Best Langfuse Alternatives in 2026 (Lighter Setup, Same Visibility)

Langfuse is one of the most widely used LLM observability tools, and for good reason. It is open source, actively maintained, and covers a broad feature set.

But if you have tried to set it up recently, you may have noticed it has gotten more complex. The self-hosted Docker Compose stack now runs six containers. The SDK integration requires wrapping your existing OpenAI client. And if you outgrow the free tier, the Pro plan is $199 per month.

For teams that want straightforward LLM logging without a large infrastructure commitment, it is worth knowing what else is out there. This post covers the top Langfuse alternatives in 2026, comparing setup complexity, integration style, and cost.

Quick Comparison

Why Developers Look for Langfuse Alternatives

Langfuse is a solid tool, but it comes with trade-offs that push some teams toward alternatives.

1. Self-hosting runs six containers

Langfuse’s self-hosted stack requires six services running simultaneously: a web server, a background worker, PostgreSQL, ClickHouse, Redis, and MinIO. For teams that want to run observability on their own infrastructure, that is a meaningful operational surface to maintain.

2. SDK wrapping requires code changes

To use Langfuse, you typically need to replace your OpenAI import with the Langfuse-wrapped version. That means modifying existing code and updating call sites throughout your codebase. For teams with large codebases or strict code review processes, this adds real friction.

3. Pricing at scale

Langfuse’s free tier is generous for small projects. The jump to Pro is $199 per month. For early-stage teams or individual developers, that is a significant cost before product-market fit.

4. Steeper learning curve

Langfuse uses a layered data model: traces contain observations, which contain scores and metadata. It is expressive, but it takes time to understand and configure correctly. Teams that just want to log LLM calls and see cost breakdowns sometimes find it more than they need.

Top Langfuse Alternatives in 2026

1. Spanlens

Spanlens is an open-source LLM observability platform built around the proxy model. Instead of wrapping your SDK, you point your existing OpenAI client at a different baseURL. Your existing code keeps working. Spanlens logs requests in the background.

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: "https://api.spanlens.io/proxy/openai/v1",
  defaultHeaders: {
    "Authorization": `Bearer ${process.env.SPANLENS_API_KEY}`,
  },
});

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: "https://api.spanlens.io/proxy/openai/v1",
  defaultHeaders: {
    "Authorization": `Bearer ${process.env.SPANLENS_API_KEY}`,
  },
});

That is the entire integration. No imports to change, no call sites to update.

Once connected, the dashboard shows request volume, spend, latency, error rates, token usage, and cost broken down by model.

What Spanlens tracks:

• Request and response payloads
• Token usage and cost per model
• Latency percentiles and error rates
• Agent spans and traces for multi-step workflows

Pricing: Free tier available. Self-hosting supported with Docker. MIT licensed.

Best for: Teams that want observability with zero code changes, accurate cost tracking, and a straightforward self-hosting path.

2. Helicone

Helicone is another proxy-based observability tool with a similar integration model. Like Spanlens, it works by routing requests through a proxy rather than wrapping the SDK. Helicone supports OpenAI, Anthropic, and several other providers. It has a clean dashboard and a generous free tier.

Best for: Teams that want a well-established proxy-based tool with broad provider support and a large community.

3. Braintrust

Braintrust takes a different angle. It is primarily focused on evaluations: running structured tests against your prompts and tracking results over time. It supports LLM logging, but evaluations are the core use case.

Best for: Teams focused on prompt testing and structured evaluations, where production request logging is secondary.

When Langfuse Is Still the Right Choice

Langfuse is worth staying with if:

• You need a mature tool with a large community and long track record
• You are already invested in its data model and SDK integrations
• You need advanced scoring, human feedback loops, or dataset management
• Your team has the infrastructure capacity to run and maintain the full stack

Langfuse’s feature depth is genuine. If observability is a first-class concern and you have the bandwidth to support it, it is a strong choice.

When Spanlens Fits Better

Spanlens makes sense when:

• You want observability without touching existing code
• You are moving fast and do not want to manage a large infrastructure stack
• You need real-time cost tracking across models without extra configuration
• You prefer fully open-source tooling with a straightforward self-hosting path

The Bottom Line

Langfuse is a great tool, but it is not the only option. If your priority is getting visibility into your LLM calls quickly without a large infrastructure commitment or code refactor, the proxy-based alternatives are worth a look.

Spanlens, Helicone, and Braintrust each solve the problem from a different angle. The right choice depends on what you are optimizing for: minimal integration overhead, broad provider support, or deep evaluation workflows.

If you are starting from scratch and want to be up and running in under five minutes, Spanlens is a good place to start.