head.
Pricing: Phoenix is open-source and free. Arize AX is enterprise-priced; contact Arize for current quotes.
Dynatrace Alternatives at a Glance
| Tool | Best For | OTel Support | AI Agent Evals | Alerting Type | Deployment | Pricing Model |
|---|---|---|---|---|---|---|
| Dynatrace | Enterprise full-stack APM | Partial | None | Infra/APM anomalies | SaaS + managed | License-based |
| Sentrial | Production AI agent observability | Yes | Yes (built-in + custom) | Behavioral + error (Slack, code-level) | SaaS | Usage-based |
| Datadog | Existing Datadog ecosystem | Yes | Partial | Infra/APM + basic LLM | SaaS | Dimension-based |
| New Relic | Cost-predictable full-stack monitoring | Yes | Partial | Infra/APM | SaaS | User-based |
| Grafana Stack | Open-source infra flexibility | Yes | None (build it yourself) | Infra/APM | Self-hosted + Cloud | Free / usage-based |
| Elastic Observability | Log-heavy enterprises | Yes | None | Infra/APM + ML anomaly | Self-hosted + Cloud | Usage-based |
| Langfuse | LLM tracing on a budget | Yes | Partial (basic scoring) | Limited | Self-hosted + Cloud | Free tier + usage-based |
| Arize Phoenix | Eval depth and dataset management | Yes | Yes (LLM-as-judge) | Limited in production | Self-hosted + Cloud | Free / enterprise |
How to Migrate Off Dynatrace
Migration splits cleanly along the two tracks, and the steps are different enough to cover separately.
Infra and APM migration
Dynatrace's autodiscovery means most teams have less direct instrumentation experience than they realize. When moving to an OTel-native tool like New Relic, Grafana, or Elastic, expect to spend time adding instrumentation that Dynatrace was generating automatically. Start by auditing which services Dynatrace's OneAgent was covering and map each to an explicit OTel SDK integration in the replacement tool. Export dashboards and alert definitions early, but plan to rebuild them in the destination platform rather than attempting a direct import. Run both systems in parallel for at least two weeks before cutting over to validate that the new tool is catching what Dynatrace was catching.
AI agent observability migration
If your reason for switching includes adding LLM observability, Dynatrace has no LLM-specific data to export, so there's nothing to migrate. The work is purely additive instrumentation.
With Sentrial, this takes about five lines of code. We use OpenTelemetry for initial logging, then run our analysis and classification on top. We automatically instrument LangChain, LangGraph, CrewAI, AutoGen, Claude Code, Vercel AI SDK, and Mastra, and we expose low-level APIs for custom spans in any Python agent. Trace context propagates across service boundaries, so one execution graph connects the frontend interaction to the retrieval call to the external API that actually failed.
Once data is flowing, deploying classifiers takes under a minute via the same three-or-four-example process described above. That works for built-in categories like hallucinations and bad tool calls, and for domain-specific failures like the GL codes classifier the finance customer used.
Validation
For the infra migration, compare alert trigger rates and error detection coverage between the old and new system during the parallel run. For AI agent observability, the validation baseline doesn't exist in Dynatrace because it never tracked these signals. Instead, run Sentrial for one week and review the behavioral failure breakdown: what percentage of sessions involved hallucinations, tool failures, or goal abandonment. That number is your new baseline, and improving it is the ongoing operational goal. Expect a chunk of what looked like healthy traffic to have failures underneath; our 78%-silent number came from exactly this kind of review.
Which Dynatrace Alternative Fits Your Team
The decision comes down to which track applies to you.
For infra and APM teams, the shortlist is Datadog if you're already in that ecosystem, New Relic if pricing predictability is the primary driver, Grafana if open-source control and self-hosting matter, or Elastic if log search and correlation are the dominant workflow. These tools don't meaningfully differ on AI agent observability because none of them cover it well. Pick based on your infra stack, budget, and operational capacity.
For teams running production AI agents who discovered that Dynatrace was never serving that use case, the shortlist is Sentrial for the full trace-to-eval-to-alert-to-fix workflow in one platform, Langfuse if budget is the primary constraint and you're early-stage, or Arize Phoenix if pre-production eval depth and dataset management are your priority over production alerting.
Most teams with both needs end up using separate tools for each, because the platforms that do infra APM well don't do agent evals well, and vice versa.
A few niche mentions: Splunk for large-enterprise log analytics at depth; Honeycomb if high-cardinality event tracing is your problem; SigNoz if you want the Datadog feature set, open-source, without the pricing model.
For a broader look at the AI observability landscape, our LLM observability platforms comparison and agentic AI observability guide cover the full picture.
FAQ
Who are Dynatrace's biggest competitors?
Dynatrace's primary competitors in the infrastructure and APM category are Datadog, New Relic, Elastic Observability, and Grafana. For enterprise procurement, Splunk also appears frequently in the same evaluations. For teams running production AI agents specifically, agent-focused platforms like Sentrial, Arize Phoenix, and Langfuse address observability gaps that none of Dynatrace's traditional competitors cover.
Which is better, Grafana or Dynatrace?
It depends on what you're optimizing for. Dynatrace has better autodiscovery, stronger out-of-the-box anomaly detection via Davis AI, and a more unified enterprise experience. Grafana offers full open-source control, lower licensing cost, and more flexibility for teams that want to own their stack. Grafana requires more operational investment to maintain, especially at scale. For AI agent observability, neither is purpose-built for the problem.
Why is Datadog better than Dynatrace for some teams?
Datadog's pricing model, while complex at scale, is more transparent than Dynatrace's enterprise licensing for many teams. Datadog also has a wider integration ecosystem, stronger developer adoption, and a faster-improving LLM observability product. Dynatrace's autodiscovery and Davis AI are real advantages for large enterprises with complex infrastructure and less instrumentation expertise. Teams that prioritize developer experience and integration breadth tend to prefer Datadog; teams that prioritize automated discovery and enterprise compliance often prefer Dynatrace. Our Datadog pricing breakdown covers what the cost difference actually looks like.
Is Dynatrace still worth using in 2026?
Yes, for the right use case. If you're running complex enterprise infrastructure, need automated discovery across hybrid environments, and value AI-assisted anomaly detection without requiring manual instrumentation, Dynatrace is still one of the best platforms available. The case for switching is strongest when licensing cost becomes a significant burden at scale, when your team needs a gentler onboarding curve, or when your workloads have shifted toward production AI agents that Dynatrace has no native support for.
What are the top Dynatrace competitors in 2026?
For infrastructure and APM: Datadog, New Relic, Elastic Observability, and Grafana. For production AI agent observability, a category Dynatrace doesn't serve: Sentrial, Arize Phoenix, and Langfuse. The distinction matters because teams searching for Dynatrace alternatives are often asking two different questions, and the answer depends on which problem they're actually trying to solve.
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