Building with LLMs is exciting until it isn’t. Once you move past demo mode and start working with real teams, real products, and real prompts, you quickly run into chaos. Prompts disappear into Notion threads. You forget which one worked. There’s no version history. Evaluation? Maybe. Collaboration? Not really.

This is where tools like Snippets AI, Langfuse, and Phoenix come in. We all solve different parts of the problem. But we do it in very different ways. In this article, we’re walking you through those differences based on what actually matters in real workflows.

Where We’re Coming From

We built Snippets AI because we got tired of losing track of good prompts in random Slack threads, pasted docs, and mental bookmarks. We weren’t trying to build another analytics dashboard. We just wanted something simple, reusable, and team-friendly.

Our focus is on prompt workflows. Not logs, not routing, not infrastructure. We organize, share, and reuse prompts in one place so teams can move faster without starting from scratch every time.

If you’re building a prompt-heavy product or managing a prompt library across a team, we’re built for that. If you need deep observability, tracing, and eval pipelines, you might need something more than us – or something alongside us.

Three Tools, Three Different Problems

Let’s be clear: these three tools don’t fully compete. They intersect. Here’s a simplified breakdown of how they line up:

Tool	Best For	Self-Hosting	Prompt Management	Observability	Evaluation Tools
Snippets AI	Teams managing lots of prompts in real products	Cloud-first	Yes, Native UI	No	Basic support
Langfuse	LLM observability with custom instrumentation	Harder	Yes (with setup)	Yes	Yes (some gated)
Phoenix	Open-source LLM tracing and evaluation	Easiest	Yes	Yes	Yes, free + built-in

Prompt Management Isn’t Just Copy-Paste

Snippets AI: Built for Prompt Workflows

This is where we live. Snippets AI is all about making prompt libraries usable, not just stored. We give you:

Keyboard shortcuts (like Ctrl + Space) to insert prompts anywhere
Clean UI for organizing and tagging
Public and private workspaces for team sharing
Voice-based prompt input for fast capture
Simple media

We’re not a backend tracing system. We’re the layer where humans interact with prompts – write them, test them, reuse them, hand them off. You don’t need infra or logging pipelines to get value. You just plug it in and go.

Collaboration: The Overlooked Problem

We’ve seen it again and again – teams don’t fail because they picked the wrong eval metric. They fail because their best prompt is stuck in someone’s Notion file.

That’s why Snippets AI leans into:

Shared libraries with team permissions
Public workspaces for community collaboration
Instant prompt sharing via shortcuts
Reusable templates for repeat tasks (sales outreach, bug triage, etc.)

Langfuse and Phoenix support collaboration in more technical ways, mostly around trace sharing or eval dashboards. But if you’re trying to unblock a designer, a PM, or a marketer who wants to reuse a working prompt – we make that easy.

Langfuse: Power Through Configuration

Langfuse also handles prompt management, but you’ll need to wire it up yourself. It’s built with observability in mind, so prompts are part of a broader trace flow. That gives you flexibility but adds overhead.

It’s better suited to teams that are already doing tracing and want to fold prompt versioning into that workflow. It’s not the fastest way to just get a shared prompt into your teammate’s hands.

Phoenix: Basic but Functional

Phoenix also lets you manage prompts. It includes prompt experiments and a prompt playground, even in the open-source version. If you’re using Phoenix for tracing or evals already, it’s a useful addition.

But it’s still designed more for inspection and testing than for day-to-day, front-of-house prompt workflows. You won’t get quick insert tools or real-time team collaboration like in Snippets AI.

Observability and Tracing

Snippets AI: Not Our Lane

We don’t do tracing. We don’t track latency or route calls. If you need logs, session traces, or runtime stats, you’ll want to pair us with something like Langfuse or Phoenix.

Phoenix: Out-of-the-Box Tracing

Phoenix shines here. It has its own OpenTelemetry-compatible tracing layer called OpenInference, which means you don’t have to cobble together extra libraries. You can self-host Phoenix with a single Docker container and immediately get structured traces for LLM flows.

It’s open-source, batteries-included, and handles everything from tracing to evaluation to dataset experiments.

Langfuse: More Polished, More Dependencies

Langfuse supports tracing too, but it doesn’t include its own instrumentation. You’ll need to hook in your own OpenTelemetry-compatible setup or use other libraries to get trace data flowing.

It’s solid once set up. The UI is polished. You can inspect complex LLM chains, user sessions, and costs. But getting there takes more config and infrastructure.

Evaluation: Built-In vs Bolt-On

Snippets AI: Lightweight Support

We’re not focused on full-scale evaluation. Our job is to help you write and reuse good prompts – the evaluation often happens manually, during testing. That said, we support prompt iteration and sharing across use cases so your team doesn’t need to reinvent what works.

Phoenix: Full Evals with Open Access

Phoenix lets you run both offline and online evaluations, including things like LLM-as-a-judge scoring and dataset-based runs. This is all free under the open-source model.

It’s useful if you’re doing structured comparisons across prompt versions or need to monitor completion quality at scale.

Langfuse: Strong But Partially Gated

Langfuse also offers eval tools, but some (like Prompt Playground and LLM-as-a-Judge) are gated behind their paid tier for self-hosted users. You can calculate scores, track app behavior, and use their analytics dashboard, but you’ll hit limitations unless you pay.

Self-Hosting and Setup: How Fast Can You Start?

Snippets AI: Cloud-Native Simplicity

We’re cloud-first. You sign up, create your workspace, and start using prompts. No hosting. No backend setup. No config files. It’s just there. We do have plans for more advanced team features, but even free users can start collaborating instantly.

Phoenix: One-Container Simplicity

Phoenix is by far the easiest to self-host. It runs in a single Docker container. You don’t need to spin up ClickHouse, Redis, or S3. You can just run it and start testing.

That makes it a great pick for early teams or open-source users who want full control without overhead.

Langfuse: Modular, But More Complex

Langfuse is also open-source, but you’ll need to set up multiple external services to get it working – ClickHouse for analytics, Redis for jobs, and S3-compatible storage for logs.

It’s more flexible, but it’s not drop-in ready. You’ll need dev time and infra.

When to Use Each Tool

Let’s make it simple.

Use Snippets AI If:

You’re managing lots of prompts across a product or team
You want fast sharing, clean organization, and no extra infra
You’re tired of hunting for “that one prompt that worked last week”

Use Langfuse If:

You need structured tracing, cost tracking, or evals
You’re already using ClickHouse or OpenTelemetry
You’re fine doing some setup to get a powerful observability stack

Use Phoenix If:

You want full LLM tracing and evals in one open-source package
You prefer something easy to self-host
You’re experimenting or prototyping and want to stay lean

Final Thoughts

Snippets AI isn’t trying to be everything. We’re not competing to be the deepest observability tool or the most powerful eval platform. We’re focused on one clear problem: helping teams manage and share prompts without chaos.

Langfuse and Phoenix both do a great job in their lanes. If you’re building complex chains or routing across LLMs, you’ll probably use them. In fact, you might use them alongside us.

But if you’re stuck in copy-paste limbo, wasting time searching old threads, or onboarding teammates who have no idea what prompt does what – we’re here for that. No extra tools. No complicated setup. Just clean, usable workflows.

Frequently Asked Questions

What’s the main difference between Snippets AI, Langfuse, and Phoenix?

Each tool covers a different layer of the AI workflow. Snippets AI focuses on prompt management and collaboration – helping teams organize, share, and reuse prompts. Langfuse is built around observability and tracing, offering insights into how LLM calls behave in production. Phoenix, on the other hand, is a fully open-source platform from Arize AI that handles tracing, evaluation, and dataset experiments out of the box. In short: Snippets AI is about workflow, Langfuse is about monitoring, and Phoenix is about experimentation.

Which tool is easier to start with?

If you want the quickest setup, Snippets AI is cloud-based and ready to go in minutes. Phoenix comes next, since it can be self-hosted using a single Docker container. Langfuse takes more setup – it requires ClickHouse, Redis, and S3-compatible storage to get everything running. It’s powerful once configured, but you’ll need more time and technical effort to get started.

Can these tools be used together?

Yes, and many teams do. Snippets AI can serve as the front-end workspace where prompts are written, organized, and shared. Those prompts can then flow into Langfuse or Phoenix for tracing and evaluation. They complement each other well rather than compete directly – one handles the creative side of prompt design, the others handle analytics and observability.

Which platform is better for evaluation and testing?

Phoenix is the strongest for built-in evaluation, with both offline and online evals available for free. Langfuse also includes eval features, but some advanced ones like LLM-as-a-judge or Prompt Playground are gated behind its paid tier. Snippets AI keeps things lighter – evaluation happens through prompt iteration and team testing rather than integrated scoring or metrics.

How do they differ in collaboration features?

Snippets AI leads here, since it’s built around shared libraries, public workspaces, and instant access to reusable prompts. Langfuse and Phoenix focus more on technical collaboration, such as trace sharing or dashboard analysis. If your team includes non-developers or cross-functional users, Snippets AI offers a simpler entry point. If everyone’s engineering-focused, Langfuse or Phoenix might fit better for deep diagnostics.

What kind of teams should use each?

Use Snippets AI if your team spends most of its time creating and refining prompts for AI tools or applications. Langfuse fits engineering-heavy teams that need full control over metrics, costs, and trace logs. Phoenix works best for those who want a self-hosted, open-source observability setup without extra integrations. Each one has its lane – the right choice depends on whether you’re managing people and prompts or monitoring performance and pipelines.

Snippets AI vs Langfuse vs Phoenix: What Actually Matters