Datadog Agent Observability Signals
What teams building AI features on Datadog Agent Observability report, with every finding tied to its evidence.
Read the findingsWhat the evidence says
Practitioner sentiment, teams and leaders
+32 Positive from 26 sources that take a side
How this figure is madePositive overall signal, net 32 points
From 25 authors in 26 discussions. Small sample. Run a pilot on one production app to measure which failures it surfaces and how long tracing them takes.
- Analyzed
- 63 total sources
- Top praise
- One Trace With APM and Infrastructure (6 of 6 sources) See praise evidence
- Top concern
- Missing or Dropped Traces (7 of 7 sources) See concern evidence
Jan 2025 to Oct 2026.
54 sources from practitioners, by month and sentiment.
The 26 counted team and leader accounts are 9 positive, 16 mixed, and 1 negative; two integration builders are listed and not counted. Twelve keep their agent traces in Datadog, most because the rest of their stack already runs there, and NonBioS chose self-hosted Langfuse instead. Missing or dropped traces are raised in 7 sources, and double-counted spans or wrong token totals in 5.
Positive, mixed and negative sources
The score counts them as 9 positive, 15 mixed and 1 negative votes, one per author. See How the numbers work.
See the evidenceBelow: 2 praise themes, 2 concerns and 1 point of agreement.
Findings
Counted for Teams and technical leaders, from 29 sources. Change group
What practitioners praise
Every capability practitioners describe positively, ranked by how many sources say so. Open one to read who said it.
One Trace With APM and InfrastructureAll 6 sources on it praise itTeams keep LLM and agent traces in the same platform and trace as their services, logs, and hosts.
Counted for Teams and technical leaders.
Positive
DifficultyIcy454, Reddit user, r/FinOps
Comment on: GenAI is the first cost line my allocation playbook completely falls apart onReddit comment
Traces every model call in Datadog LLM Observability to track model and workload efficiency, retry loops, and prompt cache hit rates, and calls it a big improvement for cost allocation.
Mixed
nijave, Reddit user, r/AI_Agents
Comment on: AI governance isn't failing because we lack regulationReddit comment
The team uses Datadog's AI and LLM Observability mainly because it already runs Datadog, finds it helpful, and notes other tools may be better or cheaper.
Positive
RasheedaDeals, Reddit user, r/devops
Comment on: Anyone else finding observability for LLM workloads is a completely different beast?Reddit comment
The team moved its LLM observability into Datadog because it already ran the main stack there, and correlating LLM traces with infrastructure metrics made debugging much easier.
Mixed
SigmoidGrindset, Reddit user, r/LocalLLaMA
Comment on: LiteLLM started breaking down for us past 300 RPS, what are folks using in prod?Reddit comment
The team uses APM with propagated trace headers, log correlation, and LLMObs, and stays on Datadog because its platform is deeply integrated with it, even though another tool looked more advanced.
Positive
Kosuke Oya, engineer at PLAID (GitHub profile)
[FEATURE]: LLMObs - Instrumentation for mastraGitHub feature request
Runs Mastra in production and asks for LLM Observability instrumentation so its traces show in an LLM-specific view, with plain APM as the fallback it considered.
Positive
QuickNick123, Reddit user, r/devops, self-described engineer at an S&P 500 company
Comment on: My CTO forced a microservices migration for our tiny user baseReddit comment
The company uses nearly every Datadog product, LLM observability included, and at its scale the Datadog bill is small next to cloud spend.
GILD's fact checks, which don't count as sources
Checked Oct 28, 2026.Agent Observability can be bought without other Datadog products, and teams that also run APM, infrastructure monitoring, or real user monitoring correlate agent behavior with them under the same trace ID. Datadog pricing: Agent Observability
Checked Oct 28, 2026.Datadog organizations can require single sign-on with SAML, with guides for Okta, Entra ID, Google, and other identity systems. Datadog docs: Single sign-on with SAML
Checked Oct 28, 2026.Datadog Audit Trail records audit logs of user activity, API requests, and resource changes across the organization. Datadog docs: Datadog Audit Trail
Open them in the recordAuto-Instrumentation of Libraries4 of 5 sources on it praise itThe SDKs trace model clients, frameworks, and gateways without code changes.
Counted for Teams and technical leaders.
Positive, 4
Positive
cflocke, engineer at codecentric AG (GitHub profile)
fix(datadog): include DD_TAGS on LLM Observability spansGitHub pull request (LiteLLM)
Adds global DD_TAGS to LiteLLM's Datadog LLM Observability spans so operators can attribute LLM usage to a cost center, verified live against Datadog.
Positive
Minh Cung (cungminh2710), GitHub user
Instrument AI SDK with Datadog LLM Observability and add content-redaction telemetryGitHub pull request
Adds Datadog LLM Observability for server-side AI SDK calls with a fail-closed redaction policy, so only workflow names, token counts, and latency leave the application.
Positive
Gaofei Zhao (dippindots), cBioPortal contributor
feat: add dd-trace for Datadog LLM ObservabilityGitHub pull request (cBioPortal)
Adds dd-trace to cBioPortal's LibreChat image so Anthropic and OpenAI SDK calls are auto-instrumented into Datadog LLM Observability with no code changes.
Positive
Caleb Evans, engineer at Airbyte (GitHub profile)
feat(mcp): default to Datadog when LLM Observability is enabledGitHub pull request (PyAirbyte)
Makes PyAirbyte's hosted MCP server pick the native Datadog tracing backend by default whenever LLM Observability is enabled. Merged the same day.
Mixed or negative, 1
Mixed
Mustafa Akin, engineer at JumpCloud (GitHub profile)
dd-trace-js issue 7146: ToolLoopAgent in AI SDK 6 and LLM Observability tracesGitHub issue
Regular streamText and object calls were captured, but the AI SDK 6 ToolLoopAgent was not traced in LLM Observability. Closed as completed in March 2026.
"However it is not being captured in LLM Observability."
Excerpt, makinjc
GILD's fact checks, which don't count as sources
Checked Oct 28, 2026.The Python SDK auto-instruments OpenAI, Anthropic, Bedrock, Vertex AI, LangChain, LangGraph, CrewAI, LiteLLM, MCP, and vLLM, among others, and the Node.js SDK covers a shorter list that includes the Vercel AI SDK. Each integration has a minimum tracer version. Datadog docs: Automatic instrumentation for Agent Observability
Checked Oct 28, 2026.Datadog signs a HIPAA Business Associate Agreement for its HIPAA-eligible services, and the docs list the restrictions that apply once a customer signs it. Datadog docs: HIPAA compliance
Open them in the record
What concerns them
Every concern practitioners raise, most raised first.
Missing or Dropped TracesAll 7 sources on it raise it as a concernSpans never arrive: streams that never finish, integrations that fail to patch, and buffers lost when a process exits.
Counted for Teams and technical leaders.
Mixed
Ino de Bruijn, cBioPortal and Memorial Sloan Kettering (GitHub profile)
Promote #168 to main: fix Datadog LLM Observability tool spansGitHub pull request (cBioPortal)
Fixes the cBioPortal MCP server after its Datadog LLM Observability tool spans dropped to zero, because it called an LLMObs method that does not exist in ddtrace 4.13.
Mixed
Kyle Purdon, engineer at Semgrep (GitHub profile)
[BUG]: Generated dd tracestate exceeds 256-character limit and is rejected by OpenTelemetryGitHub issue
LLM Observability propagation metadata pushes Datadog's tracestate past the W3C 256-character limit, so OpenTelemetry warns and drops the vendor trace state.
"We encountered this with LLM Observability propagation metadata…"
Excerpt, kpurdon Mixed
Zach Hammer, engineer at SeatGeek (GitHub profile)
[BUG]: LLMObs: errors creating openai spans - missing span kind in span contextGitHub issue
LLM spans for aborted or truncated streamed OpenAI Responses calls are dropped entirely, orphaning child spans and losing token metrics.
Mixed
Mustafa Akin, engineer at JumpCloud (GitHub profile)
dd-trace-js issue 7146: ToolLoopAgent in AI SDK 6 and LLM Observability tracesGitHub issue
Regular streamText and object calls were captured, but the AI SDK 6 ToolLoopAgent was not traced in LLM Observability. Closed as completed in March 2026.
"However it is not being captured in LLM Observability."
Excerpt, makinjc Mixed
lucasiscovici, GitHub user filing an issue
[BUG]: LLM Observability Spans Not Appearing When Payload > 1MBGitHub issue
When a payload to the LLM Observability intake exceeded 1 MB, no LLM spans appeared and the links between APM and LLM spans were missing, although the tracer and agent allow 5 MB. Datadog closed the issue as completed in August 2025.
"…exceeds 1MB, the corresponding LLM Obs spans are not correctly ingested."
Excerpt, lucasiscovici Mixed
TJ Koblentz (tjk), GitHub user filing an issue
[BUG]: LLMObs agentLessEnabled does not mean no agent?GitHub issue
With the Datadog agent reached over a Unix socket, dd-trace could not send LLM Observability span events, because the LLMObs writer expected an HTTP agent proxy. Datadog closed the issue as completed in September 2025.
"Cannot send LLMObs events using our agent configuration."
Excerpt, tjk Mixed
Ryuichi Watanabe (ryuichi1208), GitHub user filing an issue
[BUG]: Traces Dropped After 5 Retries When Sending to Local Agent ProxyGitHub issue
After an upgrade to ddtrace 2.19.2, LLM Observability traces failed to reach the local agent proxy and were dropped after 5 retries, while traces to other destinations arrived; 2.18 versions worked. Datadog closed the issue as completed in April 2025.
"The traces fail to be sent to the local agent proxy (http://localhost:8126) after 5 retry attempts, leading to the traces being dropped."
Excerpt, ryuichi1208
GILD's fact checks, which don't count as sources
Checked Oct 28, 2026.The SDK reference documents flushing buffered data and setting a trace sampling rate; sampling decides on the root span and applies to all of its children. Datadog docs: Agent Observability SDK reference
Open them in the recordDouble-Counted Spans & Wrong Token TotalsAll 5 sources on it raise it as a concernNested integrations, fallbacks, or two export paths record one model call twice, or record the wrong token counts, which skews cost views and the span bill.
Counted for Teams and technical leaders.
Mixed
Christopher Krah, engineer at UiPath (GitHub profile)
[BUG]: Anthropic streamed spans report 1h cache writes as ephemeral_5m_input_tokensGitHub issue
Streamed Anthropic calls that write a 1-hour prompt cache are recorded as 5-minute cache writes, while the same call without streaming splits them correctly, so cache cost is misreported.
"A streamed `messages.create` that writes a 1-hour prompt cache gets `ephemeral_1h_input_tokens: 0` and the whole write under `ephemeral_5m_input_tokens`."
Excerpt, chrikrah Mixed
Tomas Alabes, engineer at Source.ag (GitHub profile)
LLMObs: langchain + modelcontextprotocol-sdk integrations double-capture MCP tool callsGitHub issue
Every MCP tool call is captured twice, with the full tool payload on each span, which in the team's production app tripled storage for tool results and doubled error counts. Closed as completed in August 2026.
"…every MCP tool call is captured **twice** in LLM Observability, with the full tool input/output payload stored on each span:…"
Excerpt, tomasAlabes Mixed
Tomas Alabes, engineer at Source.ag (GitHub profile)
LLMObs: LangChain + provider (anthropic) integrations emit two llm spans per call, double-counting costGitHub issue
LangChain and the Anthropic SDK integration each emit an LLM span for the same model call, so Datadog's cost rollup overcounted the team's spend about 2.2 times. Datadog closed the issue as completed in June 2026.
"…cost rollup sums both layers and overcounts spend (~2.2× in our case)…"
Excerpt, tomasAlabes Mixed
Rita Agafonova (rita-aga), GitHub user
Disable Datadog OTel LLMObs duplicate conversionGitHub pull request
Turns off Datadog's OpenTelemetry-to-LLMObs conversion in production so the direct LLMObs API export is the only source, after both paths produced duplicate spans.
Mixed
Zach Hammer, engineer at SeatGeek (GitHub profile)
[BUG]: LLMObs: errors creating openai spans - missing span kind in span contextGitHub issue
LLM spans for aborted or truncated streamed OpenAI Responses calls are dropped entirely, orphaning child spans and losing token metrics.
GILD's fact checks, which don't count as sources
Checked Oct 28, 2026.Datadog meters and bills the number of LLM spans ingested, one per request to a model provider, so one agent workflow can produce several LLM spans. Datadog docs: Agent Observability
Open them in the record
Where the evidence agrees
Statements this group's sources agree on, then the ones close to agreement.
- Recurring point
Tracer upgrades and library upgrades change what reaches Datadog, so teams pin versions and recheck spans before relying on them.
6 supporting sources
Where it disagrees
Questions the sources split on, with the count on each side.
For teams and technical leaders, no question reaches 5 sources from 3 authors, with 2 on each side.
The evidence
Whose experience this is
The three groups, then every practitioner by the context they work in, and how emphasis changes with scale.
Three groups drawn from the same sources. Choose a group to select whose experience counts. Within that group, each author counts once in the practitioner sentiment, with their latest view. Signals open on Teams and technical leaders.
Teams and technical leaders
29 sources. Teams at named companies such as Source.ag, Semgrep, Airbyte, and cBioPortal, and small product teams, describe why they trace agents in Datadog and what broke.
Individual builders
25 sources. Individual builders describe instrumenting their own apps and the tracer defects they filed.
All practitioners
54 sources. These accounts combine named company teams with individual builders, and most are bug reports filed on Datadog's tracers.
54 sources from practitioners, by the context they work in. Filled rows count for this group.
- Individual builderDevelopers building on the platform on their own25 sources
Emphasizes Tracer defects, dropped spans, and setup
- Small product team2 to 10 engineers, or an unnamed startup team7 sources, counted here
Emphasizes Keeping LLM traces beside the rest of a Datadog stack, and duplicate spans
- Product or platform team11 to 50 engineers, or a named company's team20 sources, counted here
Emphasizes Span accuracy, cost attribution, and adoption in company repositories
- Technical leadershipLeaders setting model and platform choices across teams1 sources, counted here
Emphasizes Thomson Reuters' command center values Experiments and wants clear pricing before production
- Organization-wideEnterprise or company-wide AI platform1 sources, counted here
Emphasizes A self-described S&P 500 engineer reports LLM observability among nearly every Datadog product the company runs
What changes with scale. Individual builders file most of the tracer defects: dropped spans, broken streams, and integrations that fail after a library upgrade. Small teams keep LLM traces in Datadog because their stack already runs there, and NonBioS ruled it out because it needed tracing on its own premises. Source.ag reports double-counted spans and wrong cost totals, Airbyte, cBioPortal, and codecentric engineers wire Datadog into their own projects, and Handshake runs it beside Arize AX. Company-wide accounts at Fintool, Twine Security and AppFolio come only from Datadog's own case studies.
Where the sources come from
Where this group's sources were published.
Counted for Teams and technical leaders, from 29 sources. Change group
38 sources for this group, 9 of them documentation
- Developer Communities26, 68% of 38 sources
- Product Documentation9, 24% of 38 sources
- Engineering Blogs & Press3, 8% of 38 sources
Earlier evidence remains when it still applies. Bug reports Datadog has since closed as fixed carry Needs a recheck.
See sources by originThe record
Who each source is, how it knows, whether it still applies and what it counts toward.
Newest first, on its own page. Every count above opens its sources there.
Read all 63 sourcesAbout this read
What changed, and how we count
What changed
- Oct 30, 2026: a second-model check took RavenPack's Operator Insight off the page while a second check reads GILD's stored copy of the post, since the live page blocks automated reading; its Signals record stays. Handshake's card no longer says Datadog carries its calls beside other monitoring, which Arize's story does not state, and cBioPortal's card keeps only its June 9 pull request; the MCP server fix stays a Signals record of its own.
- Every record links to a public source and names its author and publication date.
- A page shows a finding when at least 5 sources from 3 different authors discuss it. A disagreement also needs at least 2 sources on each side.
- Pricing, retention, instrumentation, and evaluation findings are checked against Datadog's pricing page and documentation as of October 28, 2026.
- Oct 28, 2026: first edition, built from the research ledger's Datadog sources, rechecked the same day, plus new Reddit, newsletter, press, and pull request sources.
- Oct 28, 2026: adds 4 records, three GitHub issues from 2025 on traces that never reached Datadog and NonBioS's engineering post choosing Langfuse, research-ledger quote rows on 24 records, and 8 Operator Insights: Handshake running Datadog beside Arize AX, NonBioS's rejection, cBioPortal, RavenPack, Thomson Reuters as TechTarget reported it, and Datadog's case studies of Twine Security, Fintool and AppFolio. Notes the 2026 rename from LLM Observability to Agent Observability, made without a dated announcement.
Method
A running review of what teams building AI features on Datadog Agent Observability report, from individual builders to company-wide platforms. Each finding links to its sources, and each conflict shows both sides.
Counts cover only the sources GILD reviewed.
Who counts
- Teams and technical leaders
- Small product team, Product or platform team, Technical leadership, Organization-wide
- Individual builders
- Individual builder
- All practitioners
- Everyone in the teams and leaders and individual builders groups
Choose a group to select whose experience counts. Within that group, each author counts once in the practitioner sentiment, with their latest view. GILD files each source in a group from how the account describes its author. A role or employer marked self-described is what the source states about themselves. Documentation checks facts for every group and never counts toward sentiment.
How the numbers work
- Overall signal. Positive votes minus negative ones, per 100 votes from the sources that take a side. Each author counts once, with their latest view, whoever they are and wherever they posted. The older weighted reading, where accounts GILD matched to a named person count three times when GILD lists the author among technical leaders and twice otherwise, is shown beside it as a second view. Where an author wrote more than one source, the line beside the split gives the votes, one per author, so the score follows from it. When one author's sources from the same day disagree, that author counts as mixed, and a half point rounds away from zero. Sources with no opinion are listed and not counted. Strong positive is +50 or higher, positive +20 to +49, mixed −19 to +19, negative −20 to −49, strong negative −50 or lower.
- Weighted reading, second view. Counted the older way, where an account GILD matched to a named person counts two or three times, teams and leaders read +30 positive (method sentiment/S1). GILD printed that number as "GILD score" until October 29, 2026; most of those matches rest on a name or a self-described role.
- Method change, Oct 30, 2026. GILD changed how practitioner sentiment counts, to one vote per author. Weighted, it read +33 Positive; with one vote per author it reads +36 Positive. No evidence changed.
- Unscored. A group gets a score only with 20 sources from 15 authors. Below that, one source moves the score about 5 points, so the read says "Unscored until 20 sources from 15 authors" and shows its counts.
- Independence. A source whose author works for the vendor, for one of its partners or programs, or for a competitor, and a page the vendor published or ran, is listed with that tie and counts toward nothing. One author counts on at most two records per product. The line under the score says how many authors and how many separate discussions the counted sources come from: comments in one thread are one discussion.
- Praise and concerns. How many of the sources that discuss a theme praise it or raise it.
- Consensus. A statement joins when 20% of the group's sources support it and fewer contradict it. Contradicting sources are counted separately. Below that line a statement shows as building. At 40% or more it reads Strong agreement; below, Recurring point.
- Disagreements. The count on each side. GILD names no winner and gives no share of all users.
- Minimum evidence. A finding needs 5 sources from 3 authors; a disagreement also needs 2 on each side. Smaller counts stay in the table below, marked under the minimum.
Labels on a source
A source's date and whether it still applies are separate: a recent post can describe an old version, and an old one can still hold.
- Documented
- Stated in official documentation.
- Reported
- A practitioner's account of their own use.
- Measured
- Backed by the source's own measurements, such as a comparative test or tracked team metrics.
- GILD analysis
- GILD's check of a claim against official documentation.
- Current
- Describes the product as it is now.
- Still applicable
- Older, and the behavior or lesson still holds.
- Needs a recheck
- May depend on a plan, version or setting that changed since it was written. It keeps counting until GILD rechecks it, since the rest of the account usually holds, and its card says so.
- Out of date
- No longer accurate. Kept in the record; counts toward nothing current.
Flagged for a recheck, of the sources that take a side: teams and leaders 8 of 26, individual builders 7 of 25, all practitioners 15 of 51.
Limits
- The counts cover 54 public sources. GitHub supplies most of them: bug reports and feature requests on Datadog's own tracers and API client, plus issues and pull requests in the projects of teams that adopted or worked around the product. Bug reports lean toward problems, so GILD counts a report as mixed unless its author describes a production outage or leaves the product. The rest are Reddit posts and comments, one Hacker News comment, two posts on X, two newsletter posts, an engineering blog post, and a trade press article. Datadog's own blog and its customer case studies, dated only in their page metadata, are left out of the counts, as are posts by Datadog staff and Reddit threads that read as vendor promotion; the Operator Insights show Fintool, Twine Security and AppFolio from those case studies, labelled as Datadog's. Engineers whose GitHub profiles name Semgrep, Attentive, JumpCloud or Pipedream are counted as individual reports, never as company deployments. Research-ledger records from 2023 and 2024 fall outside the window and are left out, as are two Hacker News comments about Datadog in general.
- Sources run from ddtrace 2.19 and dd-trace 5.35 in early 2025 to ddtrace 4.16 and dd-trace 6.x in October 2026, and each record keeps the version it states. Most use the LLM Observability name: Datadog renamed the product Agent Observability in 2026 without a dated announcement, its old product URL redirecting by Mar 22, 2026 and the new page first archived Jun 10, 2026. Issues Datadog has since closed as completed carry Needs a recheck; open issues and the request closed as not planned stay current.
Every count, and the sources behind it
12 findings for 3 groups
Each row opens its sources for the group you are reading.
| Finding | Teams and technical leaders | Individual builders | All practitioners | Sources |
|---|---|---|---|---|
| Current readCurrent sentiment | 9 positive, 16 mixed, 1 negative | 5 positive, 17 mixed, 3 negative | 14 positive, 33 mixed, 4 negative | Sources |
| PraiseOne Trace With APM and Infrastructure | 6 of 6 positive | 3 of 4 positive (under the minimum) | 9 of 10 positive | Sources |
| PraiseAuto-Instrumentation of Libraries | 4 of 5 positive | 2 of 3 positive (under the minimum) | 6 of 8 positive | Sources |
| PraiseOpenTelemetry Intake | 1 of 3 positive (under the minimum) | 1 of 1 positive (under the minimum) | 2 of 4 positive (under the minimum) | Sources |
| PraiseEvaluations & Experiments | 1 of 1 positive (under the minimum) | 2 of 3 positive (under the minimum) | 3 of 4 positive (under the minimum) | Sources |
| ConcernDouble-Counted Spans & Wrong Token Totals | 5 of 5 raise it | 5 of 5 raise it | 10 of 10 raise it | Sources |
| ConcernMissing or Dropped Traces | 7 of 7 raise it | 6 of 6 raise it | 13 of 13 raise it | Sources |
| ConcernTracer Breaks the Application | 3 of 3 raise it (under the minimum) | 5 of 5 raise it | 8 of 8 raise it | Sources |
| ConcernDatadog Pricing & Bill Growth | 2 of 2 raise it (under the minimum) | 2 of 2 raise it (under the minimum) | 4 of 4 raise it (under the minimum) | Sources |
| ConsensusTest each tracer release before trusting it | 6 supporting | 9 supporting | 15 supporting | Sources |
| DisagreementWhere should a team keep its LLM and agent traces? | Keep them in Datadog 12, Use another tool 1 (under the minimum) | Keep them in Datadog 1, Use another tool 3 (under the minimum) | Keep them in Datadog 13, Use another tool 4 | Sources |
| Current readEvidence mix | 38 sources | 34 sources | 63 sources | Sources |
Fact checks
GILD's checks of claims against official documentation, kept apart from the source counts.
- One Trace With APM and Infrastructure. Agent Observability can be bought without other Datadog products, and teams that also run APM, infrastructure monitoring, or real user monitoring correlate agent behavior with them under the same trace ID. Datadog pricing: Agent Observability, checked Oct 28, 2026
- One Trace With APM and Infrastructure. Datadog organizations can require single sign-on with SAML, with guides for Okta, Entra ID, Google, and other identity systems. Datadog docs: Single sign-on with SAML, checked Oct 28, 2026
- One Trace With APM and Infrastructure. Datadog Audit Trail records audit logs of user activity, API requests, and resource changes across the organization. Datadog docs: Datadog Audit Trail, checked Oct 28, 2026
- Auto-Instrumentation of Libraries. The Python SDK auto-instruments OpenAI, Anthropic, Bedrock, Vertex AI, LangChain, LangGraph, CrewAI, LiteLLM, MCP, and vLLM, among others, and the Node.js SDK covers a shorter list that includes the Vercel AI SDK. Each integration has a minimum tracer version. Datadog docs: Automatic instrumentation for Agent Observability, checked Oct 28, 2026
- Auto-Instrumentation of Libraries. Datadog signs a HIPAA Business Associate Agreement for its HIPAA-eligible services, and the docs list the restrictions that apply once a customer signs it. Datadog docs: HIPAA compliance, checked Oct 28, 2026
- OpenTelemetry Intake. Agent Observability ingests OpenTelemetry spans that follow the GenAI 1.37+ conventions or OpenInference, and supports OpenLLMetry 0.47+ and Langfuse attributes. Datadog docs: OpenTelemetry instrumentation for Agent Observability, checked Oct 28, 2026
- Evaluations & Experiments. Custom LLM-as-a-judge evaluators run on an OpenAI, Azure OpenAI, Anthropic, Bedrock, Vertex AI, or OpenAI-compatible account the team connects, which Datadog calls bring-your-own-key evaluations. Datadog docs: Connect an LLM provider for bring-your-own-key evaluations, checked Oct 28, 2026
- Evaluations & Experiments. Judge accounts are connected from the Integrations page in Agent Observability settings, one tile each for OpenAI, Azure OpenAI, Anthropic, and Amazon Bedrock. Datadog docs: Connect an LLM provider for bring-your-own-key evaluations, checked Oct 28, 2026
- Double-Counted Spans & Wrong Token Totals. Datadog meters and bills the number of LLM spans ingested, one per request to a model provider, so one agent workflow can produce several LLM spans. Datadog docs: Agent Observability, checked Oct 28, 2026
- Missing or Dropped Traces. The SDK reference documents flushing buffered data and setting a trace sampling rate; sampling decides on the root span and applies to all of its children. Datadog docs: Agent Observability SDK reference, checked Oct 28, 2026
- Tracer Breaks the Application. Each auto-instrumented library lists a minimum supported library version and a minimum tracer version, so a library upgrade can move outside the tested range. Datadog docs: Automatic instrumentation for Agent Observability, checked Oct 28, 2026
- Datadog Pricing & Bill Growth. Pro includes 100,000 LLM spans a month from US$160 billed annually, then US$3.50 per 10,000 billed annually, US$4.20 month to month, or US$5 on demand. Datadog pricing: Agent Observability, checked Oct 28, 2026