Key findings
- Of 10 sources discussing correlation with APM and infrastructure, 9 praise it, most from teams that already run their main stack on Datadog.
- Missing or dropped traces are the most raised concern, in 13 sources, followed by double-counted spans or wrong token totals in 10 and tracer releases that break the application in 8.
- Of the 17 sources that describe where a team keeps its traces, 13 keep them in Datadog and 4 look at another tool, among them NonBioS, which chose self-hosted Langfuse.
- Datadog closed many of the reported defects as fixed within days to months, so the risk sits in upgrades: each new tracer or library version needs its span counts checked again.
Teams that keep their agent traces in Datadog do so because their services, logs, and hosts already report there, and they value one trace from the request to the model call. Their public reports center on the tracer: releases that drop spans, record one model call twice, or record zero tokens. Pick Datadog Agent Observability when that correlation matters, and test span counts on every tracer upgrade.
Why teams trace agents in Datadog
The common reason is that Datadog already runs the rest of the stack, and some teams pair it with a dedicated tool: Arize's customer story says Handshake's internal LLM service sends every call to both Datadog and Arize AX. A small r/devops team moved its LLM observability into Datadog for that reason and says correlating LLM traces with infrastructure metrics made debugging much easier. A r/LocalLLaMA team runs APM with propagated trace headers, log correlation, and LLMObs, and stays because its platform is too integrated with Datadog to move.
Company projects wire it in as a default. Airbyte's PyAirbyte MCP server picks Datadog whenever LLM Observability is enabled. cBioPortal added dd-trace to its LibreChat image so Anthropic and OpenAI calls are traced with no code changes. A codecentric engineer added cost-center tags to LiteLLM's Datadog spans. At Thomson Reuters, the head of the global command center told TechTarget that Experiments saves building model comparison infrastructure in house.
Where the span counts go wrong
Datadog bills each LLM span, so a model call recorded twice is billed twice. A Source.ag engineer found LangChain and the Anthropic integration each recorded the same call, which overcounted the team's spend about 2.2 times; Datadog closed that report as fixed in June 2026. The LiteLLM integration emits two LLM spans for one successful call when fallbacks are set. Teams exporting through both OpenTelemetry and the LLMObs API turned Datadog's conversion off to stop duplicates, and the Bifrost gateway ships with it off.
Token totals drift too. Streamed Amazon Nova calls record zero tokens, streamed Anthropic 1-hour cache writes are booked as 5-minute writes, and LiteLLM embedding spans arrive without a model name, which marks whole traces as partial cost. The Nova, cache, and fallback reports were open when GILD checked them on October 28, 2026.
When traces go missing or the tracer breaks the app
Missing traces come from streams and exits. Aborted OpenAI Responses streams lose their LLM span, Anthropic streams read with get_final_message never finished, Google ADK agent spans dropped intermittently, and buffered spans are lost when a pod stops; Datadog closed the request for graceful shutdown as not planned. An MCP integration fails to patch on mcp 2.0, and cBioPortal's MCP server lost its tool spans after calling a method ddtrace 4.13 does not have.
Some releases broke the application itself. The vLLM integration fails requests on vLLM 0.20 and later, an Anthropic plugin edited the caller's messages and caused 400 errors, and the Vercel AI SDK plugin leaked memory until one team's production process crashed daily. RavenPack found a Datadog and LangChain version mismatch crashing its agent. Most were fixed, so pin the tracer version you tested.
Why some teams look elsewhere
The accounts that look at other tools name fit, hosting and cost. NonBioS weighed Datadog among about a dozen tools in March 2025 and chose self-hosted Langfuse, because it needed software it could run on its own premises. One r/learnmachinelearning poster found Datadog strong for dashboards and infrastructure but built as a generalist, with pricing that creeps up. A Hacker News commenter is wary of storing LLM logs in Datadog after past runaway bills, since those logs dwarf APM logs. A LangSmith user is weighing a move to Datadog's evaluations, which shows the comparison runs both ways.
What to settle before choosing
Five questions decide whether Datadog Agent Observability fits. Answer them with your own traffic during a trial on the Free tier.
- Do your services already send APM traces to Datadog, so agent spans can join them?
- How many LLM spans does a typical run produce, and do Datadog's totals match your model invoices?
- Which tracer and library versions will you pin, and who rechecks spans before an upgrade?
- Which applications need more than 15 days of trace retention?
- Which model account will run your judges, and does it meet your residency terms?
| Theme | Sources | Share of sources |
|---|---|---|
| One trace with APM and infrastructure (praise) | 9 of 10 | |
| Auto-instrumentation of libraries (praise) | 6 of 8 | |
| Evaluations and experiments (praise) | 3 of 4 | |
| Missing or dropped traces (concern) | 13 of 13 | |
| Double-counted spans and wrong token totals (concern) | 10 of 10 | |
| Tracer breaks the application (concern) | 8 of 8 |
GILD count of 54 public sources, January 2025 to October 2026, opened Oct 28, 2026
Methodology
GILD read 54 public sources about Datadog LLM Observability and Agent Observability on October 28, 2026, published between January 2025 and October 2026. They come from GitHub issues and pull requests, Reddit, Hacker News, X, two newsletter posts, an engineering blog post, and a trade press article. GILD started from the research ledger's Datadog sources, rechecked each through the GitHub API, the Hacker News Algolia API, and the fxtwitter API, and searched Hacker News, Reddit through the Arctic Shift archive, and GitHub for more. GitHub bug reports make up most of the set and lean toward problems, so a report counts as mixed unless its author describes an outage or leaves the product. Datadog's blog and its customer case studies, dated only in their page metadata, are left out of the counts. Product behavior was checked against Datadog's pricing page and documentation the same day.
Products in this piece
Sources
- Public source
- 24
- Official documentation
- 6
- dd-trace-js issue #8936: two LLM spans per call overcount spend about 2.2x (Jun 16, 2026)
- dd-trace-py issue #20670: LiteLLM fallbacks emit two LLM spans per successful call (Sep 29, 2026)
- dd-trace-py issue #21073: streamed Amazon Nova calls record zero tokens (Oct 17, 2026)
- dd-trace-py issue #20782: Anthropic 1-hour cache writes booked as 5-minute writes (Oct 4, 2026)
- arni-labs/temperpaw pull request #372: disable Datadog's duplicate OpenTelemetry to LLMObs conversion (Jun 9, 2026)
- maximhq/bifrost pull request #8679: Datadog conversion off by default to avoid duplicate spans (Oct 17, 2026)
- dd-trace-py issue #19760: OpenAI Responses streaming span dropped (Aug 18, 2026)
- dd-trace-py issue #17125: buffered LLMObs spans lost on SIGTERM (Mar 25, 2026)
- cBioPortal/cbioportal-mcp pull request #173: fix Datadog LLM Observability tool spans (Oct 12, 2026)
- dd-trace-py issue #20768: vLLM integration fails requests on vLLM 0.20 and later (Oct 3, 2026)
- dd-trace-js issue #8105: Anthropic plugin mutates the caller's messages array (Apr 25, 2026)
- dd-trace-js issue #9276: AI SDK plugin leaks heap and crashes the process daily (Jul 9, 2026)
- dd-trace-js issue #7370: unreachable agent crashes the Node.js process (Jan 28, 2026)
- RavenPack on Medium: 70% of our AI agent failures had nothing to do with AI (Jan 29, 2026)
- airbytehq/PyAirbyte pull request #1291: default to Datadog when LLM Observability is enabled (Sep 30, 2026)
- cBioPortal/LibreChat pull request #16: add dd-trace for Datadog LLM Observability (Jun 9, 2026)
- TechTarget: Datadog AI agent observability, security seek to boost trust (Jun 12, 2025)
- Arize customer story: How Handshake deployed and scaled 15+ LLM use cases in under 6 months (Aug 17, 2026)
- NonBioS: The NonBioS LLM observability pick (Mar 5, 2025)
- Signals & Codes: Datadog AI Observability, what the getting started guide doesn't cover (Mar 19, 2026)
- Reddit r/devops comment: consolidated LLM observability into Datadog (Jan 9, 2026)
- Reddit r/LocalLLaMA comment: APM, log correlation, and LLMObs too integrated to move (Aug 6, 2025)
- Reddit r/learnmachinelearning: Datadog LLM observability alternatives (Apr 3, 2025)
- Hacker News comment: wary of LLM logs in Datadog after runaway bills (Jan 9, 2026)
- Datadog pricing: Agent Observability
- Datadog docs: Agent Observability
- Datadog docs: Automatic instrumentation for Agent Observability
- Datadog docs: OpenTelemetry instrumentation for Agent Observability
- Datadog docs: Agent Observability SDK reference
- Datadog docs: Agent Observability data governance