Best Braintrust Alternatives for Agent Evals & Tracing (2026)

Why Teams Go Looking for Braintrust Alternatives

Teams rarely leave Braintrust because their evals stopped working. They leave because evals alone stopped being enough, or because the processed-data bill and the 14-day retention window forced the question sooner than expected.

To be clear about which product we mean: this guide covers Braintrust (braintrust.dev), the proprietary AI evaluation and observability platform built around experiments, datasets, and CI/CD score gating, not the talent marketplace that shares the name.

The best Braintrust alternatives depend on what you're optimizing for. Catalyst (inference.net) leads for production trace depth, automated failure analysis, and a path to fine-tuning. LangSmith fits teams built on LangChain or LangGraph. Langfuse is the strongest open-source option for tracing and prompt management, Arize Phoenix for open-source eval rigor, and MLflow for teams already running it.

This guide is different from the other pages ranking for this query, and the difference is structural. Every result on this SERP is a vendor arguing for its own product, including Braintrust's own alternatives page, which concludes there is no true substitute. We build Catalyst, so we have a position too. The difference is that we define seven evaluation criteria up front, score everyone against them (including ourselves and Braintrust), tell you when Braintrust is the right choice, and show you how to test an alternative without ripping anything out.

Read time: 12 minutes

What Braintrust Does Well — and Where Eval-Only Workflows Hit a Wall

Any honest comparison starts by acknowledging that Braintrust is very good at the thing it was designed for. It's the most polished eval-driven development workflow on the market: run experiments against datasets, compare prompts and models side by side, and block deploys in CI when scores regress. The open-source autoevals library ships code-based checks, LLM-as-a-judge scorers, and prebuilt scorers for common patterns like factuality.

The platform has momentum, too. Braintrust raised a Series B in February 2026 and counts Notion, Replit, Cloudflare, Ramp, and Dropbox among its customers. Recent releases include Loop, an in-platform AI agent that generates better prompts, scorers, and datasets from a description of what you want to optimize, plus Brainstore, a database purpose-built for storing and querying complex AI traces at scale.

Braintrust pricing: where the meter runs

Braintrust bills on processed data and scores rather than seats; no plan charges per user. Here's the current structure, verified June 2026:

Pro overages: $3/GB and $1.50 per 1k scores. No plan charges per seat. Verified June 2026.

The catch is what "processed data" means for agent workloads. Verbose multi-step transcripts burn through gigabytes quickly, and Starter overages run $4/GB plus $2.50 per 1,000 scores. The billing model quietly discourages the very thing production debugging requires: capturing everything.

The wall: braintrust observability stops at the eval loop

Four limits come up again and again when teams describe outgrowing the platform:

1. Production trace depth. Braintrust's loop runs log → dataset → experiment → score. What it's weaker at is deep production trace trees: long multi-step agent sessions with full message history and tool-call arguments preserved at every step.
2. Retention. Fourteen days on Starter and 30 days on Pro is enough for this sprint's experiment, not for "when did this regression actually start?"
3. Portability. Brainstore and the Braintrust SDK are proprietary. Your instrumentation investment doesn't move with you, unlike OpenTelemetry-based spans that any compatible backend can ingest.
4. The loop ends at evals. There's no in-platform continuation from production traffic to training datasets to a fine-tuned model. Once your eval scores plateau on prompt changes, the platform has nothing left to offer.

Loop and Topics are real movement toward automated analysis. Loop helps you author evals from failures, and Topics classifies traces by task, sentiment, and issues. But neither is scheduled, fleet-wide trace decomposition that hands you a ranked list of systemic failure modes with evidence. That distinction matters, and we'll come back to it.

How to Evaluate a Braintrust Alternative: 7 Criteria That Matter

Before looking at any vendor, decide what you're actually scoring. These seven criteria cover what production agent teams end up needing:

1. Trace fidelity. Does the platform capture full message history (user, system, assistant, and tool messages) plus tool calls with their JSON arguments and results? Scores without the underlying transcript can tell you that something failed, never why.
2. Session and agent grouping. Multi-turn sessions should group under stable agent identity, so "Support Agent, session 4187" is one navigable thing rather than forty orphan spans.
3. Online and offline evals. You need offline experiments before a change ships and continuous scoring of live traffic after. A platform that only does one leaves half the quality question unanswered.
4. Open standards. Spans emitted as OpenTelemetry with OpenInference semantic conventions can move between backends without re-instrumenting. Proprietary formats can't.
5. Cost tracking. Per-request cost, latency, and token usage across every provider you call, in one view.
6. Automated failure analysis. At production volume, nobody reads traces one at a time. Look for analysis that runs across many runs, names recurring failure modes, and cites the specific traces as evidence.
7. Path to fine-tuning. The endgame of observability is improvement. If production traffic can become curated datasets and then custom models, the loop closes. If not, you'll eventually bolt on another tool.

Score any platform against these, including Braintrust and including us.

Braintrust Alternatives Compared

Here's how the major alternatives stack up against those criteria:

Catalyst is the only platform here that continues from traces into datasets, fine-tuning, and deployment. Helicone is in maintenance mode following its March 2026 acquisition by Mintlify.

And the entry price of each platform's first paid tier, for the tools that have one:

Catalyst (inference.net) — tracing-first, full lifecycle

Catalyst approaches the problem from the production side. Its SDKs emit OpenInference-shaped OpenTelemetry spans capturing full message history, tool calls with JSON arguments and results, token usage, and errors, with auto-instrumentation for OpenAI, Anthropic, LangChain, LangGraph, OpenAI Agents, the Vercel AI SDK, Claude Agent SDK, Pydantic AI, and more, with over a dozen integrations in total. Rubric-based evals, datasets built from captured traffic, fine-tuning, and deployment live in the same platform. The free tier includes 1M spans and 1M gateway requests per month. Honest caveats: offline evals are available today while online evals are still coming, and the eval ecosystem is younger than Braintrust's.

LangSmith — best for LangChain and LangGraph teams

If LangChain or LangGraph runs your application, LangSmith's zero-config tracing captures every chain step, tool call, and agent action automatically, with human review queues, eval dataset management, and Insights clustering on top. The trade-offs: it's proprietary with self-hosting reserved for enterprise plans, and pricing stacks per-seat fees ($39/seat/month on Plus) on top of per-trace charges.

Langfuse — best open-source tracing and prompt management

Langfuse is MIT-licensed (all product features moved to MIT in June 2025), and every core feature is self-hostable for free, though v3 requires running a ClickHouse cluster. Cloud pricing is friendly: a free Hobby tier, Core at $29/month, Pro at $199/month, with unlimited users on paid tiers. Its evaluation layer is thinner than eval-first platforms: scoring and LLM-as-a-judge exist, but with limited built-in metrics and no multi-turn simulation.

Arize Phoenix — best open-source eval rigor

Phoenix (Elastic License 2.0) comes from the ML observability world and it shows: strong eval primitives, datasets and experiments, drift detection, and embeddings analysis. It's OpenTelemetry-native and maintains the OpenInference conventions most of this category now speaks. You run it yourself, or step up to the commercial Arize AX platform.

Laminar — best open-source agent debugging

Laminar (Apache 2.0) optimizes for debugging long-running agents: a transcript view instead of span trees, SQL over traces for ad-hoc analysis, natural-language Signals for outcome tracking, and re-running an agent from any span. Smaller ecosystem, sharp focus.

Helicone — gateway logging, now in maintenance mode

Helicone's proxy model (change your base URL, get logging plus caching and failover) made it the easiest integration in the category, with a free tier of 10,000 requests per month. But Mintlify acquired Helicone in March 2026 and active feature development has ended: only security patches, bug fixes, and new model support continue. Fine for basic request logging you already have; the wrong choice for a new platform bet.

MLflow — best if you already run MLflow

MLflow 3's GenAI track (Apache 2.0) adds tracing with a side-by-side trace comparison UI, multi-turn evaluation through mlflow.genai.evaluate, and an LLM-judge API with structured outputs. For teams with MLflow already deployed, it's the lowest-friction path. The ops burden is yours, and the UI remains the least agent-centric of this group.

Braintrust vs LangSmith: Quick Verdict

Choose Braintrust when eval-driven CI gating is the center of your workflow and you want framework-agnostic experimentation with the deepest scoring toolkit. Choose LangSmith when LangChain or LangGraph powers your application and zero-config, complete framework tracing justifies the vendor coupling.

Pricing models differ more than features. Braintrust bills processed data and scores with no per-seat charges; LangSmith bills seats plus traces, and retention is the real lever. Base traces cost $2.50 per 1,000 with 14-day retention, while extended traces double that to $5.00 per 1,000 for 400 days.

If your actual pain is production trace depth and automated failure analysis, note that neither tool is optimized for it. That's a different category of problem, covered below.

Braintrust vs Langfuse: Quick Verdict

This one is control versus depth. Choose Langfuse when open source and self-hosting are requirements: it's MIT-licensed, free to self-host, and your data never leaves your infrastructure. Budget for ClickHouse operations, though. Choose Braintrust when out-of-the-box eval workflow depth matters more than source access.

On managed pricing, Langfuse undercuts: Core at $29/month and Pro at $199/month with unlimited users, against Braintrust's Pro at $249/month.

Both, however, leave the same two gaps: neither runs scheduled cross-run failure analysis that cites evidence, and neither continues from traces into fine-tuned models.

Where Catalyst Differs

Three things separate Catalyst from everything above.

1. Open-standard tracing with a drop-in setup(). Catalyst's TypeScript and Python SDKs share one entry point: call setup() before your clients are constructed and every supported SDK is auto-instrumented; wrap agent runs in agent_span for session grouping. Here's the full Python setup, from the drop-in setup() quickstart:

# Install the SDK with the OpenAI extra:
#   pip install 'inference-catalyst-tracing[openai]'
#
# Configure export before your app starts:
#   export CATALYST_OTLP_ENDPOINT="https://telemetry.inference.net"
#   export CATALYST_OTLP_TOKEN="<your-token>"
#   export CATALYST_SERVICE_NAME="support-bot"

import os

from inference_catalyst_tracing import agent_span, setup
from openai import OpenAI

tracing = setup()  # auto-detects installed SDKs and instruments them
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Wrap the run so LLM spans nest under one AGENT row,
# grouped by agent and session in the dashboard.
with agent_span(
    tracing.tracer,
    agent_id="support-agent",
    agent_name="Support Agent",
    session_id="session-001",
) as span:
    span.set_input("Summarize ticket #4187.")
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "Summarize ticket #4187."}],
        max_tokens=16,
    )
    span.set_output(response.choices[0].message.content or "")

tracing.shutdown()  # flush batched spans before exit

Spans land as OpenInference-shaped OpenTelemetry with full messages, tool-call arguments, and token counts including cache usage, so your instrumentation is portable by construction rather than a lock-in surface.

2. Evals written in plain English. Instead of composing scorer code, you describe what "good" looks like as plain-English rubrics scored by an LLM judge, with direct rubrics (grade the output) and adherence rubrics (grade against a reference). Offline evals run today against datasets built from captured traffic or uploaded JSONL; online evals are coming. In the meantime, Signals classify live spans continuously: plain-language binary or labeled classifiers an LLM judge applies with deterministic sampling.

3. A one-line Gateway change that ends in a fine-tuned model. Point your SDK at https://api.inference.net/v1 and every request is captured with cost, latency, and full payloads at under 10ms of overhead. That captured traffic becomes datasets, datasets feed training, and trained models deploy behind the same API. No other tool in this comparison closes that loop in one platform.

The free tier covers 1M spans and 1M gateway requests monthly, with 50 GB of data for each. That is enough to instrument a real production agent before paying anything.

How Halo Closes the Loop Braintrust Leaves Open

Trace data is only useful if someone reads it, and at production volume nobody reads it. This is the specific gap where eval-first platforms stop: they can score outputs, but they can't tell you what's systematically going wrong across last week's ten thousand runs.

Halo, Catalyst's open-source RLM-based analysis engine, exists for exactly that. It reads OpenTelemetry-compatible spans, decomposes them across many runs to find systemic failure modes, and returns a ranked list of findings with the specific trace IDs each one came from, so every claim is one click from its evidence. You can run it on demand against a time window, ask it pointed questions ("which tool calls return empty results most often?"), or put it on scheduled trace analysis with Halo so every run produces a report in the analysis history. That is how regressions surface before users report them.

The contrast with Braintrust's Loop is worth being precise about. Loop is an authoring assistant: you describe what you want to optimize, and it generates better prompts, scorers, and datasets. Halo's unit of analysis is the fleet (every run in a window), and its output is an engineering to-do list with citations. One helps you write the test; the other tells you what's broken.

Findings then feed the rest of the loop: failure modes become datasets built straight from production traffic, datasets become eval benchmarks and training data, and fine-tuned models ship back behind the same gateway.

Figure 3: The closed improvement loop

If ranked failure modes with trace-level evidence is the thing your current stack can't do, this is the part worth testing first.

Migration Considerations: Run It Alongside What You Have

You don't need to rip anything out to evaluate an alternative. The lower-risk pattern is dual-write: keep your current tool, mirror traces to the candidate, and compare what each shows you for the same production week.

Coming from Langfuse, it's an env-var change. Catalyst accepts the Langfuse SDK's ingestion endpoints and converts traces, generations, usage, costs, sessions, and tags into Catalyst spans: no SDK swap, no code changes. The whole migration is forwarding Langfuse traces with an env-var change:

# Keep all of your existing Langfuse tracing code.
# Point the Langfuse SDK at Catalyst with three env vars:
export LANGFUSE_HOST="https://telemetry.inference.net"
export LANGFUSE_PUBLIC_KEY="pk-catalyst"   # compatibility only; value never used
export LANGFUSE_SECRET_KEY="<your-catalyst-api-key>"

Coming from LangSmith, set the LangSmith SDK to OpenTelemetry mode and Catalyst bridges its spans into the same tracer provider, so traceable functions stay visible alongside the rest of your application traces. Install is one line per language; see bridging LangSmith spans into Catalyst:

# TypeScript
bun add @inference/tracing langsmith

# Python
pip install 'inference-catalyst-tracing[langsmith]'

Coming from Braintrust, run Catalyst's setup() in parallel. Braintrust's SDK logging and OpenTelemetry instrumentation don't conflict, so Braintrust experiments keep running while production traces flow to Catalyst for the trace-depth and analysis comparison.

For the security review that accompanies any vendor evaluation: inference.net is SOC 2 Type II compliant; API keys, tokens, and credentials are detected and excluded from all traces and logs with no configuration required; data is encrypted in transit and at rest. Request data isn't used for model training by default, and retention controls are configurable down to turning retention off entirely.

When to Stay on Braintrust — and When to Switch

A straight checklist, because sometimes the right answer is to stay.

Stay on Braintrust if:

• Eval-driven CI gating is the center of your development workflow, and it's working
• Your bottleneck is offline experiment velocity, not production debugging
• Your data volume is modest and 30-day retention covers your needs
• Loop and Topics are actively saving your team authoring time
• You have no fine-tuning plans

Switch (or at least dual-write) if:

• Debugging production agents requires full trace trees with tool-call arguments, and you don't have them
• You want instrumentation on OpenTelemetry and OpenInference so it ports between backends
• Processed-data billing is punishing verbose agent transcripts
• You want scheduled, cross-run failure analysis with cited evidence, not just per-trace classification
• You want production traffic to become datasets and fine-tuned models without adding another vendor
• You need longer retention without an enterprise contract

The honest summary: Braintrust remains the strongest pure eval workflow. Alternatives win when production observability and the full improvement loop matter more than experiment polish.

FAQ

Is Braintrust open source?

No. Braintrust is a proprietary, closed-source platform, though its autoevals scorer library is open source. If open source is a requirement, look at Langfuse (MIT), Arize Phoenix (Elastic 2.0), Laminar (Apache 2.0), or MLflow (Apache 2.0).

How much does Braintrust cost?

The Starter plan is free with 1 GB of processed data, 10,000 scores, and 14-day retention; overages run $4/GB and $2.50 per 1,000 scores. Pro is $249/month with 5 GB, 50,000 scores, and 30-day retention. Enterprise is custom-priced. No plan charges per seat.

What is the best Braintrust alternative?

It depends on the criterion. For production trace depth, automated failure analysis, and a path to fine-tuning: Catalyst. For LangChain/LangGraph-native tracing: LangSmith. For open-source self-hosting: Langfuse. For open-source eval rigor: Phoenix. For an existing MLflow stack: MLflow 3.

Can I run Catalyst alongside Braintrust or LangSmith?

Yes. Catalyst's OpenTelemetry instrumentation runs in parallel with Braintrust's SDK logging, LangSmith spans bridge in via OTel mode, and Langfuse traces forward with three environment variables. Dual-writing for a week or two is the standard evaluation pattern.

The Bottom Line

Pick among Braintrust alternatives by criteria, not by whichever vendor's comparison page you landed on. Score trace fidelity, session grouping, online and offline evals, open standards, cost tracking, automated failure analysis, and the path to fine-tuning, then weigh what your team actually debugs day to day. Braintrust earns its place when the eval workflow is the product. When the question becomes "why is the agent failing in production, and what do we train to fix it," the loop has to extend further than scores.

That full loop — trace, analyze, evaluate, fine-tune, deploy — is what Catalyst was built around, and the free tier is enough to run a real comparison this week.

Related Reading

• Agent Observability: A Practical Guide for Production AI Teams — the observability practices this comparison scores, in depth
• LLM Tracing: What to Capture in Production Agents — expands the trace-fidelity criterion that separates these tools
• AI Regression Testing for LLM Apps — the eval-gating workflow Braintrust users rely on, and how to keep it when you switch