FastRouter vs. Langfuse

They're complementary tools.

Langfuse is the leading open-source LLM observability and eval platform. MIT-licensed, OpenTelemetry-friendly, with mature traces, sessions, scores, datasets, experiments, LLM-as-judge evaluators, prompt management with versioning, and human annotation queues. It does not proxy LLM calls — your application calls models directly and instruments those calls with the Langfuse SDK or any OTel exporter.

FastRouter is a gateway. Your application calls FastRouter, FastRouter calls the model providers, and the gateway enforces routing strategies, budgets, evaluations, and credential vaulting along the way. The eval surface inside FastRouter (Smart Evaluations, Automatic Evaluations, GEPA, video evals) overlaps with Langfuse's eval surface — but FastRouter doesn't replace Langfuse for teams that want a dedicated experimentation, dataset, and prompt-management workspace.

Three real configurations: (1) FastRouter alone — gateway + on-traffic evals are enough; (2) Langfuse alone — you don't need a gateway, you instrument direct provider calls; (3) Both — FastRouter as the gateway, Langfuse as the dedicated eval/observability workspace, OTel between them.

Naming the boxes the two products live in

Feature matrix

Where the two diverge today. ✓ supported, ✗ not supported, ◑ partial

One area, three differences

The actual overlap is the eval and observability layer. Both products will give you traces, sessions, cost tracking, and LLM-as-judge scores. The substantive differences are:

• How evals run. FastRouter's evals are continuous, on-traffic, and feed back into routing decisions automatically — they're a substrate the gateway uses to make better choices. Langfuse's evals are workflow-driven — you define them, run them against datasets or live traces, and review the results in a dedicated workspace.
• How prompts are managed. Langfuse's prompt library is best-in-class for the experimentation workflow: client-side caching, labels for deploy environments (production, staging, prod-a, prod-b), diff views across versions, A/B testing built in. FastRouter's prompt management is functional and tied to GEPA optimization.
• How datasets and experiments work. Langfuse has a dedicated dataset + experiment system with regression detection across runs. This is the part of Langfuse that doesn't have a parallel in FastRouter today.

If your team already runs eval and prompt experimentation as a dedicated practice — your ML engineers maintain golden datasets, run regression checks before deploys, manage prompt versions deliberately — Langfuse adds a workspace your team will use. If you're earlier in the journey and want the gateway to make most of these decisions automatically, FastRouter's on-traffic evals plus GEPA optimization may be enough.

The gateway primitives Langfuse does not have

Langfuse is purposely not a gateway. The team has been clear about this: traces are sent post-hoc, not on the request path. That means Langfuse cannot:

• Route requests across providers. No multi-provider failover, no per-request model selection, no category-based routing.
• Hold or vault provider credentials. No BYOK enforcement, no MCP credential vaulting. Your application still holds the keys.
• Enforce hard budget kill-switches. Cost tracking is robust; the gateway-level "stop accepting calls when this workspace hits $X this month" enforcement is not in scope.
• Cache responses across providers (semantic or simple).
• Apply guardrails / PII redaction at the request boundary. Observability sees the data; it does not modify the call.

None of this is a defect — it's a consequence of the architectural choice to be off the request path. Langfuse gets latency, reliability, and async-ingestion benefits from that posture. The trade-off is that it cannot do what gateways exist to do.

Langfuse sees what happened. Fastrouter changes what happens.

The third architecture: both

For teams that want a serious gateway and a dedicated experimentation workspace, the two products coexist cleanly. The pattern looks like this.

Fastrouter vs Langfuse

FastRouter handles the gateway responsibilities (routing, BYOK enforcement, budget caps, MCP credential vaulting, on-traffic evals) and exports OpenTelemetry traces to Langfuse. Langfuse holds the offline experimentation workspace — golden datasets, regression checks, human annotation queues, prompt versioning with explicit labels. Your team uses Langfuse the way they'd use a notebook or eval harness, while FastRouter handles production traffic.

This is the configuration we see most often in mature stacks. It's also the easiest place to start if you're not sure which one you need — they don't overlap enough to step on each other's toes.

How the eval surfaces actually compare

Langfuse's eval system is broad and workflow-flexible:

• LLM-as-judge with templates — hallucination, helpfulness, relevance, toxicity, correctness, context relevance, conciseness
• Custom evaluators in code
• Dataset experiments — run your app against golden data, score with LLM-as-judge or custom scorers
• Session-level scoring for multi-turn conversations
• Human annotation queues for domain-expert review

FastRouter's eval system is narrower in workflow flexibility but deeper in automation:

• Smart Evaluations score live production calls automatically — no need to define datasets or scoring functions
• Automatic Evaluations run continuous benchmarks of competing models on a slice of real traffic in the background
• GEPA prompt optimization searches across prompt and model combinations toward Pareto-optimal cost/quality
• Video evaluations extend this to a content type Langfuse doesn't currently cover

If your eval question is "which model is currently winning on my live workload, and what's the optimal prompt for it" — FastRouter's evals are designed to answer that without you defining anything. If your eval question is "did this prompt regress versus the golden dataset on yesterday's run" — that's Langfuse's home turf.

When Langfuse alone is the right call

You don't want a gateway in front of your LLM calls

• You're calling providers directly and want to keep it that way
• Latency, reliability, and complexity simplicity are priorities
• You're early enough that one provider covers most use cases

You need open-source / self-host

• MIT license, full source on GitHub
• Postgres + ClickHouse + Redis stack you can operate
• Air-gapped or sovereign deployment supported

Eval workflow is the dominant need

• Dataset experiments + regression detection
• Human annotation queues for domain experts
• Custom evaluators written in code

You want best-in-class prompt management

• Client-side cached prompt library
• Label-based deploys (production, staging, prod-a/b)
• A/B testing on labeled versions

When FastRouter alone is the right call

You need routing across multiple providers

• 7 routing strategies including AI Auto Model Router
• Per-request model selection from cost/latency/quality
• Provider failover and load balancing

You need budget enforcement, not just tracking

• Workspace-level kill-switches
• Hard caps that stop accepting calls
• BYOK with credential vaulting

On-traffic evals are enough

• Smart and Automatic Evaluations cover most needs
• GEPA prompt optimization runs continuously
• You'd rather not run a separate eval workspace

You're running agentic / MCP workloads

• MCP credential vaulting — agents never see raw keys
• Per-tool budget caps and rate limits
• Audit trail across multi-step agent runs

Pick the architecture, not the product

1) IF -> You don't want a gateway and observability + evals is the entire need

Use Langfuse alone. SDK in your app, instrument calls, done. Cloud or self-host depending on your infra preferences.

2) IF -> You want a gateway and FastRouter's on-traffic evals cover your eval needs

Use FastRouter alone. Smart + Automatic Evaluations + GEPA replace the dedicated eval workspace for most teams.

3) IF -> You want both — gateway in production and a dedicated eval workspace

Use both. FastRouter on the request path, Langfuse as the offline workspace, OpenTelemetry between them. This is the most common mature configuration.

4) IF -> You're already on Langfuse and considering whether you also need a gateway

Run a FastRouter audit for 7 days against a slice of your traffic. The output will tell you what routing efficiency and cost savings would look like — independent of whether you keep Langfuse for evals.

5) IF -> You're using LiteLLM + Langfuse today and one of them is starting to hurt

If LiteLLM operations are the pain, swap to FastRouter while keeping Langfuse. If Langfuse is fine and you don't need the eval workspace, swap LiteLLM for FastRouter and drop Langfuse.

Common questions

1) Does FastRouter integrate with Langfuse?

Yes. FastRouter exports OpenTelemetry traces using the GenAI semantic conventions; Langfuse is a native OTel backend. You can run both products with no extra wiring beyond pointing FastRouter's OTel exporter at your Langfuse endpoint.

2) Should I keep Langfuse if I move to FastRouter?

It depends on how your team uses it. If you actively run dataset experiments, regression checks, or human annotation queues, keep it — FastRouter's eval surface is narrower in those workflows. If you mostly used Langfuse for traces and basic LLM-as-judge scoring, FastRouter's built-in evals likely cover that.

3) Can Langfuse replace FastRouter?

No — Langfuse is intentionally not a gateway. It does not proxy LLM calls, does not hold provider credentials, does not enforce budget kill-switches, does not route between providers, does not vault MCP credentials. If you need any of those, you need a gateway alongside Langfuse.

4) Can FastRouter replace Langfuse?

For most teams, yes — observability, sessions, traces, on-traffic evals, GEPA prompt optimization, and prompt versioning are all in FastRouter. The gaps are workflow-shaped: dedicated dataset experiments with regression detection, human annotation queues, and the "eval workspace" UX. If your team uses those daily, keep Langfuse alongside.

5) What's the LiteLLM + Langfuse comparison to FastRouter?

"LiteLLM + Langfuse" is essentially "self-hosted gateway + observability." FastRouter alone covers the gateway plus most of the observability and eval surface, without the operational tax of running LiteLLM yourself. Compared to that combined stack, FastRouter is the managed equivalent.

6) What about Langfuse on self-host vs Cloud?

OSS self-host is free in license — Postgres + ClickHouse + Redis are required. Cloud free tier is 50K units/month, paid plans scale from there. Self-hosting at production scale typically lands in the $3K–$4K/mo range once you account for infra and engineering time, vs Cloud Pro plans in the $200–$300/mo range. Pick on the same managed-vs-self-hosted axis as any other observability platform.

7) Does Langfuse work with OpenAI-compatible gateways like FastRouter?

Yes. Langfuse instruments the call regardless of where it goes — direct provider, FastRouter, LiteLLM Proxy, Portkey. The trace just records what was called and how it performed. The integration with FastRouter is one OTel exporter config.