Arize Phoenix Alternatives 2026: Top 7 for Agent Observability

Arize Phoenix is a solid open-source LLM tracing and evaluation tool. It was built around the notebook: prompt experimentation, LLM-as-judge evals, and OpenInference, the most widely adopted set of OpenTelemetry semantic conventions for LLM spans. The industry has moved past the notebook. Teams ship agents now, and an agent is a different shape of problem.

An agent can run for hours, call fifteen tools, spawn sub-agents, and fail in subtle, unexpected ways. You open Phoenix and get a span tree, then spend hours sifting through prompts and tool calls just to understand what the agent was doing. Understanding agents simply requires a new type of tooling.

This article ranks the top Arize Phoenix alternatives for 2026, ordered by how well they handle agents rather than prompt-centric experimentation. TLDR: the best Arize Phoenix alternative in 2026 is Laminar, because Laminar was built for AI agents from the ground up.

Best Arize Phoenix alternatives in 2026#

Why Laminar is the best Arize Phoenix alternative#

Laminar is an open-source, OpenTelemetry-native observability platform purpose-built for AI agents. Where Phoenix organizes around spans and notebook evals, Laminar organizes around the agent: how its traces are shaped and stored, how you read them, and how you understand agent failure modes and fix them. Because Laminar is OTel-native, your existing OpenInference instrumentation flows in unchanged.

Open-source under Apache 2.0, easy to self-host#

Laminar is Apache 2.0 and can be easily self-hosted with Docker Compose with a single command or with a production-ready Helm chart for high scale applications. Every feature ships on the OSS image, including Signals, the SQL editor, and the debugger.

The license is the material difference with Phoenix. Phoenix uses the Elastic License 2.0, which is not OSI-approved and forbids offering it as a hosted or managed service. Laminar's Apache 2.0 has no such clause: you can host it, embed it in a commercial product, fork it, and ship a managed version. For teams whose legal review treats ELv2 as non-OSS, this alone decides it.

20x trace compression and the best data pricing on the market#

Agents re-send the whole conversation every turn, so Laminar stores each unique message once and reconstructs traces on read. This results in 20x less storage on average, and up to 50x on long runs. We go into the details of trace compression in this blog post.

That's why our data pricing is the lowest on the market, twice over. You pay per GB of unique data, not for the copies your agent re-sends on every turn. And the per-GB rate itself is lower, because the storage layer costs us less to run. Phoenix's commercial path (Arize AX) bills on spans per month, and an agent emits spans by the thousands per run, so span-based pricing climbs fast on exactly the workload this article is about.

Start on a generous free tier and scale without a surprise bill. See the pricing page.

Signals: track agent failures in plain language#

A Signal is a prompt that runs against every trace. "Agent used tools inefficiently." "User asked for the same thing twice." "Tool failed and the agent made up the data instead." Laminar's agent analyzes every trace, and when it finds what you described it produces a structured event: queryable via SQL, clustered with similar events from other runs, and backfilled across your history. You can also connect Slack and receive a realtime alert when a certain event is detected.

A Signal reads the whole trace, every LLM turn and tool call from first span to last, because most agent failures only make sense in the context of the entire agent run. Phoenix has evals that score a trace after the fact; Signals are a different primitive: you name a failure mode once in English, and Laminar finds every run that hit it, past and future, without re-tagging anything.

Then we go one step further. Laminar runs a proprietary online clustering algorithm over all extracted signal events, so instead of scrolling ten thousand individual events you see the ten behaviors your agent actually has. Clusters are a high-level map of your agent in production: what fails, how often, and which failures are the same failure.

Evals: a code-first, barebones SDK#

Laminar's evaluations follow a code-first philosophy: a small, unopinionated SDK that makes versatile evals easy to write, because agent behavior is too varied for a rigid eval form. You define datapoints, an executor function that produces an output, and one or more evaluator functions that score it. Laminar runs them in parallel, traces every call, and stores the scores so you can compare runs over time. Phoenix Evals is a strong notebook-resident library; Laminar's eval SDK scores the agent's real output with plain functions, in the same platform that holds the traces.

Your coding agent can use Laminar to improve your agent#

Building an agent is a loop: run it, read what it did, change something, run it again. Laminar is built so a coding agent can drive that loop, because all platform data, traces, spans, signal events, and eval scores, is exposed through raw SQL. Your coding agent queries it from the CLI (lmnr-cli sql query) or the MCP server, and you get the same SQL in the UI and API.

The loop it runs: query the worst failures, read the traces, fix the code, rerun, measure against the same eval. The debugger captures every trace, eval, CLI command, and note in one session, and replays cached responses on reruns so each iteration is cheap.

Where Laminar might not be the right pick#

• Your entire workflow is evaluating prompts against datasets in a notebook. Phoenix Evals or Braintrust still fit that better.
• You have no tool use, no sub-agents, and no multi-step runs. A single-call logging tool is enough.

2. Langfuse#

License: MIT. Deployment: Cloud, self-host. Repo: github.com/langfuse/langfuse.

If you like Phoenix's data model but need a permissive license, Langfuse is the closest swap. Prompt versioning, typed observations (generations, spans, events), an eval harness that plugs into CI, and a self-host that includes every feature on the free image.

Strengths:

• MIT license. No Elastic License 2.0 restrictions.
• Strong prompt management: versioning, tagging, release channels.
• Mature eval harness with LLM-as-judge, custom scorers, and human feedback.

Weaknesses:

• Observation-first data model is still closer to Phoenix than to an agent-first product. Agent runs render as a list of observations.
• Unit-based pricing on Cloud (traces + observations + scores) adds up when an agent run is 40 to 75 spans.
• No SQL over traces in product, no natural-language outcome tracking, no cached-rerun debugger.

Pricing: Free tier includes 50k observations with 30-day retention. Core $29/month. Pro $199/month. Self-host is free with all features. See our Langfuse alternatives guide for the deeper comparison.

3. LangSmith#

License: Closed source. Deployment: Cloud, hybrid, self-hosted (Enterprise only).

If your stack is LangChain or LangGraph, LangSmith fits like a glove. One environment variable, and runs are traced. LangGraph Studio is the best agent IDE available for that stack: visualize the graph, set breakpoints, modify state mid-run, resume from a checkpoint.

Strengths:

• LangGraph Studio (a real agent IDE, not just a viewer).
• Managed deployment with checkpointing and memory.
• OpenTelemetry support added in 2026.

Weaknesses:

• Closed source. Self-hosting is Enterprise-only.
• Seat-based pricing ($39/seat/month on Plus) gets expensive with larger teams.
• Tightest fit is still LangChain. Teams on other frameworks get less value.

Pricing: Developer free with 5k base traces/month. Plus $39/seat/month plus $0.50 per 1k base traces. Extended-retention traces cost $2.50 per 1k. Full comparison: LangSmith alternatives 2026.

4. Braintrust#

License: Closed source. Deployment: Cloud, on-prem for Enterprise.

Braintrust is eval-first. Tracing exists to feed the eval loop, not to stand alone.

Strengths:

• Mature scorers, comparisons, regression detection.
• Clean prompt playground tied to eval sets.
• Strong if your bottleneck is "did this change break behavior X."

Weaknesses:

• Not a debugger. You will not be faster at finding what broke in production.
• Lighter agent-specific UX than Laminar or LangSmith.
• Closed source.

Pricing: Free tier available. Pro scales with usage. Enterprise custom. Full comparison: Braintrust alternatives 2026.

5. Weights & Biases Weave#

License: Closed source. Deployment: Cloud, on-prem for Enterprise.

Weave plugs tracing into the existing W&B console. If your ML team already lives there, it is the path of least friction.

Strengths:

• Native W&B integration.
• Strong eval framework with scorers and comparisons.
• Good for teams evaluating models and agents on the same platform.

Weaknesses:

• Trace UX borrowed from ML experiment tracking. Not agent-first.
• Weak on realtime trace viewing during long runs.
• Closed source.

Pricing: Free tier with limited storage. Paid plans scale with volume and seats.

6. Helicone#

License: Apache 2.0. Deployment: Cloud, self-host.

Helicone is a proxy that sits in front of the LLM provider and logs every request. Simplest integration of any tool in this list: change a base URL.

Strengths:

• Zero-code proxy integration.
• Caching, rate-limit handling, and retries built into the proxy.
• Cheap to get started.

Weaknesses:

• Request/response focused, not span-based. Multi-step agents are stitched together after the fact.
• No transcript view, no Signals, no debugger, no SQL over traces.
• Proxy model adds a hop to every LLM call.

Pricing: Free tier. Paid plans scale with request volume.

7. Traceloop / OpenLLMetry#

License: Apache 2.0 (OpenLLMetry SDK). Deployment: Cloud backend, vendor-neutral SDK.

Traceloop's value is the OpenLLMetry SDK: vendor-neutral OpenTelemetry instrumentation for LLMs. Traceloop's own backend is one place the traces can go. Most backends in this list (Laminar, Langfuse, Phoenix itself, LangSmith) can also ingest OpenLLMetry spans, which makes OpenLLMetry the safest instrumentation choice for teams that want portability.

Strengths:

• OTel-native. Works with any compatible backend.
• Active open-source community.

Weaknesses:

• The backend UX is less agent-specific than Laminar or LangSmith.
• Primary value is the SDK, not the product.

Head-to-head: where each Arize Phoenix alternative wins#

Pricing comparison for 2026#

Note the pricing shape: Phoenix OSS is free, but the graduation path (AX) bills on spans per month, and an agent run emits 40 to 75 spans. Laminar's data-volume pricing tracks compressed payload size instead, and 20x trace compression means each gigabyte holds far more agent traffic, so it stays predictable as traces grow.

Open-source scorecard#

Matters if you self-host, run in air-gapped environments, or want to own the trace data without a license review.

The Elastic License 2.0 row is the important one for Phoenix alternatives specifically. ELv2 forbids offering Phoenix as a hosted or managed service to third parties. For most internal users this is fine. For platform companies, consultancies, and anyone whose legal team uses the OSI definition, Phoenix does not clear review and Apache 2.0 or MIT alternatives do.

How to pick an Arize Phoenix alternative in 5 minutes#

Answer these in order. Stop at the first yes.

1. Are you building AI agents and want low-cost optimized trace storage, Signals, a coding-agent debugger, and SQL over all your data? → Laminar.
2. Do you need a permissive OSS license (Apache or MIT) with prompt management and evals? → Langfuse.
3. Are you committed to LangChain or LangGraph and want an agent IDE? → LangSmith.
4. Is your only pain regression testing, not debugging? → Braintrust.
5. Does your ML team live in W&B? → Weave.
6. Do you just need cheap request/response logs for raw LLM calls? → Helicone.
7. Do you want vendor-neutral instrumentation and will decide the backend later? → OpenLLMetry plus any of the above.

Migrating from Arize Phoenix to Laminar#

If you are on Phoenix and the friction above applies, the migration is straightforward because both products speak OpenTelemetry.

1. Keep the instrumentation. If you are already using OpenInference, point the OTLP exporter at Laminar's endpoint. Spans flow in unchanged. If you prefer Laminar's native SDK, the Python and TypeScript packages follow the same auto-instrumentation pattern. Start with the Laminar quickstart.
2. Map the data model. Phoenix spans are OTel spans. Laminar treats them as such. Projects map to Laminar projects. Sessions map to trace sessions.
3. Port the evals. Phoenix Evals stay in Phoenix during migration. For production outcome tracking, recreate the important eval templates as Signals so they backfill across history and fire on new traces going forward.
4. Run side-by-side during the transition. Send to both backends until you trust the new pipeline. OTel supports multiple exporters.

Why we recommend Laminar#

We built Laminar because the industry moved from single LLM calls and notebooks to agents, and the prompt-first tools did not move with it. Compression came from understanding that agent traces repeat themselves. Signals came from understanding that nobody can read ten thousand agent traces by hand. The debugger came from understanding that a coding agent should drive the fix-and-rerun loop. Raw SQL came from understanding that complex traces raise questions only a query can answer. Each one is a thing you get because the platform was built for agents.

If you are looking at Phoenix alternatives because your agents outgrew the span-tree view, or because ELv2 does not clear your legal review, that is the reason to try Laminar first. Start with the free tier: 1GB of traces, 7-day retention. Instrument one agent. If you do not see the difference in the first hour, come back and tell us why.

Try Laminar free · Read the docs · Star on GitHub

FAQ: Arize Phoenix alternatives in 2026#

What is the best Arize Phoenix alternative in 2026?#

Laminar is the best Phoenix alternative in 2026. It is Apache 2.0 licensed, OpenTelemetry-native, OpenInference-compatible, and built for AI agents, with 20x trace compression, the lowest data-volume pricing on the market, Signals for plain-language outcome tracking, a coding-agent debugger, and raw SQL over all platform data. Langfuse is the best alternative if you want an MIT-licensed OSS product with strong prompt management; LangSmith is the best alternative for LangGraph-committed teams; Braintrust is the best alternative for eval-first regression workflows.

What is the best open-source Arize Phoenix alternative?#

Laminar is the best open-source Phoenix alternative. It is Apache 2.0 (OSI-approved, unlike Phoenix's Elastic License 2.0) and ships a Helm chart for one-command self-host with every feature on the OSS image, including Signals, the SQL editor, and the debugger. Langfuse (MIT) and Helicone (Apache 2.0) are also OSI-approved options.

Is Arize Phoenix actually open source?#

Phoenix uses the Elastic License 2.0, which is not OSI-approved open source. ELv2 permits source availability, modification, and internal commercial use, but prohibits offering Phoenix "as a hosted or managed service" to third parties. Laminar (Apache 2.0), Langfuse (MIT), and Helicone (Apache 2.0) are all OSI-approved open source with no such restriction.

Which Phoenix alternative is cheapest for agents?#

Laminar is the cheapest Phoenix alternative for agents. It prices by data volume rather than per span or seat, and compresses agent traces by 20x on average, so each gigabyte of quota holds far more agent traffic. Phoenix's commercial path (Arize AX) bills on spans per month, and an agent run emits 40 to 75 spans, so span-based pricing climbs fast on agent workloads.

How does Laminar make agent traces cheaper to store?#

Agents re-send the full conversation on every turn, so a trace repeats most of its content. Laminar hashes each message, stores every unique message once per trace, and reconstructs the full trace byte-for-byte at query time. This yields 20x storage reduction on average and up to 50x on the longest agent runs, which is why Laminar's data-volume pricing is the lowest for agent workloads.

Can I query my agent traces with SQL in Laminar?#

Yes. Laminar exposes all platform data, traces, spans, signal events, and evaluations, through SQL. You can run queries from the SQL editor in the UI, the lmnr-cli sql query command, the MCP server (so a coding agent can query your data directly), or the SQL API. Phoenix has no in-product SQL over traces; analysis runs in a notebook or via export.

Can I send OpenInference traces to a Phoenix alternative?#

Yes. OpenInference is a set of OpenTelemetry semantic conventions. Any OTel-native backend can ingest OpenInference spans. Laminar and Langfuse both accept them via the standard OTLP exporter, so you can keep your existing Phoenix instrumentation and swap the backend without re-instrumenting.

What is the difference between Arize Phoenix and Laminar?#

Phoenix is optimized for prompt-centric experimentation and LLM-as-judge evals in a notebook-friendly self-host. Laminar is built for AI agents: 20x trace compression and data-volume pricing, Signals for outcome tracking across history, a coding-agent debugger with cached reruns, raw SQL over all platform data, and a code-first eval SDK. Licenses differ: Phoenix is ELv2 (not OSI-approved), Laminar is Apache 2.0.

What is the difference between Arize Phoenix and Arize AX?#

Phoenix is the free, self-hosted OSS side of Arize. Arize AX is the commercial SaaS, with managed infrastructure, alerts, online evaluations, agent copilots, and enterprise compliance. AX Free is 25k spans and 1GB at 15-day retention; AX Pro is $50/month for 50k spans and 10GB at 30-day retention. Graduating from Phoenix to AX is a new contract, not a tier upgrade, and span-based pricing gets expensive on agent workloads.

What is agent observability?#

Agent observability is the practice of capturing and debugging the full execution of an AI agent, including every LLM call, tool call, retrieval, and sub-agent invocation. It differs from classical LLM observability because agent runs are long, non-deterministic, and deeply nested. Agent-specific tooling renders the run as a transcript, tracks outcomes in plain language, and lets a coding agent rerun the agent from a cached point. See our ranked list of the top agent observability platforms for the full field.

How much does an Arize Phoenix alternative cost?#

Pricing varies by model. Laminar: data-volume, free 1GB with 7-day retention, Hobby $30/month for 3GB, Pro $150/month for 10GB with 6-month retention. Langfuse: unit-based, free 50k observations, Core $29/month, Pro $199/month. LangSmith: seats plus traces, $39/seat/month plus $0.50 per 1k base traces. Braintrust: free tier plus usage-based Pro. Weave: scales with volume and seats. Helicone: free tier plus request-based plans. For agent workloads with large traces, Laminar's data-volume pricing with 20x compression is the most predictable. Self-hosting Laminar, Langfuse, and Helicone is free.

Last updated: August 2026. Verify features and pricing against each vendor's current documentation before committing.