Token Observe vs Langfuse
Langfuse says in its own documentation that its SDKs are asynchronous and that blocking is a guardrail library’s job. That sentence is the whole comparison, and it is not a criticism.
2 September 2026
- Langfuse product page
- Observability overview (async ingestion)
- LLM security and guardrails
- Access control (RBAC)
- Audit logs
- Data retention
- Spend alerts
- Monitors and alerts
- Token and cost tracking
- Masking
- OpenTelemetry get started
- API rate limits
- Security and compliance overview
- Cloud pricing
- Self-hosted pricing
- Self-hosting licence key
Their claims, not our testing. Verify anything that decides it for you.
On this page
For most teams shopping for LLM observability, Langfuse is the better purchase, and the certification gap alone decides it for many
Start with what procurement asks for, because it ends the conversation for a large share of readers before any feature is discussed. Langfuse’s security page states that they undergo annual SOC 2 Type 2 and ISO 27001 audits and annual third-party penetration tests, that they are GDPR compliant and offer a DPA, and that a HIPAA-ready region is available; their cloud runs in three regions — us-west-2, eu-west-1 and ap-northeast-1 — on AWS and ClickHouse Cloud. Token Observe holds no SOC 2, no ISO 27001, no ISO 42001 and no independent penetration-test result, publishes a licence its own repository describes as a template pending counsel, and runs as a single-writer SQLite process on one host at its current target scale with no replica, no clustering and no vendor-operated uptime SLA. If your gate is an audit report, the comparison ends here in Langfuse’s favour and it is not close.
The second advantage is the daily loop a developer actually works in, and Token Observe does none of it. Langfuse’s product page describes hierarchical traces that capture every LLM call, tool invocation and retrieval step; evaluation by LLM-as-a-judge, heuristic functions or human review, run on production data or during experiments; prompt management with one-click deployments and rollbacks; and a playground for testing prompts on real production inputs and comparing models side by side. Their OpenTelemetry page positions Langfuse as an OTLP backend on the /api/public/otel endpoint over HTTP with JSON and protobuf, with instrumentation-library coverage tabulated across more than twenty-five LLM providers, nine vector databases and more than twenty frameworks. Token Observe has no prompt playground, no dataset management, no experiment runs and no evaluation harness; those were designed for in the data model and deliberately not implemented, and the product’s roadmap names another standalone tracing and evaluation product as a strategic non-goal rather than a backlog item. If the question you need answered is why a chain is slow or which prompt version regressed, Langfuse is the tool and Token Observe will not become it.
The third advantage is architectural and it is a genuine argument against buying Token Observe rather than a consolation. An asynchronous exporter cannot take your application down: Langfuse’s documentation says trace events are queued locally and flushed in batches so response time is not affected, which means a failure costs you visibility and nothing else, and adoption needs no operational conversation beyond a dependency. Token Observe sits in band and fails closed — a corrupt audit chain latches readiness and audit writes unavailable and governed requests receive a 503 carrying ACP_AUDIT_UNAVAILABLE, with no online clear — so its failures become your agents’ failures. That trade is deliberate, because a control you can switch off is not a control, but if the appetite in your organisation is for visibility without a new dependency in the request path then Langfuse is the correct choice and the honest recommendation. Everything said about Langfuse on this page comes from the pages listed in the sources, read on 2 September 2026, and none of it has been tested; where a row reads as an absence, treat it as a question to put to the vendor in writing rather than as a finding.
Token Observe and Langfuse, capability by capability
The Langfuse column paraphrases Langfuse’s own published material as it stood on 2 September 2026. None of it has been independently tested here, products in this category ship quickly, and a capability that is absent from a vendor’s documentation is not the same thing as a capability the product lacks. Check anything that decides it for you against their own current documentation.
Where it sits in the request path
SDKs and framework integrations inside your application, plus an OpenTelemetry backend: their docs describe Langfuse receiving traces on the /api/public/otel endpoint over OTLP HTTP in JSON and protobuf, with gRPC not yet supported.
A base-URL change in front of your providers: OPENAI_BASE_URL or ANTHROPIC_BASE_URL points at Token Observe, and for supported OpenAI-compatible, Anthropic and Gemini ingress that is normally a configuration change rather than an application refactor.
After it. Their observability overview: the SDKs “send tracing data asynchronously in the background”, with trace events “queued locally and flushed in batches, so your application’s response time is not affected”.
Before it. Eleven ordered steps — authenticate, resolve the agent, open the trace, sanitise Unicode, scan, take the verdict at step 6, enact it, route, call upstream, govern any proposed tool call, then meter and record.
These are two different design goals rather than two attempts at the same one. Keeping the exporter off the response path is how instrumentation stays safe to adopt; taking the verdict before egress is how a refusal becomes possible. Neither choice is free, and Token Observe pays for its side by failing closed.
Their masking page describes redacting sensitive information “before trace data leaves your application”, through mask_otel_spans or the legacy mask hook in the SDK, or in an OpenTelemetry Collector — that is, controlling what reaches Langfuse. Server-side data masking is listed on their self-hosted pricing page as an enterprise feature.
Detection at step 5 over prompt content, a redaction plan decided at step 6 and applied at step 7 to the outbound payload, so tokenised or blocked values do not reach the provider at all.
The two are answering different questions. Langfuse’s masking protects the observability store from data it should not hold; Token Observe’s redaction protects the provider from data your policy says it should not receive.
Recorded. Their product page describes hierarchical traces that “capture every LLM call, tool invocation, and retrieval step”, filterable by user, session, cost, latency or custom metadata.
Evaluated on the way back at step 10 against tool_call policies before the proposal reaches the caller, and authorised again at execution when the tool runs through the MCP gateway rather than only filtered out of the catalogue.
Their observability overview describes structured traces capturing “the exact prompt sent, the model’s response, token usage, latency, and any tools or retrieval steps in between”, sent asynchronously after the fact; a mechanism for withholding streamed bytes in flight is not described in their published documentation as of 2 September 2026.
Outbound streams pass a hold-back buffer with a 64-character floor and a separate channel per tool-call argument, and a blocking data class ends the stream with an in-band ACP_POLICY_BLOCKED frame — the response-side plan is fixed before the first byte, because a status line is spent once written.
What it enforces before the payload leaves
Third-party libraries do the blocking. Their security-and-guardrails page assigns “catching and blocking a potentially harmful or inappropriate prompt before sending to the model” to guardrail libraries — LLM Guard, Prompt Armor, NeMo Guardrails, Azure AI Content Safety, Lakera are the ones named — and describes Langfuse’s own role as “ex-post evaluation of the effectiveness of these measures”.
Detection is built in and heuristic: eleven sensitive-data classes with Luhn, IBAN mod-97 and NHS mod-11 checksums on three of them, and nine weighted injection patterns scored 1.25× when the text is a tool result. Narrower than a maintained detector, and it produces a verdict rather than a score.
RBAC over users, organisations, projects and roles. Their docs name five: Owner “has all permissions”, Admin “can edit the project settings and grant access to other users”, Member “can view all metrics & create scores, but cannot configure the project”, Viewer with view-only access, and None. Project-level roles override the organisation role; project-level RBAC is listed as an enterprise feature on their self-hosted pricing page.
Action-level and deny-by-default for the agent, not only for the human: Read: Customer Account and Update: Shipping Address may be granted while Delete: Account is simply absent and therefore denied. Explicit denies win, and delegation chains intersect permissions so one agent cannot escalate by asking a higher-privileged agent.
Both products use the word RBAC and mean different subjects. Langfuse’s roles govern who may read and configure the observability platform. Token Observe’s permissions govern what the agent may do to your systems, and are the thing the gateway enforces against.
Evaluation and review: their product page describes LLM-as-a-judge, heuristic functions or human review running on production data or during experiments, and “collaborative human-in-the-loop workflows to review traces and create golden datasets”, with unlimited annotation queues listed on the Pro plan.
A run-time gate. A policy can park a request on a named human, and the approval is bound to a SHA-256 of the canonical action plus its execution context, single-use, expiring — 60 minutes by default, configurable from one minute to seven days — with the caller receiving 403, the approval id, a status URL and a resume contract.
Alerting. Their monitors-and-alerts page describes an alert evaluating an observation metric such as cost over a lookback window it illustrates as an hour, a day or a week, with a required alert threshold and an optional warning threshold, notified to Slack, a webhook or GitHub Actions. Separately, spend alerts cover the Langfuse Cloud bill and their page states these “cover what you pay Langfuse for using Langfuse Cloud — not LLM or model costs you track in Langfuse observability”; those are emailed to Owners and Admins, at most once per billing cycle.
Hard USD ceilings per request, hour, UTC day and UTC month. Token Observe prices every provider and fallback the resolved route could execute and reserves the most expensive of them against the agent’s windows in one transaction before egress, so the ceiling refuses the call rather than reporting on it.
On traffic to Langfuse itself. Their API-limits page gives organisation-level buckets — tracing ingestion at 1,000 req/min on Hobby up to 20,000 on Pro, Team and Enterprise, with a general API bucket from 30 to 1,000 req/min — answering 429 with a Retry-After header that is “the authoritative number of seconds to wait”.
On the agent’s own traffic to providers and tools: requests, tool calls and tokens per minute per agent, alongside a kill switch scoped to one agent, one team, or everything.
What it records, and who is allowed to read it
Traces, observations and scores. Their pricing page defines a billable unit as “any tracing data point sent to the platform — including traces (complete application interactions), observations (individual steps: spans, events, and generations), and scores (evaluations)”.
One trace per governed request, opened at step 3 before the verdict, so a request refused at step 6 is still recorded, with the id returned to the caller on every response. Trace events are append-only and the full-text index covers redacted content only.
Ingested or inferred. Their token-and-cost-tracking page: you send usage and cost from the LLM response, or “Langfuse works them out from the generation’s model parameter, using a model definition that stores prices per usage type”, with ingested values taking priority. They ship prices for popular OpenAI, Anthropic and Google models and support your own definitions, which take priority over theirs.
Priced before egress from fifty shipped price rows loaded additively on every boot, then reserved against the agent’s windows in the same transaction as the decision, and reconciled after the call by normalising provider usage into a ledger entry — metering runs even when step 10 refuses a proposed tool call, because the tokens were spent either way.
Their audit-logs page describes immutable records capturing “Who: User or API key that performed the action, What: The specific action taken (create, update, delete), When: Precise timestamp”, plus organisation and project context, the role at the time, and complete before-and-after JSON state, readable with the auditLogs:read permission and exportable from the table. It states that the audit log viewer is “available in the Enterprise Edition”, and their licence-key page lists audit logs among the features a commercial key activates.
Hash-chained: each entry’s digest covers the previous entry’s hash plus the canonical JSON of its own content, so an alteration breaks verification at a named sequence number. Plain SHA-256 by default, HMAC-SHA256 under an audit MAC key held outside the database when one is configured, and Token Observe reports which of the two you are holding in every verification result and every export.
Tamper-evident is the correct word and tamper-proof is not: on a default install an operator with write access can rewrite an entry and recompute the downstream digests, and the repository ships a forgery test asserting exactly that. The MAC key and the Ed25519 anchor exist because of it.
Their audit-logs page describes the entries as immutable records; a cryptographic anchoring or hash-chaining mechanism is not described in their published documentation as of 2 September 2026.
Optional and off until configured: with a signing key set, Token Observe periodically signs a statement of the chain head with Ed25519, chains anchors to one another, and publishes each to a file or HTTP sink off the box. The claim that buys is narrow and is the only one made — any copy you kept off-box beats any rewrite made after you took it.
Roles decide it, and their security page describes RBAC applied “before queries are made” with every record scoped to a project by projectId. Enterprise SSO and fine-grained RBAC are sold on the Pro plan as a Teams add-on at $300/month, and the SCIM API is listed as a self-hosted enterprise feature.
Reads are themselves evidence: trace list, search, detail and export reads are attributable to the reader, spend figures and recertification evidence are gated on team scopes as well as role, and surfaces that join records with no trustworthy team key return 403 rather than a misleading partial view.
Configurable per project. Their data-retention page: “Data retention is configured on a project level, and we accept a number of days with a minimum of 3 days”, deleting traces, observations, scores and media assets nightly, and “Without a retention policy, Langfuse does not automatically delete event data.” Cloud plans carry a data-access window of 30 days on Hobby, 90 on Core and three years on Pro and Enterprise; retention policies are listed as an enterprise feature for self-hosting.
Trace retention is unset by default, and unset means keep forever. That is a deliberate default for an evidence store and a real operational cost, and it is the number to set before the first production week rather than after it.
How it deploys, and what reaches the vendor
Both. Their product page describes a managed cloud in US and EU regions and self-hosting with “Docker Compose, Kubernetes (Helm), and Terraform” templates; their security page names three cloud regions — us-west-2 for US and HIPAA, eu-west-1 for EU, ap-northeast-1 for Japan — on AWS and ClickHouse Cloud.
Self-hosted and bring-your-own-key only. There is no managed cloud, which removes a decision and also removes the option.
For Langfuse Cloud, the trace data you send. Their security page describes customers controlling “what reaches Langfuse and how long it stays” through masking, retention policies and deletion, and points to self-hosted Langfuse for stricter isolation.
Nothing. The vendor receives no product telemetry, phone-home data, prompts, keys or trace database; governed payloads leave your network only for the model and tool providers you configure, after policy and redaction, and the runtime data flow is documented for verification.
Their security page states annual SOC 2 Type 2 and ISO 27001 audits and annual third-party penetration tests, GDPR compliance with a DPA available, and a HIPAA-ready region; SOC 2 Type II and ISO 27001 reports are listed as a self-hosted enterprise entitlement.
None of those. No SOC 2, no ISO 27001, no ISO 42001, and no independent penetration test — said first rather than under questioning. A pre-purchase test is expressly permitted by the licence, with no gag clause and no pre-approval of results.
Broad, through instrumentation. Their OpenTelemetry page tabulates instrumentation-library support across more than twenty-five LLM providers, nine vector databases and more than twenty frameworks, via OpenLIT, OpenLLMetry, Arize and MLflow alongside the OTEL-native Langfuse SDK v4.
Six first-class upstreams — OpenAI, Anthropic, Google Gemini, OpenRouter, Amazon Bedrock and Azure OpenAI — plus any OpenAI-compatible endpoint you register. Policy, redaction, budgets and tracing are asserted to apply identically across them by a table-driven test over every provider kind.
The numbers are not comparable. Langfuse’s coverage is of things it can observe; Token Observe’s is of things it can sit in front of and refuse. A gateway’s list is always the shorter one.
Designed to stay out of the way: their overview states that batching and background flushing mean “your application’s response time is not affected”.
Fail-closed. A full streamed walk of the audit chain runs before the process listens, and a verdict of intrinsic corruption latches readiness and audit writes unavailable, after which governed requests receive a 503 carrying ACP_AUDIT_UNAVAILABLE. There is intentionally no online clear, so recovery means restoring a database whose chain and independently retained head verify.
What it costs and how it is licensed
Their licence-key page states that “All core Langfuse features and APIs are available in Langfuse OSS (MIT licensed) without any limits”, with enterprise features activated by setting LANGFUSE_EE_LICENSE_KEY on both containers.
A source-available licence the repository itself describes as a template pending counsel. Treat it as a question for your legal team rather than as settled, and ask for the reviewed version before signing anything.
Their self-hosted pricing and licence-key pages list project-level RBAC roles, protected prompt labels, data retention policies, audit logs, server-side data masking, UI customisation, organisation creators, the organisation management API with SCIM, and the instance management API.
One build. Policy, approvals, budgets, the audit chain, anchoring and the flight recorder are not tiered, which is easier to reason about and comes with no managed option and no published price list at all.
This row is the one to check before concluding you already have what you need. “We run Langfuse” usually means the MIT deployment, and on their own published line that deployment does not include audit logs, project-level RBAC or retention policies.
Published. Hobby is free with 50k units per month, 30-day data access and two users; Core is $29/month with 100k units and 90-day access; Pro is $199/month with three-year access and unlimited annotation queues; Enterprise is $2,499/month with custom rate limits, an uptime SLA, a dedicated support engineer and audit logs. A Teams add-on at $300/month adds enterprise SSO and fine-grained RBAC.
No published price list, and no managed cloud to price. That is a real disadvantage in a procurement process that wants a number on a page before it will take a meeting.
By tracing volume, graduated: $8 per 100k units from 100k to 1M, $7 to 10M, $6.50 to 50M and $6 above it, where a unit is any trace, observation or score sent to the platform.
Not metered by trace volume, because Token Observe holds the trace database you already own. The cost that scales is your own storage, which is why the keep-forever retention default is worth changing deliberately.
Tiered. Their pricing pages list community support on Hobby, in-app support on Core, prioritised support on Pro, and a dedicated support engineer with an uptime SLA on Enterprise; the self-hosted enterprise tier adds a private Slack channel, solutions-architect support and a support SLA.
No vendor-operated uptime SLA and no published response-time commitments. The support model does state one thing plainly: a deployment serving traffic happily but no longer recording is treated as a severity-one incident.
The asynchronous exporter is the architecture, and it is the right one for their job
Langfuse’s observability overview describes the mechanism in one sentence: the SDKs “send tracing data asynchronously in the background”, trace events are “queued locally and flushed in batches”, and the consequence is that “your application’s response time is not affected”. Everything else about the comparison follows from that. An exporter that runs after the response cannot refuse a request, cannot hold back a streamed token, and cannot make your agent wait for a decision — and none of those are things a developer instrumenting an agent wants it to do. The reason to state it so directly is that a feature grid can make the difference look like a missing checkbox when it is a deliberate and well-chosen constraint.
The guardrails page makes the same division explicit rather than leaving it to be inferred. It describes catching and blocking a harmful prompt before it reaches the model as the work of a guardrail library, names LLM Guard, Prompt Armor, NeMo Guardrails, Microsoft Azure AI Content Safety and Lakera, and positions Langfuse as the place you trace every request through your guardrail pipeline to see which checks triggered, score traces for toxicity, PII leakage and prompt injection with LLM-as-a-judge evaluators, and track security scores on a dashboard over time. Their own phrase for their contribution is “ex-post evaluation of the effectiveness of these measures”. Read that as an architecture statement, because that is what it is: the blocking belongs to something in the path, and Langfuse is not in the path.
Token Observe is what sits in that slot, and the ordering of its eleven steps is where the guarantees come from. Unicode sanitisation runs before any detector reads the payload, so smuggled invisible characters cannot make a scanner read a different string from the one the model will read. Detection runs before the verdict. The verdict is a single point rather than a set of middlewares that can each decide something different. On the response side, a plan is resolved before the first byte, because a streamed response cannot be re-decided once bytes are on the wire and the status line is spent as soon as it is written. The price of all of that is that Token Observe can break your traffic, and an exporter cannot. That is the trade, stated in both directions.
- What Langfuse’s docs say blocks
- A guardrail library in the request path — LLM Guard, Prompt Armor, NeMo Guardrails, Azure AI Content Safety or Lakera are the examples their page names.
- What Langfuse’s docs say Langfuse does
- Traces every request through that pipeline, scores traces with LLM-as-a-judge evaluators, and tracks security scores on dashboards over time — “ex-post evaluation”, in their words.
- What Token Observe adds beside it
- A single verdict at step 6 before egress, an approval bound to one exact payload, a hard USD ceiling reserved before the call, and a kill switch — the deterministic half, which bounds damage rather than detecting it.
Check which Langfuse you are running before you decide you already have this
The sentence that matters is on Langfuse’s own licence-key page: “All core Langfuse features and APIs are available in Langfuse OSS (MIT licensed) without any limits.” That is an unusually generous open-source line and it is the reason Langfuse is so widely deployed. It is also a line, and the features on the other side of it are precisely the ones a governance conversation reaches for. Their licence-key and self-hosted pricing pages list project-level RBAC roles, protected prompt labels, data retention policies, audit logs, server-side data masking, UI customisation, organisation creators, the organisation management API with SCIM, and the instance management API as requiring LANGFUSE_EE_LICENSE_KEY. Their audit-logs page says the same thing from the other direction: the feature is “available in the Enterprise Edition”.
This matters because “we already have Langfuse” is almost always a statement about the MIT deployment, and a buyer comparing a governance product against the free thing already running is comparing against something narrower than they believe. It is not a point against Langfuse — a commercial open-source company has to draw the line somewhere, and drawing it around administrative and compliance features while leaving the entire observability and evaluation product free is a defensible place to draw it. It is a point about the accuracy of the comparison, and the reader can settle it in about two minutes by checking whether their instance has a licence key set.
The equivalent disclosure on the other side is that Token Observe does not tier anything and also does not publish a price. Policy, approvals, budgets, the audit chain, anchoring and the flight recorder are one build. There is no managed option, no free hosted tier to try, and no number on a page for a procurement team to react to, which is a straightforward disadvantage against a vendor whose four cloud tiers are listed with prices. Where Langfuse’s enterprise features answer the question of who may administer the observability platform, Token Observe’s answer a different question — what an agent may do, to what, with whose approval, and under which ceiling — so the two lists are not alternatives even where the words overlap.
Developer telemetry and compliance evidence are stored differently on purpose
Telemetry is written for the team that owns the service and is usually readable by all of them; evidence carries a different set of obligations, and the two products reflect their respective audiences accurately. Langfuse’s RBAC model is built for the first: organisations contain projects, users hold a role at the organisation level and optionally at the project level, and the five roles run Owner, Admin, Member, Viewer and None, with their security page describing RBAC applied before queries are made and every record scoped to a project by projectId. That is a clean model for controlling who may configure a project and who may only read its metrics.
Token Observe’s record is built for a reader who arrives with a question and has to be able to prove the answer. Reads of the trace list, search, detail and export are attributable to the person who made them. Spend figures and recertification evidence are gated on the reader’s team scopes as well as their role. Surfaces that join records across teams with no trustworthy team key — the organisation-wide audit view, the executive dashboard, radar findings — require an explicit organisation-wide scope and return 403 rather than a partial view that would mislead. Search never writes SQL: a question in English becomes a validated filter object over fourteen allow-listed fields, shown back as editable chips, with a deterministic keyword parser answering when no translation model is configured. The stated cost of that constraint is that the filter cannot group, count or correlate, so which agents used the same card number twice is not a question you can ask.
Sealing works the same way, and the wording is worth keeping precise because a security reviewer will check it. Langfuse’s audit-logs page describes immutable records with before-and-after JSON state, exportable from the table, available in the Enterprise Edition. Token Observe hash-chains every governance-plane change so that an alteration breaks verification at a named sequence number, upgrades those digests to HMAC-SHA256 when an audit MAC key is held outside the database, and can sign a statement of the head with Ed25519 to a sink off the box. What that buys is exactly one thing: any copy of an anchor you kept off-box beats any rewrite made after you took it. Compliance exports are SHA-256 digest-sealed over canonical JSON and carry the chain verdict; they are not themselves signed, and the chain is tamper-evident rather than tamper-proof. Saying it the other way round would be the kind of claim that fails the first review it meets.
- Their audit trail
- Who, what, when, organisation and project context, the role at the time, and full before-and-after state — a viewer their page places in the Enterprise Edition, readable with auditLogs:read.
- Token Observe’s audit trail
- A hash chain that names the sequence number of any break, keyed when a MAC key is configured, optionally anchored with Ed25519 to a sink the database administrator cannot reach.
- What neither of them is
- Tamper-proof. Token Observe’s repository ships a test asserting that a rewrite-and-recompute on a default install passes verification, which is why the keyed and anchored layers exist above it.
Where each one is the right answer
Choose Langfuse when
- The question you need answered is why a chain is slow, why answer quality dropped, or which prompt version regressed — Langfuse publishes hierarchical traces, evaluators, experiments and a playground for exactly that loop, and Token Observe has none of them.
- Procurement needs an audit report. Langfuse publishes annual SOC 2 Type 2 and ISO 27001 audits and annual third-party penetration tests; Token Observe holds no certification and no independent test result.
- You cannot accept a component in the request path that fails closed, which is a reasonable position for an estate whose agents draft text a human reads before anything happens.
- You want a managed service with a price on the page and a free tier to try, or a HIPAA-ready region, all of which Langfuse publishes and Token Observe does not offer.
- Your instrumentation is already OpenTelemetry-deep across frameworks and vector stores, and what you want is more of that rather than a decision point in front of your providers.
Choose Token Observe when
- You need the payload stopped rather than annotated, because the value leaving your network is itself the incident.
- A request has to wait for a named human, and the approval has to be bound to that exact payload, single-use and expiring, rather than recorded as a review afterwards.
- The ceiling has to refuse the call. An alert that a threshold was crossed is a notification; a reservation taken before egress is a limit.
- The reader of the record is an auditor or a compliance officer, reads of that record need to be attributable to a named person, and the export needs to carry a chain verdict.
- You already run Langfuse, the visibility is good, and the gap you have found is enforcement rather than instrumentation.
When you would run both
Running both is the normal answer, and the integration is one-directional in a way that keeps it simple: Token Observe governs the request and Langfuse observes the application, with no need for either to hold a credential into the other. Point your agents at Token Observe with a base-URL change so that policy, redaction, approvals and budgets bind before egress, and keep your Langfuse SDKs and OpenTelemetry instrumentation exactly where they are, because their asynchronous exporter is unaffected by a gateway in front of the provider and continues to answer the developer questions Token Observe was never built for. Token Observe’s own OTLP over HTTP receiver takes bounded JSON and protobuf for logs, traces and metrics as an input rather than as a competing destination — it feeds the observed view in a reconciliation against what was declared, locked and deployed — so telemetry you already emit can serve both without a second exporter. The product’s roadmap names another standalone tracing and evaluation product as a strategic non-goal, which is the clearest available statement that this is a complement rather than a replacement: if you rip out Langfuse to install Token Observe, you will have traded a tool your engineers use daily for one your compliance officer uses quarterly, and you will want the first one back inside a fortnight.
The category argument sits above this one: Token Observe and llm observability covers what the whole category does and does not do, which is the better page to read if you have not yet shortlisted a product.
The others in the same slot
LangSmith
LangSmith’s callback handler watches the call from beside it, and their newer LLM Gateway now stands in it too. The remaining differences are narrower than they were, and worth stating precisely.
Arize
Arize is instrumented into your application and reads what it did. Token Observe is a hop your agents call through and decides what they may do.
Datadog LLM Observability
Datadog puts LLM spans beside the rest of your telemetry. Token Observe puts a verdict in front of the call. Datadog also sells a verdict — and where it is made is the whole comparison.
Braintrust
Braintrust is in the request path too. What it does there is deliver the call and record it; what it blocks is the release that would have made the call worse.
Does Token Observe replace Langfuse?
No, and the product’s roadmap forbids it: another standalone tracing and evaluation product is a named strategic non-goal. Token Observe records governed requests, their tool calls, their policy decisions, their approvals and their cost, but it has no prompt management, no datasets, no experiment runs, no playground and no evaluation harness, and it is not going to grow them. Langfuse’s product page describes all of those, and if you remove it you lose the loop your engineers work in every day. The intended shape is both: Langfuse answers why the agent behaved that way, Token Observe decides whether it may and holds the evidence that it did.
Langfuse has guardrail documentation. Doesn’t it already block?
Their documentation assigns the blocking to something else. The security-and-guardrails page describes catching and blocking a harmful prompt before it reaches the model as the work of a guardrail library — it names LLM Guard, Prompt Armor, NeMo Guardrails, Azure AI Content Safety and Lakera — and describes Langfuse’s own contribution as tracing every request through that pipeline, scoring traces with evaluators, and providing “ex-post evaluation of the effectiveness of these measures”. That is consistent with the rest of their architecture, where the SDKs send data asynchronously in the background so response time is not affected. If you want Langfuse to be the thing that refuses, ask them in writing what it is bound to and at which point in the path it runs; this page has not tested any of it.
We self-host Langfuse under MIT. What are we actually missing?
On Langfuse’s own published line, the enterprise-only list includes project-level RBAC roles, audit logs, data retention policies, server-side data masking, protected prompt labels, UI customisation, organisation creators, the organisation management API with SCIM and the instance management API, all activated by setting LANGFUSE_EE_LICENSE_KEY. Everything else — observability, evaluation, prompt management, datasets and the APIs — is described as available in Langfuse OSS under MIT without limits. Check whether your instance has a key set before you conclude that you already hold the administrative and compliance features, because the answer changes what you are comparing against. Separately, and regardless of tier, nothing in their published material describes Langfuse refusing a request inline.
Both products track cost. What is the difference?
The difference is whether the number can refuse anything. Langfuse establishes cost either from usage you send with the LLM response or by inferring it from the generation’s model parameter against a model definition holding prices per usage type, and then alerts on it — their monitors page describes an alert evaluating an observation metric such as cost over a lookback window it illustrates as an hour, a day or a week, with a required alert threshold and an optional warning threshold notified to Slack, a webhook or GitHub Actions, and their spend alerts cover the Langfuse Cloud bill rather than model spend. Token Observe prices every provider and fallback the resolved route could execute, reserves the most expensive of them against the agent’s per-request, hourly, daily and monthly windows in the same transaction as the decision, and refuses the call when a ceiling would be crossed. Both numbers are useful; only one of them is a limit.
Is anything on this page tested?
No. Every statement about Langfuse is paraphrased from the pages listed in the sources, read on 2 September 2026, and none of it has been independently verified — the same caveat the product’s own competitive benchmark states about itself. Langfuse ships quickly, and a grid about somebody else’s product is a set of assertions with a shelf life, which is why the date sits beside the table rather than in a footnote. Where a cell says a capability is not described in their published documentation, that is a statement about their documentation on that date and not a finding about their product; put it to the vendor as a question and ask for the answer in writing.
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If Langfuse is already in your stack, the useful question is not which to buy but what each is for, and where the seam between them sits. Say what you have and you will get a straight answer — including when the answer is that you do not need a second thing.
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