Grepture vs. Langfuse: AI Gateway vs. Observability Platform

TL;DR

Langfuse is an open-source LLM engineering platform focused on tracing, evals, prompt management, and datasets. It observes your AI calls via SDK instrumentation — it sees everything but doesn't touch the data flowing to providers.

Grepture is an open-source AI gateway that sits on the hot path between your app and LLM providers. It can actively intercept, redact PII, block threats, and log every request — while also offering a trace-only mode for lightweight observability.

The core difference: Langfuse watches. Grepture watches and acts.

At a glance

Architecture: gateway vs. tracing SDK

This is the fundamental difference — and it shapes everything else.

Langfuse is a tracing platform. You instrument your code with their SDK (or one of 50+ framework integrations), and it sends trace data asynchronously to Langfuse's backend. Your LLM calls flow directly from your app to the provider. Langfuse never touches the actual request or response — it receives a copy for analysis.

import { Langfuse } from "langfuse";
import OpenAI from "openai";

const langfuse = new Langfuse();
const openai = new OpenAI();

const trace = langfuse.trace({ name: "chat" });
const generation = trace.generation({ name: "completion", input: messages });

const result = await openai.chat.completions.create({ model: "gpt-4o", messages });
// Data flows directly to OpenAI — Langfuse gets a copy

generation.end({ output: result });

Grepture is a gateway. In proxy mode, every request flows through Grepture on its way to the LLM provider. This means Grepture can scan, redact, block, and transform requests before they reach the model — and restore redacted values in the response on the way back.

import OpenAI from "openai";
import { clientOptions } from "@grepture/sdk";

const openai = new OpenAI(clientOptions());
// Every request now flows through Grepture — scanned, redacted, logged
const result = await openai.chat.completions.create({ model: "gpt-4o", messages });

What this means in practice: Langfuse can tell you that a user sent their SSN to GPT-4 last Tuesday. Grepture would have caught that SSN and replaced it with a token before it ever reached OpenAI — and restored it in the response so your app still works.

Trace mode: observability without the proxy hop

Not every team needs inline data protection from day one. Some want observability first.

Grepture's trace mode works similarly to Langfuse — it captures request and response data for the dashboard without routing traffic through the proxy. Zero added latency, same tracing and cost-tracking features.

The difference is that trace mode and proxy mode share the same dashboard, the same eval pipeline, and the same prompt management system. When you're ready to add PII redaction or prompt injection detection, you flip the mode — no migration, no new tool, no second dashboard.

With Langfuse, adding a proxy layer means bringing in a separate tool (typically LiteLLM), managing a second system, and stitching the data together.

Observability and tracing

Both tools provide observability, but with different depth and focus.

Langfuse has the more mature tracing product. Nested observation trees show the full request flow — including tool calls, retrieval steps, and agent chains. Session tracking groups multi-turn conversations. User tracking ties traces to specific users. Their dashboard surfaces latency, cost, and quality metrics. The breadth of framework integrations (LangChain, LlamaIndex, Vercel AI SDK, and dozens more) means you can instrument almost any stack with minimal effort.

Grepture provides full-text search across all prompts and responses, waterfall timelines for multi-step agent traces, request replay, and before/after diff views showing exactly what was redacted. The tracing is tightly integrated with the security layer — you see not just what was sent, but what was caught, blocked, or redacted along the way.

Verdict: If your primary need is deep tracing across complex agent workflows with many framework integrations, Langfuse has the edge. If you want observability that's connected to active data protection, Grepture gives you both in one view.

PII redaction and data protection

This is where the architectures diverge most sharply.

Grepture redacts PII inline — before data reaches the LLM provider. The Free plan includes 50+ regex patterns for structured PII (emails, phone numbers, credit cards, SSNs). The Pro plan adds AI-powered detection for names, organizations, and addresses. The Business plan includes prompt injection detection, toxicity scanning, and data loss prevention.

Mask-and-restore is a core feature: Sarah Chen becomes [PERSON_a3f2] on the way out, the model processes sanitized text, and Grepture restores the original on the way back. Your app receives complete, personalized responses. The model never sees real PII.

Langfuse offers data masking — client-side (strip fields before sending to Langfuse) and server-side (Enterprise tier, mask data in stored traces). This is for protecting data in your observability platform, not for preventing data from reaching the LLM. By the time Langfuse sees the trace, the PII has already been sent to OpenAI, Anthropic, or whoever your provider is.

Bottom line: These aren't competing features. Langfuse masks data in traces. Grepture prevents PII from reaching the model in the first place. If regulatory compliance or data protection is a priority, a tracing tool alone doesn't solve the problem.

Prompt management

Both tools offer prompt management, with similar core capabilities and some differences in workflow.

Langfuse has a polished prompt management system. Text and chat prompt types, {{variable}} templates, version history, and label-based deployment ("production", "staging"). You can compare performance metrics across versions and run prompt experiments against datasets without writing code. The LLM Playground lets you test prompts interactively.

Grepture takes an API-first approach. Stable slugs, automatic versioning with full history, {{variable}} templates with type validation and defaults, and runtime fetching via SDK or REST. A/B experiments with weighted variant distribution let you gradually roll out prompt changes. You can run experiments with evaluators to compare prompt versions side by side — seeing how a change affects quality scores before going all-in. The dashboard includes a diff view between versions. Prompt updates don't require redeploys.

Verdict: Both handle the core workflow well. Langfuse has a playground for interactive testing. Grepture's evaluator-backed experiments and weighted A/B testing make it easy to measure the impact of prompt changes before full rollout.

Evals

Both tools support LLM-as-a-judge evaluation, but Langfuse goes further in evaluation flexibility.

Langfuse offers LLM-as-a-judge, human annotation with configurable queues, custom evaluation scores (numeric, boolean, categorical), and external evaluation pipelines via API. You can evaluate at the trace level or on individual observations. The annotation queue workflow is well-designed for teams that need human review alongside automated scoring.

Grepture provides LLM-as-a-judge with six pre-built templates (relevance, helpfulness, toxicity, conciseness, instruction-following, hallucination), custom judge prompts, configurable sampling, and quality badges on traffic logs. Evals run in the background with zero impact on proxy latency. A/B testing integrations auto-create evaluators for prompt experiments.

Verdict: If you need human annotation workflows or complex evaluation pipelines, Langfuse is the stronger choice. If you want automated quality scoring on live traffic with minimal setup, Grepture's approach is simpler to get started with.

Datasets and experiments

Langfuse has a significantly more developed datasets feature. You can create datasets from production traces, CSV uploads, or manual entry, validate with JSON Schema, organize in folders, version them, and run experiments comparing model or prompt performance across dataset versions. The prompt experiment runner works without writing custom code.

Grepture supports datasets with a unique angle: rule-based automatic dataset creation. You define rules (e.g., "all requests that triggered a PII detection" or "all requests with a toxicity score above 0.8"), and matching traffic is automatically added to a dataset. You can also create datasets manually. Datasets integrate with the eval and prompt experiment workflow — run evaluators against a dataset to compare prompt versions or model changes.

Verdict: Different strengths. Langfuse has more flexibility for manual dataset curation (CSV uploads, JSON Schema validation, folder organization). Grepture's automatic rule-based creation is powerful for teams that want datasets built from live traffic patterns without manual effort.

Cost tracking

Both tools track token usage and estimate costs.

Grepture provides per-request token breakdowns, per-model cost estimation, cost attribution by endpoint and model, spend trends, and exportable cost reports. This works in both proxy and trace modes.

Langfuse automatically calculates costs from token usage with support for custom model pricing. Dashboard metrics show cost trends over time. Cost tracking is tied into their tracing system.

Verdict: Comparable. Both give you the visibility you need. Grepture's export feature is useful for finance reporting.

Integration and setup

Langfuse has the broader integration ecosystem. Python and JavaScript SDKs, OpenTelemetry support for other languages, and 50+ framework-specific integrations (LangChain, LlamaIndex, Vercel AI SDK, CrewAI, Haystack, and many more). If you're using a popular LLM framework, there's probably a Langfuse integration.

Grepture takes a different approach. In proxy mode, it works with any language or framework that makes HTTP calls — no SDK required, no per-framework instrumentation. The @grepture/sdk provides TypeScript-first convenience, and clientOptions() works as a drop-in with OpenAI and Anthropic SDKs. Special integrations exist for Claude Code and Cursor.

Verdict: If you need deep tracing in a specific framework (LangChain, LlamaIndex), Langfuse's native integrations are valuable. If you want a language-agnostic solution that works at the HTTP level, Grepture's proxy approach requires less per-framework setup.

Self-hosting

Both tools are open source and self-hostable, but the operational complexity is very different.

Langfuse requires PostgreSQL, ClickHouse (OLAP analytics), Redis/Valkey (caching and queues), and S3-compatible blob storage. The recommended production setup uses Kubernetes with Helm charts. Enterprise features (RBAC, audit logs, server-side masking) require a paid license key.

Grepture is a single Bun server with a Supabase (Postgres) backend. Simpler to deploy, fewer moving parts.

Verdict: Grepture is easier to self-host. Langfuse's ClickHouse-backed architecture scales better for very high trace volumes but comes with more operational overhead.

Pricing

Both tools offer free tiers and usage-based paid plans. Langfuse starts at $29/month (Core) and scales to $199/month (Pro) and $2,499/month (Enterprise). Grepture starts at €49/month (Pro) and scales to €299/month (Business).

The key difference isn't the price — it's what's included. Langfuse's pricing covers observability, prompt management, and evals. Grepture's pricing covers all of that plus the gateway layer, PII redaction, secret scanning, and security features. With Langfuse, adding a proxy and data protection means paying for additional tools on top.

Who Langfuse is best for

• +Teams that need deep tracing across complex agent workflows with nested observation trees
• +Teams heavily using specific LLM frameworks (LangChain, LlamaIndex) that benefit from native integrations
• +Teams that need human annotation workflows for evaluation
• +Teams that need offline dataset management and experiment runners
• +Teams already using LiteLLM as their proxy and wanting a dedicated tracing backend
• +Organizations that want a large open-source community (25K+ GitHub stars) and broad ecosystem

Who Grepture is best for

• +Teams that need active data protection — PII redaction, secret scanning, prompt injection blocking — not just observation
• +Teams that want observability and security in one tool instead of stitching Langfuse + LiteLLM + a PII tool together
• +Teams that want the flexibility to start with trace mode and upgrade to proxy mode without switching tools
• +Organizations with compliance requirements (GDPR, EU AI Act) that need PII to be redacted before it reaches the LLM
• +Teams using multiple AI providers that want a single gateway with built-in observability

FAQ

Is Langfuse free?

Langfuse is open source under the MIT license. The cloud Hobby tier is free with 50,000 events per month and 30-day retention. Paid plans start at $29/month. Self-hosting is free but requires PostgreSQL, ClickHouse, Redis, and blob storage.

Does Grepture support tracing without a proxy?

Yes. Grepture's trace mode captures observability data without routing traffic through the proxy — zero added latency. You get the same dashboard, cost tracking, and eval features, with the option to switch to proxy mode when you need active data protection.

Can Langfuse redact PII from prompts?

Langfuse offers client-side and server-side data masking (server-side requires Enterprise). These mask fields in your traces — they don't prevent PII from reaching the LLM provider. For inline redaction before data hits the model, you need a proxy layer.

Does Langfuse have a proxy or gateway?

No. Langfuse is a tracing and observability platform. For proxy functionality, they recommend integrating with LiteLLM. Grepture combines both the gateway and observability layer in one tool.

Can I use Grepture and Langfuse together?

You can, but most teams won't need to. Grepture covers tracing, evals, prompt management, and cost tracking alongside its gateway features. Using one tool is simpler than operating two.