Langfuse vs. Datadog
This guide outlines the key differences between Langfuse and Datadog Agent Observability. All Datadog facts were checked against public Datadog sources in September 2026.
TL;DR
Choose Langfuse if you want:
1. MIT-licensed self-hosting. Cloud and self-hosted run the same product. Tracing, evaluations, prompt management, and experiments are in the free OSS distribution. Used by 21 of the Fortune 50.
2. Months to years of queryable history. Core is $29/month with 90 days. Pro includes 3 years on ClickHouse.
3. Purpose-built AI quality workflows. Code evaluators on live observations, experiments in the UI and CI, and prompt-version metrics.
Choose Datadog Agent Observability if you want APM and LLM tracing in one Datadog deployment. You accept SaaS-only hosting and 15-day default trace retention.
Many teams run both: Datadog for APM and infrastructure, Langfuse for AI engineering, from the same OpenTelemetry instrumentation. See Using Langfuse and Datadog together.
Open source and distribution
Langfuse is open source (MIT). Self-hosting is a first-class deployment mode and uses the same codebase as Langfuse Cloud. Datadog Agent Observability is proprietary SaaS. Traces are stored on Datadog-hosted sites. The product is unavailable on Datadog's government sites (US1-FED, US2-FED).
| Langfuse | Datadog Agent Observability | |
|---|---|---|
| License | MIT | Proprietary SaaS |
| GitHub stars |  | N/A (platform not on GitHub) |
| Self-hosting | Every tier, including free | Not offered. Traces are stored on Datadog-hosted sites |
| Air-gapped operation | Supported | Not offered |
| Data residency | Cloud EU, US, Japan; HIPAA region; any region via self-hosting | Nine hosted sites (US, EU, Japan, Australia, UK, US-gov); Agent Observability is unavailable on the two gov sites |
| Published Cloud Enterprise | $2,499/month | Custom |
History and data plane
Langfuse writes each LLM call, tool execution, and agent step to open-source ClickHouse. Cloud and self-hosted share the same engine as of Langfuse v4. High-volume reads use the Observations and Metrics APIs. Self-hosters can query ClickHouse in SQL.
Datadog Agent Observability retains traces and spans for 15 days on Free, Pro, month-to-month, and annual plans. Add-ons extend traces to 30, 60, or 90 days and experiments to 6, 9, or 12 months. Datasets are versioned separately and kept for 3 years. There is no published 365-day tier for Agent Observability traces.
| Langfuse | Datadog Agent Observability | |
|---|---|---|
| Storage engine | ClickHouse (Apache 2.0) | Datadog-operated SaaS store |
| Default trace retention | 30 days Hobby, 90 days Core, 3 years Pro and Enterprise | 15 days on Free, Pro, M2M, and annual |
| Extended trace retention | Included in the plan window; project retention policies on Pro+ | Paid add-on to 30, 60, or 90 days. Rates in Pricing |
| Experiment retention | Same window as traces | 15 days on-demand; 90 days on committed plans; add-ons to 6 / 9 / 12 months |
| Dataset retention | Same project data plane | 3 years, versioned separately |
Pricing
Langfuse bills units: one unit is a trace, an observation, or a score. Datadog Agent Observability bills LLM inference spans (one call to an LLM provider). Tool, workflow, agent, embedding, and retrieval spans are free on Datadog's published pricing. Span mix changes the crossover; the editable pricing model takes it as an input so you can find yours.
| Langfuse | Datadog Agent Observability | |
|---|---|---|
| Free tier | 50,000 units/month, 2 users, 30 days | 40,000 LLM spans/month, 15-day retention, full feature access |
| Paid entry | Core $29/month (100,000 units, unlimited users) | Pro $160/month billed annually ($200 month-to-month) for the first 100,000 LLM spans |
| Usage rate | $8/100k units after the included 100k, graduating to $6/100k at 50M+ | $3.50 per 10k additional LLM spans (annual); $4.20 M2M; $5.00 on-demand |
| Retention | 90 days Core, 3 years Pro and Enterprise | 15 days included; 30 / 60 / 90 days at $1.50 / $3.00 / $4.00 per 10k LLM spans |
| Seats | None on Core and above | None published for Agent Observability |
| Evaluations | Scores bill as units | Eval LLM calls bill as LLM spans; no separate eval product fee |
| Enterprise | $2,499/month | Custom |
Worked example
500,000 traces/month · 10 observations per trace · 2 LLM calls per trace · 1 score per trace · 5 users.
| Base | Usage | Retention | Total/month | |
|---|---|---|---|---|
| Langfuse Pro (3-year retention) | $199.00 | $422.00 | $0.00 | $621.00 |
| Datadog Pro (90-day retention) | $160.00 | $315.00 | $400.00 | $875.00 |
Public annual list prices. Edit the inputs yourself: Langfuse vs Datadog pricing model. Langfuse: 6,000,000 units, 100k included → $199 base + $72 (100k–1M at $8/100k) + $350 (1M–6M at $7/100k) = $621, 3-year history included. Datadog: 1,000,000 LLM spans → $160 base + 90 × $3.50 ($315) + 90-day retention add-on 100 × $4.00 ($400) = $875. Month-to-month Pro is $200 + $4.20/10k.
See pricing · Start Free · Talk to us
Tracing and stack context
Both platforms capture hierarchical traces of LLM applications and agents, including token usage, latency, errors, and cost.
Datadog Agent Observability correlates an LLM span with APM, infrastructure, logs, and RUM in the same Datadog account. Those signals come from the rest of Datadog.
Langfuse records a trace per request, with typed observations for LLM calls, tools, and retrieval. Multi-turn chats group into sessions. Agent runs render as agent graphs. Cost is attributed on each generation.
| Feature | Langfuse | Datadog Agent Observability |
|---|---|---|
| SDKs | Python and JS/TS on OpenTelemetry; other languages via the OTLP endpoint | Python, Node.js, and Java; HTTP API for other languages |
| Auto-instrumentation | 100+ integrations, including OpenAI, LangChain, LlamaIndex, Vercel AI SDK, LiteLLM, CrewAI | Supported LLM providers and agent frameworks |
| OpenTelemetry | OTel-native SDKs + OTLP endpoint | OTLP intake for GenAI semantic conventions 1.37+ and OpenInference |
| Platform context | Sessions, users, environments, releases, agent graphs | Correlation with Datadog APM, infrastructure, logs, and RUM |
| Production insights | Dashboards, Pulse, filter search | Patterns (topic clustering of production traffic) |
| Sensitive data | SDK and OTel masking | Sensitive Data Scanner included with Agent Observability usage |
Evaluation and experiments
Langfuse evaluations include LLM-as-a-judge evaluators, code evaluators on live observations and experiments, custom scores via SDK/API, annotation queues, and dataset experiments on Cloud and self-hosted. CI/CD gates fail a pipeline on experiment results.
Datadog provides LLM-as-a-judge evaluators from a template or your own prompt, annotation queues, end-user feedback, and external evaluations via API. Experiments are started from the Python SDK with function- or class-based evaluators; datasets and run comparison live in the UI. Eval LLM calls bill as LLM spans.
| Feature | Langfuse | Datadog Agent Observability |
|---|---|---|
| LLM-as-a-judge | Yes (observations + experiments) | Templates or your own prompt |
| Deterministic online code evals | Yes, native | External via API or third-party frameworks; function evaluators in experiments |
| Experiments | UI + SDK, versioned datasets | Python SDK datasets and experiments |
| CI/CD gates | GitHub Action | Via SDK / custom |
| Annotation queues | UI + API | Yes |
| Where evals run | Cloud or self-hosted | Datadog SaaS |
Prompt management
Edit the prompt in the UI. The running app fetches it. In Langfuse, pass the fetched prompt into the generation to link it, so each version gets cost, latency, and scores. Datadog Prompt Tracking records which prompt a span used and does not serve it.
| Langfuse | Datadog Agent Observability | |
|---|---|---|
| Who fetches | Python and JS/TS, then cached | Python (ddtrace>=4.13), LLMObs.get_prompt() |
| How you aim a version | Labels (production, staging, custom) | DD_ENV, numeric version, Feature Flags targeting |
| One prompt includes another | Prompt references | Concatenate in application code |
| Where it runs | Cloud or self-host | Datadog SaaS |
Using Langfuse and Datadog together
Keep Datadog for APM and infrastructure and add Langfuse for AI engineering.
The Python SDK sets up OpenTelemetry when you initialize the client. The default filter exports Langfuse SDK spans, gen_ai.* attributes, and known LLM instrumentors.
from langfuse import Langfuse
langfuse = Langfuse()To keep Langfuse spans out of Datadog, pass an isolated TracerProvider. JS/TS still registers a LangfuseSpanProcessor on your NodeSDK. A collector can also fan out one export to both backends.
Which should you choose
Langfuse when you want MIT self-hosting, years of queryable history, or purpose-built AI engineering workflows. Datadog Agent Observability for APM and LLM tracing in one Datadog deployment. Both when SRE keeps Datadog and the AI team uses Langfuse. See Using Langfuse and Datadog together.
Why teams move away from Datadog Agent Observability
- SaaS only. Datadog Agent Observability cannot be self-hosted and is unavailable on Datadog gov sites.
- Quality loop is Python-SDK-first. Datadog documents prompt fetch and experiment runs for Python. Online code evals go through the API or another framework. Langfuse runs code evaluators and UI and CI experiments on Cloud and self-host.
- 15-day default traces. Datadog Agent Observability includes 15 days. A 90-day window is a paid add-on on the AI SKU, with no published year-long tier. In the worked example, that is $875/month vs $621 on Langfuse Pro with 3-year history.
Why teams choose Langfuse
- Canva built a multi-agent support system for a product with 250 million monthly active users. Help Assistant is Java via OpenTelemetry; Omni Agent is Python via the Langfuse SDK. LLM-as-a-judge evaluators score both systems across 15–20 metrics, and domain experts build evaluators without engineering help. They self-hosted first, then moved to Cloud.
- SumUp rolled AI support to 35+ markets and deflected almost 50% of conversations, cutting external BPO cost by 30%. They started self-hosted for the PoC, then moved to Langfuse Cloud.
- Merck runs about 80 of 300+ GenAI use cases on Langfuse with 200+ people building on it. They self-host for data sovereignty and provision via API.
Switching from Datadog Agent Observability
Move live instrumentation and durable assets. Trace history usually stays in Datadog. Most teams see first traces in Langfuse the same day.
- Instrumentation: initialize the Langfuse SDK or send OpenTelemetry to the OTLP endpoint.
- Datasets, prompts, and evaluators: import datasets via CSV, SDK, or API. Recreate prompts and judges in prompt management and evaluators.
- Trace history: stays in Datadog for its retention window.
Talk to us if you want help planning a migration.
Start free: Cloud or self-host
Langfuse Cloud Hobby includes 50k units/month, no credit card. Or self-host the MIT stack. Explore the example project first. Already on Datadog Agent Observability? See Switching from Datadog Agent Observability.
FAQ
Is Langfuse an alternative to Datadog Agent Observability?
Yes. Langfuse is an open-source alternative to Datadog's AI observability SKU. See Which should you choose.
Can I use Langfuse and Datadog at the same time?
Yes. Keep Datadog APM and add Langfuse for LLM traces and the AI quality loop. See Using Langfuse and Datadog together.
How does Datadog Agent Observability pricing compare to Langfuse?
Datadog Agent Observability bills LLM inference spans (Pro $160/month billed annually for 100k spans, September 2026). Langfuse bills units from $29/month. On the rates in our editable public model, 500k traces / 10 observations / 2 LLM calls / 1 score per trace is $621 Langfuse Pro with 3-year history vs $875 Datadog Pro with the 90-day retention add-on. Details in the worked example.
Can I self-host Datadog Agent Observability?
No. Agent Observability is Datadog-hosted SaaS. Langfuse self-hosts on every tier.
Did joining ClickHouse change the Langfuse product?
No. Langfuse already ran on ClickHouse. The announcement states the MIT license, self-hosting, Cloud endpoints, and roadmap stay the same, with more capacity to ship. Cloud Core and Pro remain self-serve; OSS self-host has no sales motion. Details in clarifications.
How do I migrate from Datadog Agent Observability to Langfuse?
Point live traffic at Langfuse and recreate datasets, prompts, and evaluators. History usually stays in Datadog. See Switching from Datadog Agent Observability.
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