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We ran analysis across 12 million logs and the number that keeps stopping us is 78%. That's the share of AI agent failures that never produce an error, never spike latency, never trigger an alert. The agent just returns a wrong answer and the user walks away. Datadog wasn't built to catch that. And neither was most of what gets recommended as a Datadog replacement.
Most Teams Are Looking for the Wrong Thing When They Switch
Datadog is genuinely good at what it does. For infrastructure observability, cloud metrics, log aggregation, and distributed APM across traditional services, it's hard to beat. Teams with mature infra practices built around Datadog dashboards are getting real value from that depth.
Teams usually start shopping for one of three reasons.
The first is cost predictability. Datadog's per-host and per-log billing compounds fast as you scale. Most teams see invoices two to three times higher than their first estimate because the billing model compounds aggressively as infrastructure scales. Custom metrics are billed at $5 per 100 metrics per month, and distribution or histogram metrics carry a 5x multiplier compared to standard gauges, which catches teams off guard. If cost is the main frustration rather than features, see our Datadog pricing breakdown first, then come back.
The second is fit. Traditional APM tools like Datadog were built for deterministic systems and excel at tracking request-response patterns, but AI agents break this model in several fundamental ways. Metrics and traces surface crashes and latency. They don't surface hallucinations, tool misuse, user frustration, or agent forgetfulness. Those are the failure modes that actually drive churn in production AI products.
The third is lock-in anxiety. Datadog's proprietary agents, custom ingestion pipelines, and dashboard formats create switching costs that grow over time. Teams expecting to scale prefer to build on open standards like OpenTelemetry before those costs become painful.
Those three triggers map to two different categories of alternatives. The first is infra APM replacements: tools like Grafana, Dynatrace, and New Relic that solve the cost and lock-in problems but leave the AI observability gap wide open. The second is AI-agent-native monitoring: tools built specifically to detect failures that never produce an error. If you're running AI agents in production, you probably need one from each column, not just a Datadog clone. Only 5% of AI agents that reach production have mature monitoring in place, with teams still focused on surface-level response quality instead of deeper reasoning and precision control.
The Six Evaluation Criteria That Actually Matter
Criteria for an AI agent team look different from criteria for a pure infra team.
Sampling vs. full capture. Can the tool classify and index every log at production scale, or is it working on a sampled slice? AI workloads generate non-deterministic failures that traditional sampling cannot capture. Silent agent failures are rare events in the long tail of your traffic. Sampling misses them by design.
Replay and step-level debugging. Can you replay execution from an intermediate agent step, not just view a linear trace? Multi-step agents can fail at step 4 of 12 and the downstream steps will look completely fine. Without the ability to fork from the failure point, root cause analysis is guesswork.
Silent failure detection. Does the tool detect wrong answers, hallucinations, and user frustration, or only crashes and latency spikes? AI agents can return confident, well-formatted output while getting the answer completely wrong, with errors propagating silently across downstream steps.
Evaluation methodology. There's a real difference between a generic LLM-as-judge prompt and a model fine-tuned on your specific traffic patterns. Generic judges produce inconsistent false positive rates. Models trained on your agent's behavior are calibrated to what "wrong" actually looks like for your use case.
Hosting model. SaaS versus self-hosted matters for data residency, compliance requirements, and cost structure. Know your constraints before you start evaluating.
Integration surface. OpenTelemetry support, SDK language coverage, and the actual migration lift to get your first trace flowing. The smaller that lift, the faster your proof of concept.
Before signing any contract, run these three tests on every tool you shortlist: send a known hallucination through the system and verify it gets flagged; attempt to replay a failed multi-step agent run starting from a middle step; and confirm whether the tool ingests 100% of your traffic or works on a sample. Those three tests will rule out more tools faster than any demo.
If your concern is primarily infra cost and lock-in, focus on Group 1 below. If you're running AI agents in production, focus on Group 2 and keep something lightweight from Group 1 running alongside it.
Infra APM Replacements: Solving the Bill Without Solving the Real Problem
These tools fix the cost and lock-in problems. They don't fix the AI agent observability problem. None of them do semantic failure detection, replay from intermediate agent steps, or hallucination classification.
Grafana + Prometheus
Best for: Teams who want open-source infrastructure monitoring with full control over their stack.
Grafana paired with Prometheus is the most common self-hosted alternative to Datadog for infrastructure metrics. Prometheus handles metric collection and storage; Grafana handles dashboarding and alerting. Together they cover host metrics, service health, and custom instrumentation with no per-host licensing cost. A strong community ecosystem means integrations exist for almost every infrastructure component. The migration lift is real: dashboard recreation takes time and alert rule migration from Datadog's syntax requires effort. The downside is operational overhead. You're running the infrastructure for your infrastructure monitoring, which adds engineering burden that Datadog's SaaS model absorbs.
Pricing: Open source. Infrastructure costs only.
Dynatrace
Best for: Large enterprises that want AI-assisted root cause analysis across complex microservice environments.
Dynatrace positions directly against Datadog with automated dependency mapping and its Davis AI engine for root cause analysis. It covers APM, infrastructure, digital experience, and cloud automation in one platform. For enterprises running complex, deeply interconnected services, the automated topology discovery cuts down on manual instrumentation work. The con is pricing: Dynatrace enterprise contracts aren't cheap, and the complexity of the platform means a longer time-to-value for smaller teams.
Pricing: Enterprise pricing, custom contracts. No public free tier for production use.
New Relic
Best for: Teams that want a broad observability platform with a generous free tier and straightforward per-user pricing.
New Relic moved to a consumption-based model that many teams find more predictable than Datadog's per-host approach. It covers APM, infrastructure, browser monitoring, and synthetics. The 100GB per month free data ingest tier makes it accessible for teams that want to run a real proof of concept without committing budget. The platform is broad rather than deep; teams with specialized needs often find they need to customize heavily. Like all infra APM tools, it doesn't surface AI agent semantic failures.
Pricing: Free tier available. Paid tiers start around $0.30 per GB of data ingested beyond the free allowance.
Elastic Observability and Amazon CloudWatch
For completeness: Elastic Observability is a strong option for teams already running the Elastic stack for search or log analytics, consolidating into a single platform. Amazon CloudWatch is the pragmatic choice for AWS-native teams who want observability without adding a third-party vendor. Both cover infra metrics, logs, and basic APM. Neither is purpose-built for AI agent monitoring.
Infra Tooling Leaves a Blind Spot That Will Surprise You
The infra replacements above solve the billing problem. They leave a blind spot that's larger than most teams realize until they're fielding user complaints about an agent that, by every metric, looks perfectly healthy.
Our analysis across 12 million logs found that hallucinations were the top failure category, user frustration was second, and agent forgetfulness or laziness was third. Together with explicit tool call failures (22% of issues), these categories account for the full picture of what goes wrong with production AI agents. The 78% majority are silent. APM tools treat AI like any other service: they capture latency and token counts but don't evaluate whether the model's response was faithful, relevant, or safe. AI agents may follow incorrect reasoning paths even when all traditional observability metrics appear healthy, causing legacy APM dashboards to fail at reflecting true model performance.
Elite teams that adopt comprehensive evaluation and observability approaches achieve 2.2x better reliability than non-elite teams, reaching the highest reliability levels 70% of the time compared to just 32%. The tools in this section are built to close it.
LangSmith
Best for: LangChain-native teams who want traces tied directly to their chain definitions.
LangSmith is LangChain's native observability layer. If your agent stack is built on LangChain, the integration is close to zero-effort: traces map directly to your chain and tool definitions, and the debugging interface is designed around the LangChain mental model. It covers tracing, dataset management, and evaluation runs. The con is ecosystem lock-in: LangSmith works best if you're staying in the LangChain universe. Teams running custom Python agents or multi-framework stacks find the fit weaker. Evaluation depth is limited to the primitives LangChain exposes, and large-scale automated classification across millions of production logs isn't its primary design target.
Pricing: Free tier available. Cloud pricing is usage-based. Self-hosted options exist.
Langfuse
Best for: Teams that want self-hosted LLM observability with open-source flexibility.
Langfuse is one of the most popular open-source options in this category and was widely adopted in the early LLM application era. It covers tracing, prompt management, and evaluation scoring. Self-hosting makes data residency requirements easier to meet, and the open-source model means teams can extend it for their use case. Langfuse's core model is input, LLM decision, output. As agents have grown more complex, with hundreds of tool calls running over extended sessions, the interface shows its origins: it surfaces logs and end-to-end traces well, but deeper customization and semantic classification require building on top of it rather than getting it out of the box. Teams that need more than log visibility tend to outgrow it as agent complexity scales. See our Guardy vs Langfuse comparison for a detailed side-by-side.
Pricing: Open source (self-hosted free). Cloud pricing usage-based.
Arize AI
Best for: ML platform teams expanding into LLM evaluation with existing Arize infrastructure.
Arize started in traditional ML model monitoring and has extended its platform to cover LLM tracing and evaluation. Teams already running Arize for their ML models benefit from a unified platform across model types. The evaluation tooling is mature, and the Phoenix open-source project provides a self-hosted tracing option. The con is that Arize's roots are in structured ML monitoring: the platform's mental model fits teams treating LLMs as models to be evaluated better than it fits teams debugging multi-step agent workflows in production. For a direct comparison, see our Arize vs Guardy analysis.
Pricing: Free tier via Phoenix (open source). Enterprise pricing on request.
Helicone and Confident AI (DeepEval)
For budget and open-source completeness: Helicone is a lightweight LLM proxy that logs requests and responses with minimal setup, making it a reasonable starting point for teams that just need visibility into LLM calls before they've defined a full observability strategy. Confident AI, built on the DeepEval framework, focuses on evaluation metrics and testing rather than production monitoring. Both are accessible starting points; neither is designed for the scale and semantic classification depth that production agent teams eventually need.
Guardy: Built Around the Failures That Never Make Noise
Best for: Engineering teams running production AI agents who need to detect and diagnose failures that never throw an error.
Datadog tells you when your agent crashes. It can't tell you when your agent confidently gives a wrong answer, forgets prior context, or routes a user in circles for six turns before they give up and leave. Those failures are invisible to infra APM, and they're the ones that actually drive churn.
Guardy covers the full observability loop: what the agent did (session-level tracing of inputs, outputs, latency, and token costs at every step), whether it did it well (automated evaluations), and what to do when it didn't (real-time Slack alerts with source-code-level failure pinpointing and fix suggestions).
We classify every log, not a sample. Silent failures are rare events distributed across your full log volume. If you sample, you miss them. Across 12 million logs analyzed, we run full classification without passing every log through a large LLM. That's possible because we use post-trained models fine-tuned on each customer's specific traffic patterns, not a generic judge prompt. The accuracy difference is meaningful in practice, and the cost per log is much lower than LLM-as-judge approaches.
We support custom classifier instantiation. Teams can define any failure mode they want to track, check three or four example logs to calibrate, and deploy a fine-tuned lightweight classifier in under a minute. A finance customer used this to track mismatched GL codes, a failure that end-state checking couldn't reliably catch because the agent's output varied across hundreds of intermediate paths. Built-in classifiers cover hallucinations, bad tool calls, agent forgetfulness, and jailbreaking out of the box.
We support replay and fork from any intermediate step in an agent run, not just trace viewing. When a failure occurs at step 7 of 14, you can branch execution from that state, test a fix, and validate it without re-running the full session.
Integration is five lines of code. We support OpenTelemetry, LangChain, LangGraph, and custom Python agents. For a Fortune 1000 customer running a mix of custom Python and LangChain agents across supply chain, HR, and marketing workflows, their error rate dropped from over 20% to under 10% within a single week of deployment.
The downside: Guardy is purpose-built for AI agents. It doesn't cover host metrics, network monitoring, or log aggregation for non-AI services. If you need that coverage, you'll want a tool from Group 1 running alongside it. Our customers typically run both.
Pricing: Usage-based. Contact us at tryguardy.com for current rates.
Comparison Table: All Alternatives at a Glance
| Tool | Best For | Infra APM | Silent Failure Detection | Sampling vs. Full Capture | Replay / Step Debug | Hosting Model | Pricing Signal |
|---|---|---|---|---|---|---|---|
| Datadog | Full-stack infra and APM | Strong | No | Sampled | No | SaaS | Per-host + per-log; scales aggressively |
| Grafana + Prometheus | Open-source infra monitoring | Strong | No | Sampled | No | Self-hosted | Open source |
| Dynatrace | Enterprise APM with AI root cause analysis | Strong | No | Sampled | No | SaaS / hybrid | Enterprise contracts |
| New Relic | Broad observability with free tier | Strong | No | Sampled | No | SaaS | Free tier; ~$0.30/GB beyond |
| Elastic Observability | Teams already on the Elastic stack | Strong | No | Sampled | No | SaaS / self-hosted | Usage-based |
| Amazon CloudWatch | AWS-native teams | Strong | No | Sampled | No | SaaS (AWS) | AWS consumption pricing |
| LangSmith | LangChain-native tracing | No | Partial | Sampled | Partial | SaaS / self-hosted | Free tier; usage-based |
| Langfuse | Self-hosted LLM observability | No | Partial | Sampled | No | SaaS / self-hosted | Open source; cloud usage-based |
| Arize AI | ML teams expanding into LLM evals | No | Partial | Sampled | Partial | SaaS / self-hosted | Free tier (Phoenix); enterprise pricing |
| Helicone | Lightweight LLM request logging | No | No | Sampled | No | SaaS / self-hosted | Free tier; usage-based |
| Guardy | Production AI agent failure detection | No | Yes (full semantic classification) | Full capture | Yes (fork from any step) | SaaS | Usage-based |
For AI agent teams, the two columns that matter most aren't pricing tiers. They're whether the tool can detect a wrong answer and replay from the step that caused it.
How to Migrate Without Breaking Production
That's a problem because switching cost is exactly what teams at this stage are weighing. Here's how to do it without a hard cutover.
The recommended approach is coexistence, not replacement. Keep Datadog running for infra metrics and host-level alerts during the transition. Add an AI-agent-native monitoring layer in parallel, using OpenTelemetry's vendor-neutral tracing and GenAI semantic conventions as the portability layer that limits lock-in risk from day one.
A staged rollout typically runs four weeks.
Week 1: Instrument the new tool alongside the existing Datadog agent. For infra replacements, this means deploying Prometheus exporters or the new vendor's agent in parallel. For AI agent tools, this means dropping in the SDK integration. For Guardy, that's five lines of code using OpenTelemetry or the LangChain/LangGraph integration.
Week 2: Validate parity on infra signals and begin routing agent traces to the new layer. Confirm that the same alerts firing in Datadog are firing in the replacement. Don't deprecate anything yet.
Week 3: Run both in parallel and compare alert coverage. For AI agent tools specifically, this is when you'll start seeing failures that Datadog was never surfacing. Keep a log of the delta coverage.
Week 4: Consolidate dashboards and deprecate redundant Datadog monitors. If cost reduction is the goal, this is when you start scaling down Datadog's ingestion to only what it handles uniquely.
The migration lift differs by tool type. For infra replacements, the main work is dashboard recreation and alert rule migration. For AI agent tools, the work is instrumentation plus building your evaluation taxonomy: deciding what counts as a failure for your specific agent. Custom classifier instantiation in Guardy cuts that second piece of work significantly. Teams can define a new failure mode against three or four example logs and have a deployed classifier in under a minute, rather than spending days writing eval uses.
For more on what a complete AI agent observability setup looks like once you've migrated, see our AI agent tracing guide and our best practices for agentic AI observability in production.
The Cases Where Staying on Datadog Is Actually Right
Datadog is the right call if you're a large enterprise with existing contracts, a mature infra team, and deep workflow integrations across your engineering organization. The switching costs are real, the workflow integration value is real, and if your AI usage is still experimental or pre-production, the agent observability gap simply doesn't hurt yet.
It's also the right call if you need a single pane of glass across infra, APM, real user monitoring, and security, and your budget can absorb the cost. Datadog's breadth is hard to replicate with a pieced-together stack.
AI workloads generate 10 to 50 times more telemetry than traditional services, so teams are often blindsided when they add LLM monitoring to their Datadog bill. If that's your situation and cost is the only trigger, look at the infra replacements in Group 1 first.
Datadog is good. The question is whether its observability model matches your actual failure modes. If your agents are in production and your error rate looks fine but your users are complaining, Datadog is the wrong tool for that problem. It was built for a different class of failure.
FAQ
Why look for Datadog alternatives for ML and LLM applications?
Datadog was designed for deterministic systems where failures produce errors, latency spikes, or crashes. LLM applications and AI agents fail differently: an agent can complete a run successfully by every infrastructure metric while giving a wrong answer, hallucinating a fact, or forgetting context from earlier in the session. Those failures are invisible to APM tooling. Teams running production AI applications need tools that evaluate what the agent said, not just whether it responded.
What is the best free alternative to Datadog?
For infrastructure monitoring, Grafana paired with Prometheus is the most widely adopted open-source option. It covers host metrics, service health, and custom dashboards with no licensing cost, though you absorb the operational overhead of running it. For LLM observability, Langfuse and Arize's Phoenix project both offer self-hosted open-source tiers. For teams that want to start quickly with minimal setup, New Relic's 100GB per month free data ingest tier is a practical SaaS entry point.
Who is Datadog's biggest competitor?
In traditional infrastructure APM, Dynatrace and New Relic are Datadog's most direct competitors by market position and feature breadth. For AI agent observability specifically, the competitive set is different: LangSmith, Langfuse, Arize, and Guardy are the tools purpose-built for the failure modes that production AI teams actually face.
Why is Grafana better than Datadog?
"Better" depends entirely on your context. Grafana isn't better at the breadth of integrations, enterprise support, or out-of-the-box coverage that Datadog provides. What Grafana offers is cost: it's open source, which means no per-host licensing, no per-log ingestion charges, and full control over your data. For teams with strong infrastructure engineering capacity and cost sensitivity, the total cost of ownership can be much lower. For teams that want managed SaaS and broad coverage without internal operational overhead, Datadog's value proposition still holds.
Does Datadog have a future?
Yes. Datadog is a large, well-resourced company with strong infra APM coverage and an expanding product surface. The question isn't viability, it's fit. As AI agents become core production infrastructure, the observability category is splitting: infra APM handles what it always handled, and a new category handles semantic AI behavior. Datadog is investing in AI monitoring features, but the architectural assumptions of APM, that failures produce measurable signals, aren't the right foundation for catching wrong answers and hallucinations at scale. Whether Datadog closes that gap over time is worth watching; for teams with AI agents in production today, the gap exists right now.
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