If you're shipping LLM features to production, you need observability. Without it, debugging is guesswork, cost runs ahead of revenue, and quality regressions reach users before dashboards. This is the honest list of the best tools to do that work in 2026 — including ours, with what we're good at and where we fall short.
A note on the bias: we ship Respan, so we'd rank ourselves favorably. We've tried hard to be specific about what each tool is good at and what it isn't, including our own weaknesses. If something's wrong, email hello@respan.ai and we'll update it.
Quick comparison
| Tool | Best for | Self-host | Free tier | Pricing tier |
|---|---|---|---|---|
| Respan | Unified platform: traces + evals + gateway + prompts | Enterprise | Yes | $$ |
| Langfuse | Open-source self-hosted | Yes (OSS) | Yes | $$ |
| LangSmith | LangChain ecosystem | Enterprise only | Yes | $$$ |
| Helicone | Proxy-style instrumentation | Yes (OSS) | Yes | $ |
| Braintrust | Eval-first workflow | Enterprise only | Limited | $$$ |
| Datadog LLM | Existing Datadog stacks | No | Trial | $$$$ |
| Arize Phoenix | ML observability + LLMs | Yes (OSS) | Yes | $$ |
| Weights & Biases | Experiment tracking + traces | No | Yes | $$$ |
| Galileo | Eval-heavy enterprise | No | Trial | $$$$ |
What to evaluate
Before the list, the criteria that matter:
- Instrumentation model: SDK, OpenTelemetry, or proxy. OTel-native is the safest long-term bet.
- Tracing depth: full input/output capture, multi-step agent traces, tool-call spans.
- Eval support: rule-based, LLM-as-judge, human review, online + offline.
- Gateway / model routing: paired with the platform or separate?
- Prompt versioning: code-grade lifecycle (diff, A/B, rollback).
- Cost / pricing transparency: predictable, or surprise-bill territory?
- Self-host option: data residency requirements?
- Free tier: generous enough to validate before commitment?
Now the list.
1. Respan
Best for: Teams that want observability + evals + prompt management + LLM gateway in one platform without integrating four separate tools.
The story: Respan is the platform we ship. Our pitch is unification — every other product on this list does observability well, but solving production AI requires observability plus evals plus prompt versioning plus a gateway, and most teams end up running 3-4 tools and stitching them together. Respan is one platform that owns all four primitives so the trace, the eval, the prompt, and the model call are a single object.
Pros:
- Unified platform — observability, evals, prompt management, gateway
- OpenTelemetry-native + SDK + proxy — three instrumentation modes
- 500+ models routable through built-in gateway
- 100% trace capture by default
- Real-time online evals with LLM-as-judge + rule-based
- Prompt versioning with A/B and rollback
Cons:
- Smaller community than Langfuse / LangSmith
- Less battle-tested at the "10-year incumbent" scale of Datadog
- We don't have all the bells and whistles of niche tools (e.g., we're less specialized than Braintrust on offline eval workflows)
Pricing: Free tier with generous limits. Pro and Enterprise tiers for higher volumes. Self-host available on Enterprise.
Best fit: Teams shipping LLM products in production who want one platform instead of four. Especially good if you're using both observability and a gateway.
2. Langfuse
Best for: Teams that want open-source self-hosting and a strong tracing UI.
The story: Langfuse pioneered the open-source LLM observability space. The product is mature, well-engineered, and has a strong community. The free self-hosted version is genuinely usable in production — that's a meaningful differentiator from most other tools on this list.
Pros:
- Open source, MIT licensed, self-hosting is free
- Strong tracing UI with multi-step agent visualization
- Solid evals support including LLM-as-judge
- Active community and contribution velocity
- Good prompt management
Cons:
- No built-in LLM gateway — integration burden if you also need a gateway
- Self-hosting is real work — multiple containers, postgres, ClickHouse
- Eval setup is less opinionated than Braintrust's
Pricing: Self-host is free. Cloud tier offers managed hosting at competitive rates.
Best fit: Teams with strict data residency requirements (self-host) or strong open-source preferences.
3. LangSmith
Best for: Teams deep in the LangChain / LangGraph ecosystem.
The story: LangSmith ships from the LangChain team. The integration with LangChain and LangGraph is tight by design — if your stack is LangChain-heavy, LangSmith is the most natural choice for observability and evals.
Pros:
- Best-in-class integration with LangChain and LangGraph
- Mature evaluator library
- Strong dataset management and offline eval workflows
- Active development tied to the broader LangChain ecosystem
Cons:
- Self-host only on Enterprise tier
- Less general-purpose if you're not on LangChain
- Pricing escalates fast at production volumes
- OpenTelemetry support exists but isn't the primary path
Pricing: Free dev tier. Plus and Enterprise tiers with predictable but premium pricing.
Best fit: Teams who chose LangChain / LangGraph as their framework and want observability from the same team.
4. Helicone
Best for: Teams that want one-line proxy instrumentation without code changes.
The story: Helicone's distinctive feature is proxy-based instrumentation — point your OpenAI client at Helicone's URL and you get tracing + caching + cost analytics for free, without touching your application code. Originally proxy-only, they've added SDK support too.
Pros:
- Proxy mode is genuinely the easiest install on this list
- Strong cost analytics — Helicone's
/llm-cost/*programmatic pages drive serious traffic - Open source self-host available
- Good free tier
- Caching out of the box
Cons:
- Less depth on agent tracing (proxy can't see agent state)
- Eval support is basic compared to Braintrust / Langfuse
- Less polished UI
Pricing: Generous free tier. Pro and Enterprise tiers reasonable.
Best fit: Teams that want fast time-to-value and aren't running deep multi-step agents.
5. Braintrust
Best for: Teams whose primary need is rigorous offline eval pipelines.
The story: Braintrust is eval-first. Tracing exists but secondary to the eval workflow — datasets, scoring functions, comparison reports, regression testing. If you're a team that takes eval discipline seriously, Braintrust is built for you.
Pros:
- Deepest scoring functions library
- Dataset versioning and management is strong
- A/B testing and experiment comparison built in
- Good offline + online eval support
Cons:
- Tracing is solid but less polished than dedicated observability tools
- No LLM gateway
- Pricing tier escalates fast at scale
- Self-host on Enterprise only
Pricing: Free dev tier with limits. Pro starts at sane prices but Enterprise pricing is opaque.
Best fit: Teams where the bottleneck is eval workflow quality, not basic tracing.
6. Datadog LLM Observability
Best for: Teams already standardized on Datadog for the rest of their stack.
The story: Datadog added an LLM observability module to their broader APM platform. If your engineering org already runs Datadog for metrics, logs, and traces, adding LLM observability there is the path of least friction.
Pros:
- Native integration with Datadog's broader observability stack
- Strong infrastructure and APM correlations
- Mature alerting, dashboards, and team workflows
- Enterprise-grade support and SLAs
Cons:
- Bolt-on, not LLM-native — less depth on prompt versioning, evals, gateway
- Datadog pricing in general is steep; LLM module adds to it
- No self-host
- Trial only — no real free tier
- Most teams find dedicated LLM observability tools cover the LLM-specific work better
Pricing: Datadog standard pricing — usage-based, generally premium.
Best fit: Teams already running Datadog who want to consolidate vs. teams choosing a fresh tool.
7. Arize Phoenix
Best for: ML teams transitioning from classical ML observability to LLM observability.
The story: Arize started in classical ML observability (drift detection, feature monitoring) and expanded into LLMs. Phoenix is their open-source LLM observability tool. Good for teams with both classical ML and LLM workloads in the same org.
Pros:
- Open source, self-host friendly
- Bridges classical ML monitoring + LLM observability
- Strong drift detection capabilities
- Active OSS community
Cons:
- LLM-specific features (prompt versioning, gateway) are less mature than dedicated LLM tools
- Setup more complex than turnkey alternatives
- Less polished UI
Pricing: Phoenix open source is free. Arize cloud is paid.
Best fit: Organizations with mixed classical ML + LLM portfolios.
8. Weights and Biases
Best for: Teams already using W&B for experiment tracking who want LLM tracing alongside.
The story: W&B is the canonical experiment tracking tool for ML training. They've added LLM observability features (Weave) so teams running both training and inference can track everything in one place.
Pros:
- Strong experiment tracking for fine-tuning workflows
- Good for teams that train their own models
- Mature dataset and artifact management
Cons:
- LLM-specific observability is newer / less mature
- No LLM gateway
- Premium pricing for what overlaps with dedicated LLM observability tools
Pricing: Free tier exists; team plans escalate quickly.
Best fit: Teams that train custom models and want training + inference observability in one place.
9. Galileo
Best for: Enterprise teams with heavy eval and quality requirements.
The story: Galileo is the most enterprise-focused tool on this list. Heavy investment in eval automation, quality scoring, hallucination detection, and compliance workflows. Pricing reflects this — it's the tool you pick when budget isn't the constraint and quality is.
Pros:
- Strong hallucination and quality detection
- Mature compliance and audit workflows
- Eval automation that scales
Cons:
- Premium pricing; not for small teams
- No real free tier (trial only)
- Less developer-friendly than tools built for individual engineers
Pricing: Enterprise — contact sales territory.
Best fit: Enterprise teams with regulated workloads (legal, healthcare, finance) where eval rigor and compliance are the dominant constraints.
How to choose
Quick decision framework:
- Want one platform across observability + evals + prompts + gateway? → Respan
- Open-source, self-host, strong community? → Langfuse
- Already on LangChain / LangGraph? → LangSmith
- Easiest install, proxy-based? → Helicone
- Eval workflow is the bottleneck? → Braintrust
- Already on Datadog and want to consolidate? → Datadog LLM
- Have both classical ML + LLM workloads? → Arize Phoenix
- Train custom models + want unified observability? → W&B
- Enterprise with regulated workloads? → Galileo
Most teams will benefit from 1-3 above. The "right" answer depends on your specific stack, scale, and constraints — run a free-tier pilot of two or three before committing.
FAQ
Which is the cheapest LLM observability tool? Open-source self-hosted Langfuse is free if you're willing to run the infrastructure. Helicone has the most generous free cloud tier. Respan's free tier is competitive too.
Which has the best free tier? Helicone, Langfuse, and Respan all offer functional free tiers. Langfuse goes further by being free-and-self-hostable in addition to a free cloud option.
Which integrates best with OpenTelemetry? Respan, Langfuse, and Arize Phoenix are all strong on OTel-native instrumentation. LangSmith and Datadog LLM both support OTel but it's not the primary path.
Which has the best evals? Braintrust is the most rigorous offline eval workflow. Respan offers strong online + offline evals integrated with traces and prompts. Langfuse and LangSmith are competitive.
Which has a built-in LLM gateway? Respan and Helicone are the two on this list with a real gateway integrated into the observability product. Most others (Langfuse, LangSmith, Braintrust, Datadog) don't bundle a gateway.
Should I just use Datadog if I already have it? Maybe — if your LLM workloads are simple and you value consolidation. For complex agentic workloads, dedicated LLM observability tools cover the LLM-specific work better. Common pattern: dedicated LLM observability tool forwarding OTel to Datadog as a secondary destination.
Can I switch later? Yes — if you instrument with OpenTelemetry GenAI conventions, your traces are portable. Lock-in risk is highest with proprietary SDKs and lowest with OTel-native instrumentation.