LLM Observability Tools Compared: The 2026 Landscape

This post is the map we wish we'd had when we started building Grepture. We'll cover the eight tools most teams evaluate in 2026, how they actually differ, and when to pick each. We build a tool in this space, so we'll flag that clearly — but the bulk of this post is about the other seven, because you need that context first.

What you're actually evaluating

Before the tool-by-tool walkthrough, here are the five dimensions that matter. Most comparison tables online gloss over these.

Architecture. Is it a proxy (requests flow through it), an SDK (you instrument your code), or both? Proxies give you coverage without code changes but add a network hop. SDKs are zero-latency but require integration in every service.

Data captured by default. Some tools log full prompts and completions. Others capture only metadata (tokens, latency, errors). This matters for privacy — if your prompts contain PII, a default-log-everything tool creates a compliance liability you probably didn't plan for. We wrote a separate post on that specific problem.

Evals vs. monitoring orientation. Some platforms are built around experiments and LLM-as-judge evals; observability is secondary. Others are production-monitoring first with evals bolted on. Both are legitimate — pick the one that matches what you actually do day to day.

Cost tracking granularity. Token counts are table stakes. The real question is: can you attribute spend to a team, a feature, an environment, or a user? And can you set budget alerts before the CFO notices?

Deployment model. Open source self-host, cloud, or both? This is usually a compliance question, not a cost question. EU-regulated teams often need self-host; US startups rarely do.

The eight tools

1. Langfuse

Langfuse is the most widely deployed open-source LLM observability platform. It's MIT-licensed, self-hostable, and has a generous cloud free tier.

Architecture: SDK-based tracing. You call langfuse.trace() in your code or use their OpenAI/Anthropic wrappers. It's not a proxy.

Strengths: Open source with an active community. Rich tracing model that handles nested spans, generations, and scores well. Built-in prompt management and evals. Self-host is genuinely usable (though it needs PostgreSQL, ClickHouse, Redis, and blob storage).

Weaknesses: Instrumentation burden — every service that makes LLM calls needs the SDK. No native multi-provider gateway features like routing or fallback. Prompt management is decent but not as deep as dedicated tools.

Pick Langfuse if: You want open-source, your team is comfortable instrumenting code, and you don't need a proxy layer. We wrote a detailed Grepture vs. Langfuse comparison if you want the full breakdown.

2. Helicone

Helicone is the clearest example of "observability as a proxy." You change your OpenAI base URL to Helicone's endpoint and every request gets logged.

Architecture: HTTP proxy, primarily. They also offer an async logging mode.

Strengths: Zero-code integration — change a base URL, done. Strong cost tracking and user-level attribution. Caching built in, which is genuinely useful for reducing spend on repeated prompts. Open source.

Weaknesses: Proxy adds a network hop and a failure mode. Less mature evals story than Langfuse or Braintrust. Prompt management exists but is basic.

Pick Helicone if: You want the fastest possible integration and you're comfortable with a proxy in the request path. Caching is a genuine bonus if your workload has repeated prompts.

3. Arize (Phoenix + AX)

Arize comes from traditional ML observability and extended into LLMs. Phoenix is the open-source tracing library; Arize AX is the paid enterprise platform.

Architecture: OpenTelemetry-based SDK. Phoenix is a local/self-hosted tool; AX is the hosted enterprise product.

Strengths: Deep eval and drift-detection heritage from their ML past. Best-in-class for teams that also monitor traditional ML models alongside LLMs. OTel compatibility means it plays nicely with existing observability stacks.

Weaknesses: Enterprise-oriented pricing and sales motion. Overkill for most startups. LLM-specific features are newer than their ML ones.

Pick Arize if: You're a larger org already running ML in production and want LLM observability in the same pane of glass.

4. Braintrust

Braintrust is evals-first. Observability is there, but the product is organized around experiments, scoring, and iterating on prompts.

Architecture: SDK with tracing, plus a strong web UI for running evals and comparing experiment runs.

Strengths: Best eval workflow on this list, by a wide margin. Playground, dataset management, and LLM-as-judge scoring are tightly integrated. Fast-moving product.

Weaknesses: If you just want to monitor production traffic, it's more product than you need. Closed source, cloud only.

Pick Braintrust if: Your team iterates heavily on prompts and evals, and you want the observability and eval stories to be one tool.

5. Lunary

Lunary (formerly LLMonitor) is a lightweight open-source platform aimed at indie devs and small teams.

Architecture: SDK-based tracing. Also offers a proxy mode.

Strengths: Simple setup, clean UI, open source. Decent cost tracking and user analytics. Good if you want to avoid enterprise complexity.

Weaknesses: Smaller team and ecosystem than Langfuse. Evals are basic. Less production-hardened at scale.

Pick Lunary if: You're a small team, want open source, and Langfuse feels heavy.

6. Humanloop

Humanloop leans into prompt management and evaluation more than pure observability.

Architecture: SDK-based, with strong prompt versioning and deployment primitives.

Strengths: The prompt-management story is excellent — versioning, deployment, non-engineer collaboration. Eval workflows are mature.

Weaknesses: Observability is secondary. Closed source, enterprise pricing.

Pick Humanloop if: Prompt management and non-engineer collaboration are your primary pain points. See also our post on prompt management and version control.

7. LangSmith

LangSmith is LangChain's official observability and eval platform. If you're using LangChain or LangGraph, it's the path of least resistance.

Architecture: SDK-based tracing, tightly integrated with LangChain's framework primitives.

Strengths: Zero-friction if you're already in the LangChain ecosystem. Deep support for agent traces, tool calls, and chain runs. Decent evals.

Weaknesses: Best-in-class only if you're a LangChain shop. Feels bolted-on if you're using raw SDKs or other frameworks. Closed source, and the pricing model has shifted a few times.

Pick LangSmith if: You're committed to LangChain/LangGraph and want the most integrated experience.

8. Grepture

Disclosure: this is us. Grepture started as a content-aware AI gateway with PII redaction and expanded into full observability. We'll be specific about fit so you don't waste time on us if we're the wrong match.

Architecture: Both. Proxy for full-coverage observability without code changes, plus a zero-latency trace mode where the SDK logs async and requests go direct to the provider. We wrote the reasoning behind the dual architecture in Trace Mode — Full Observability Without the Proxy Hop.

Strengths: Observability + AI gateway + PII redaction in one. Multi-provider routing and fallback. Prompt management with versioning. Evals on real production traffic. EU-hosted option with GDPR-compliant defaults.

Weaknesses: Smaller eval workflow than Braintrust (we handle production evals well, not experiment-heavy iteration). Younger product than Langfuse or Helicone. Not the right choice if all you need is tracing and you have zero interest in gateway features.

Pick Grepture if: You want observability, PII handling, cost tracking, and multi-provider routing from one tool — especially if you're EU-based or have compliance requirements.

Side-by-side comparison

How to decide

The category is fragmented because teams have genuinely different needs. A useful way to narrow down:

Start with your integration constraint. If you can't touch every service, you need a proxy — that narrows you to Helicone, Lunary (proxy mode), or Grepture. If you can instrument, everything else opens up.

Then filter on evals vs. monitoring. If your team iterates on prompts daily and runs structured experiments, Braintrust or Humanloop pull ahead. If you're mostly watching production, Langfuse, Helicone, or Grepture fit better.

Then consider compliance. If you need self-host or EU data residency, Langfuse, Phoenix (Arize), Lunary, or Grepture's EU deployment are the shortlist. Others are cloud-only or US-hosted by default.

Finally, think about scope creep. "Observability only" tools tend to expand into prompt management, evals, and routing over time. If you know you'll need those, consider a tool that already has them rather than stitching four products together.

How Grepture helps

If you made it this far and observability is genuinely the only thing you need, a pure-play tracing tool is probably the right pick. Langfuse is the default if you want open source; Braintrust if you want the best eval workflow.

Grepture makes sense when you need more than tracing: an AI gateway for multi-provider routing and fallback, content-aware PII redaction before requests leave your infrastructure, or unified cost tracking across providers. The trace-only mode means you can get full observability with zero proxy latency, and flip to full gateway mode when you need routing or redaction.

For EU teams, the GDPR-compliant defaults and EU hosting are typically the deciding factor — most tools on this list are US-hosted and default to logging full prompt/completion pairs, which creates a DPIA headache.