# 7 Best Datadog Alternatives for AI and Agent Observability > Source: > Published: 2026-08-26 20:01:00+00:00 # 7 Best Datadog Alternatives for AI and Agent Observability Comparing Datadog alternatives for AI and agent observability? See how Honeycomb, New Relic, Dynatrace, Grafana Cloud, Phoenix, Langfuse, and SigNoz stack up on cost, investigation, and OpenTelemetry support. By: [Kale Bogdanovs](/author/kale-bogdanovs) #### 15 Best AI Observability Tools for Production Teams in 2026 Compare the best AI observability tools for tracing, evals, token cost tracking, agent workflows, and production reliability in 2026. [Read More](/blog/best-ai-observability-tools) There isn't one obvious Datadog replacement. The right choice starts with why you're considering a switch: You may want observability costs that are easier to forecast as telemetry grows, your engineers may spend too much time connecting separate signals, or you may want instrumentation that stays portable between backends. AI and agent workloads make each concern more significant. Agents add model calls, tools, retrieval, high-cardinality context, and unpredictable production behavior. This guide compares seven Datadog alternatives against those needs. We cover production investigation, AI depth, telemetry economics, [OpenTelemetry](/platform/opentelemetry), deployment, and platform scope. ## Key takeaways - Teams often explore Datadog alternatives for three related reasons: [cost predictability](/use-cases/predictable-costs), fragmented investigations, and instrumentation portability. - Honeycomb is a strong Datadog replacement for teams prioritizing production observability, [high-cardinality](/resources/getting-started/understanding-high-cardinality-role-observability)investigation, and OpenTelemetry adoption. - AI raises the stakes because agents introduce more context, more unpredictable behavior, and more possible failure points across production. ## Why teams look for Datadog alternatives as AI workloads grow AI doesn't create every reason to reconsider your observability platform. It makes existing tradeoffs harder to ignore. Three concerns belong near the top of the evaluation: **Telemetry economics:** Production systems generate more telemetry as services, users, and AI workflows grow. Your pricing model determines how much context you can afford to retain and investigate.**Investigation complexity:** Debugging becomes harder when engineers must reconstruct a single production problem across separate signals, tools, or workflows.**Instrumentation portability:** OpenTelemetry gives teams more control over instrumentation. The practical question is which platform capabilities remain available without proprietary collection or schemas. AI adds another layer to each issue. One request may involve agents, models, retrieval steps, tool calls, retries, and downstream services. Similar inputs can also produce different outputs, and that unpredictability creates more unknown-unknowns: production behaviors you couldn’t define in a dashboard or alert beforehand. Engineers may need to investigate by user, session, prompt version, model, tool, tenant, or outcome. Those fields often contain thousands of values. The replacement should allow engineers to explore those dimensions when the question arises, and it should preserve enough context to explain what happened. OpenTelemetry can help keep instrumentation independent from the backend. Its Collector can export telemetry to multiple destinations. Honeycomb's [OpenTelemetry-native platform](/platform/opentelemetry) supports an open instrumentation approach across application and AI telemetry. ## What to look for in a Datadog alternative for AI and agent observability A useful shortlist starts with the problems you want the replacement to solve. Feature counts rarely show how production investigations work. Use the below questions to compare platforms. ### Can you investigate unknown-unknowns across AI and application behavior? An AI request rarely stays inside the model layer. It may touch APIs, databases, queues, vector stores, external services, and several agents. Your observability platform should let you investigate that behavior without knowing in advance which dimension will matter. Conversation views explain how an agent behaved. Distributed tracing explains how that behavior moved through the production system. Production teams often need both. ### Can you retain useful context without making costs harder to control? Rich telemetry only helps when teams can afford to keep and investigate it. AI workloads can add prompts, models, tools, users, sessions, evaluations, tokens, and other dimensions. Compare how each platform charges. Then, model the cost using your expected production volume. Pay attention to incentives. A pricing model can influence whether engineers keep useful context or remove it to control spending. ### How deep are the AI evaluation and quality workflows? System health tells you whether a request completed. It cannot tell you whether the answer was useful. Focused AI platforms may offer deeper evaluation and experimentation features. Broader production observability platforms may connect a poor result to problems outside the model layer. Prioritize the workflow creating the biggest investigation gap for your team. ### How portable is your telemetry, and how much platform breadth do you need? OpenTelemetry, a vendor-neutral framework, can reduce instrumentation dependence. It does not make every backend interchangeable. Ask which capabilities work with standard OpenTelemetry data, then identify anything that still requires vendor-specific instrumentation or schemas. Decide how much platform breadth you actually need. A broad suite may span infrastructure, applications, security, and AI, while a focused platform may go deeper on fewer engineering workflows. The important question is whether that scope matches the Datadog workloads you actually plan to replace. ## Best Datadog alternatives for AI and agent observability These seven platforms cover three common paths: broad suites, connected production debugging, and focused AI engineering. This list is not a ranking. Each platform fits a different mix of workloads, operating models, and investigation needs. Use these entries to compare strengths, tradeoffs, and fit before building your shortlist. ### 1. Honeycomb **Best for:** Teams replacing Datadog with an OpenTelemetry-based observability platform built for exploratory investigation across application and AI telemetry.**Why it stands out:** Honeycomb's event-based data model keeps rich production context queryable. Engineers can investigate high-cardinality fields without choosing every important dimension before something breaks. BubbleUp helps surface attributes that distinguish unusual behavior from normal requests.**AI and agent investigation:**[Agent Timeline](/platform/agent-timeline)adds conversation-level context for multi-agent workflows. Engineers can inspect model calls, tools, handoffs, failures, tokens, and related application spans.**Open instrumentation:** Honeycomb is built around OpenTelemetry. Teams can keep instrumentation portable while connecting AI spans with the wider production request path. Learn more about[Honeycomb and OpenTelemetry](/platform/opentelemetry).**Telemetry economics:** Honeycomb prices real-time telemetry around event volume. Current plans include unlimited custom fields, seats, and querying. This lets teams add investigation context without pricing each additional field separately. See[Honeycomb pricing](/pricing).**Proof in practice:** Birdie replaced Datadog with Honeycomb and reported 50% observability budget savings. The team also consolidated seven tools and reduced root-cause identification to about five minutes. Read the[Birdie case study](/resources/case-studies/birdie-evolution-fueled-by-observability).**Primary consideration:** Honeycomb focuses on software production and engineering investigation. Evaluate broader security or IT operations requirements separately if they are part of your Datadog footprint. ### 2. New Relic **Best for:** Teams wanting AI monitoring inside an established full-stack SaaS observability platform.**Why it stands out:** New Relic connects AI monitoring with its application performance monitoring agents. It tracks model performance, cost, tokens, response details, and user feedback.**Key strengths:** Agent monitoring traces agent invocations, tool calls, handoffs, and related services. Its entity map and trace waterfall show where latency or errors entered the workflow.**Primary consideration:** At the time of writing, New Relic labels agent monitoring as a preview feature. Confirm supported frameworks, release status, and production requirements during evaluation. ### 3. Dynatrace **Best for:** Large enterprises connecting AI observability with application, infrastructure, automation, security, and governance work.**Why it stands out:** Dynatrace AI observability includes overview, exploration, prompts, agent topology, and evaluation views. Teams can ingest data through OneAgent, OpenTelemetry, OpenInference, or OpenLLMetry.**Key strengths:** Dynatrace connects AI behavior with downstream effects on applications and infrastructure. It also supports LLM-as-a-judge evaluations and custom evaluators. At the time of writing, its Evaluation method library for managing custom evaluators is in preview.**Primary consideration:** Confirm licensing, ingestion requirements, deployment choices, and rollout scope. The right path depends on existing Dynatrace use and enterprise controls. Test permissions and cross-team workflows early. This approach can suit centralized platform and governance teams. ### 4. Grafana Cloud **Best for:** Teams already invested in Grafana, OpenTelemetry, and a composable observability stack.**Why it stands out:** Grafana Cloud AI Observability covers LLMs, evaluations, vector databases, GPUs, and MCP. Its agent experience organizes generations into conversations and tracks agent versions.**Key strengths:** Teams can connect agent logs with OpenTelemetry trace and span identifiers. Grafana also supports continuous quality evaluation and cost tracking. This can connect model behavior with vector, GPU, and protocol dependencies.**Primary consideration:** Confirm how AI Observability, Agent Observability, and existing Grafana components fit together. Decide which parts your team will manage. Test setup effort for every required data source. ### 5. Arize Phoenix **Best for:** AI and machine learning teams focused on LLM tracing, evaluations, retrieval analysis, experiments, and model quality.**Why it stands out:** Phoenix is an open-source AI observability platform built on OpenTelemetry. It captures LLM calls, tool execution, retrieval, generation, sessions, latency, and token use.**Key strengths:** Annotations measure quality, while sessions group related conversations. Phoenix also supports trace evaluations and retrieval-augmented generation analysis. Its open-source design supports custom deployment and data-control needs.**Primary consideration:** Phoenix centers on AI application behavior. Plan how host, network, and general service telemetry will connect with your wider toolset. Test how teams will correlate Phoenix traces with non-AI incidents. ### 6. Langfuse **Best for:** Teams seeking an open, self-hostable platform for LLM tracing, evaluations, prompt management, experiments, and cost analysis.**Why it stands out:** Langfuse understands model parameters, prompts, completions, scores, tokens, and costs. It supports online evaluations, offline experiments, datasets, human review, and LLM-as-a-judge workflows.**Key strengths:** Prompt versions can link directly to traces and evaluation metrics. Experiments compare prompts, models, and code variants against shared datasets. Current trace ingestion supports OpenTelemetry, and teams can run Langfuse themselves.**Primary consideration:** Langfuse targets AI engineering rather than general application performance monitoring. Most teams will pair it with broader service and infrastructure coverage. Define that handoff before the proof of concept. ### 7. SigNoz **Best for:** Teams seeking an OpenTelemetry-native, open-source alternative to Datadog with full-stack and AI monitoring.**Why it stands out:** SigNoz combines application performance monitoring, logs, metrics, traces, dashboards, alerts, and LLM monitoring. It supports cloud and self-hosted deployment.**Key strengths:** Current guides cover agent reasoning, tool calls, chain execution, and model responses, all of which use OpenTelemetry and OpenInference. Teams can correlate those traces with logs and services.**Primary consideration:** Compare its evaluation and prompt workflows to those of focused AI tools. Self-hosting also adds operating and upgrade work. SigNoz may suit teams seeking open-source control without having to assemble separate telemetry backends. Learn more about the production questions behind [AI and LLM observability](/use-cases/ai-llm-observability). ## How to shortlist and test a Datadog alternative Start with the reason you're considering a switch, then choose two or three platforms that address that problem directly. **If investigation is too fragmented:** Include Honeycomb, New Relic, and Dynatrace. Compare how each moves from a symptom to its production cause.**Suppose costs are the problem:** Model Honeycomb and other candidates against your real telemetry. Include fields, queries, retention, seats, and expected growth.**If instrumentation portability matters:** Compare OpenTelemetry support carefully. Honeycomb centers its instrumentation strategy on OpenTelemetry. Grafana Cloud and SigNoz also deserve consideration.**If AI evaluation depth is the priority:** Include Phoenix or Langfuse. Add Honeycomb when those AI workflows must stay connected to the wider application.**If you need to replace broader Datadog workloads:** Include Honeycomb for production engineering observability. Compare other platforms according to your infrastructure, security, and IT operations requirements. Use a single representative-agent workflow for the test. Include retrieval, tool calls, application services, and a known failure mode. Measure how quickly each platform answers five questions: - Which request or conversation failed? - Which model, prompt, agent, or tool was involved? - Where did latency or cost increase? - Did a downstream service cause the symptom? - Can another engineer reproduce the investigation? Also compare retention, data controls, query speed, operating work, and estimated production cost. Dual-send OpenTelemetry data when practical. The Collector can export the same telemetry to multiple destinations. Test the production problem that made you consider leaving Datadog. A polished demo will tell you much less. Record the same investigation with each platform. Compare the time to the first useful hypothesis and the time to the verified cause. Include one privacy-sensitive path. Verify filtering, access controls, and prompt-content handling before production. ## See how Honeycomb compares with Datadog If you're considering leaving Datadog, make sure the replacement fixes the problem that prompted you to look for a replacement. Maybe observability spending has become difficult to forecast. Maybe investigations require too much movement between signals. Or perhaps you want instrumentation that stays portable. AI makes those pressures more important. More production context and less predictable behavior create more ways for something unexpected to happen. Honeycomb approaches that problem with an event-based data model, high-cardinality investigation, OpenTelemetry instrumentation, and event-volume pricing. Agent telemetry lives inside that same production investigation model. For a closer look at the differences, explore our [Honeycomb vs. Datadog comparison](/why-honeycomb/comparisons/datadog).