Should You Build or Buy LLM Observability?

Lina Lam· April 10, 2025

• Buying: Zero maintenance and often a more polished product that's up-to-date with industry standards. However, you have less customization and subscription costs.
• Building: Totally customizable to your needs. But building it from scratch and maintaining it can take significant effort, bandwidth, and resources.

We hear the same story with nearly every team shipping LLM apps to production:

• "We lost $800 on LLM retries last week, and didn't even know it until a user complained."
• "We shipped a broken tool call chain to prod. It took us 3 hours to trace the root cause."
• "We changed one prompt, and our cost per request doubled. No one noticed for a week."

As more teams ship LLM apps, observability has gone from a nice-to-have to a baseline requirement. The real question becomes: Should you build LLM observability yourself, or just buy it?

After speaking with over 20 teams in the last 6 months, we've learned what it actually takes to build from scratch - and when buying helps you move faster without burning cycles on infra. Here's what we've learned from the conversations and building LLM apps ourselves.

TL;DR: Build vs. Buy

What you need to build LLM observability from scratch

Building your own LLM observability tooling can be a straightforward process.

At a basic level, you'll want to log requests, capture outputs, and display them in a nice dashboard to serve your needs. As your product grows, features like retries, cost tracking, rate limits, caching, and evals start becoming more relevant.

Here's a peek into what that journey looks like:

1. You start simple. Start logging prompts, responses, cost and latency.
2. You might add retries or rate limits. As you build out more features in your app, it's probably far from a single LLM call. You need to track chains, tools, and multi-step agents. Now you need trace trees to understand the workflow.
3. You see that prompts change weekly. So you start to build versioning.
4. You should test which prompt is better. So you manually A/B test on a few examples, realize that doesn't scale, and build a test harness.
5. Then you can add evals, scoring, dashboards, feedback, redaction... and now you've got a small observability platform on your hands.

Going the build route

One company that went the build route is incident.io. They built internal LLM tooling to support drafting incident summaries and debugging workflows. Their rationale was they wanted tight feedback loops, transparency, and fast iteration - and weren't satisfied with off-the-shelf platforms.

They admit it was a major investment. Their advice was try before you buy, but if you're thinking about building, be clear what your north star is. Otherwise, you risk spending weeks on infra instead of shipping value.

In short: Building your own observability stack is possible — and for some teams, even necessary. But it comes with a list of hidden costs. Know your goals, your bandwidth, and your tolerance for infrastructure overhead before you dive in.

Building a LLM Debugging System

Let's start with a scenario 🔍

Your product manager pings you, saying that the AI assistant gave a completely wrong recommendation to a customer again. You wonder, what happened?

You start by digging into the log: the user asked a question about the nearest gym. The assistant responded with options from another city.

By building a tree view, you can visualize the complete flow with timestamps and durations. You can see every model response in the order it was called, along with tool and vector database calls.

Here's a screenshot of what the visualized tree for an LLM workflow would look like in Helicone, an LLM observability platform:

The ability to filter this tree is critical. The tree view helps you pinpoint the exact LLM request that contains "Vancouver" in the response body, or requests that caused a 429 error, or that took longer than 10 seconds to complete:

In our scenario, the prompt looks fine, and the tool call also returns correct data. So what broke?

You dig deeper. Turns out, the prompt was silently updated in production two days ago. The tool call triggered a fallback function due to a schema mismatch. The model retried twice, each time pulling from stale memory instead of live RAG context.

Without proper tracing, finding this would require manually piecing together logs from multiple systems—potentially hours of work. With a visual trace tree, you can step through each component and identify the exact failure point in minutes instead of hours.

When does building make sense?

For some teams, building is the right call. If you:

• Have unique security/compliance requirements no vendor satisfies
• Are already running a robust monitoring system you can easily extend
• Have a dedicated infrastructure team with bandwidth to spare
• Work in a regulated industry with specific audit requirements

Then yes, building your own solution might be the best path. The companies that succeed with this approach typically dedicate at least one engineer, if not more, to maintaining their observability stack long-term.

Building a system to test prompts and models

Another solution teams building AI-powered features often need is an easy way to test different models and prompts.

Let's say you're trying to evaluate whether Claude 3.5 Sonnet or GPT-4o is better for your use case. You run a few experiments with real user queries.

Below is a screenshot of the spreadsheet-like interface in Helicone. In this case, we're testing different prompts with the same model.

Spreadsheet-like interfaces work really well for testing, especially for comparing prompt and model variations. Here's an example of comparing the same prompt input with Claude 3.5 Sonnet and GPT-4o:

You want to see the full prompts, variable values, and real production inputs (e.g., user queries) to make sure your prompt works in the real world, and then see the outputs side-by-side.

If you're curious, here's how testing works in Helicone.

Building the "very basics" of LLM observability

When building your own observability tool, there are some unavoidable aspects you must always take into consideration.

1. Request/response logging in real-time: This helps you stay on top of how your LLM app is performing. Set up alerts or jump to fix errors as they happen.
2. Data segmentation & aggregation: Add metadata to your requests to make it easier to filter and analyze.
3. Cost, usage and latency tracking: See how your LLM costs change over time. What's the average cost per session? Does the new prompt version cost more?

Get granular with user-level metrics

As your app grows, it's helpful to zoom in on specific users - especially your power users or edge cases. Getting deep into user-level metrics helps you answer questions like:

• How often is this user hitting the API?
• Are they generating a disproportionate amount of cost or tokens?
• Are they using the product as expected, or maybe pushing the limits?

What you get when you buy

Everything you've seen in this post — tracing, retries, prompt testing, evals, model comparison — is built-in when you buy an observability tool like Helicone out of the box.

Also included:

• One-line proxy: Minimal latency, no need to build your own logging system
• Dashboards: Pre-built metrics for cost, latency, and usage
• Sessions: Trace step-by-step calls, nested tool/vector DB calls
• Prompt management: Version tracking, playgrounds, and diff comparisons
• Experiments: Test prompt variations at scale in a spreadsheet-like interface

Key takeaways

Phew! That was a lot. Whether you're a solo builder or part of a fast-moving team, LLM observability will become unavoidable.

It's not really about whether you need it, more about how much time you want to spend building vs. shipping.

Here are 5 main takeaways:

1. If you aren't sure what you need, try an existing observability tool like Helicone first. Helicone's free tier comes with 10,000 requests/mo, and you can get started right away.
2. Building gives you control, but eats up time. It's not just logging, you'll need tracing, testing infrastructure, eval pipelines, retries, cost tracking, and more.
3. Buying lets you move faster. You get debugging tools, prompt/version tracking, and cost visibility out of the box — so you can focus on your app, not infrastructure.
4. Most teams underestimate what "just logging" turns into. It starts small but grows into a full observability stack before you know it.
5. Build if observability is your product. Buy if you want to ship faster and have observability that scales with your LLM stack.

Is Helicone right for you?

We built Helicone so you don't have to build your own observability tool from scratch and can focus on shipping amazing products. It's open-source, production-ready, and designed to help you monitor, debug, and improve your app faster.

Helicone is a great fit if:

• You prefer "truly open-source tools" that integrate easily into your existing stack.
• You want a solution that works with OpenTelemetry and other open standards
• You want to compare models, track cost, debug prompts without building everything from scratch
• You need to test LLMs using real production data
• You have serious usage and traffic to monitor.

You might also be interested in

• Best LangSmith Alternatives for LLM Observability
• 4 Essential Helicone Features to Optimize Your AI App's Performance
• The Ultimate Guide to Effective Prompt Management