Braintrust vs Galileo vs Maxim AI

TL;DR

Three platforms, three meters, one shared gap. Braintrust meters scores and publishes every number self-serve, from $0 to $249/mo flat. Galileo meters traces and gates its real-time guardrails to a quote-based Enterprise tier. Maxim meters per seat plus monthly logs and gates in-VPC deployment to Enterprise. All three are closed source, and Galileo is being acquired by Cisco.

Pick Braintrust when you want a developer-owned experiment loop: datasets, scorers, run-to-run diffs, and CI regression gates you wire in yourself, framework-agnostic and metered by scores (10k free, 50k on the $249/mo Pro plan). Pick Galileo when you want an enterprise quality platform that ships proprietary scorers and inline protection: its Luna evaluation models run 20-plus out-of-the-box metrics and power a real-time guardrail layer, metered by traces (5k free, 50k on the $100/mo-billed-yearly Pro plan), with guardrails on quote-based Enterprise. Pick Maxim when you are shipping a multi-step agent and need to simulate it across personas and scenarios before production, metered per seat plus logs (free up to 3 seats, then $29/seat on Pro). All three are closed source, and all three put their top capabilities behind a sales motion.

Quick Comparison

Braintrust is the eval-first developer loop billed on scores, $0 to $249/mo flat, with every price published. Galileo is an enterprise eval-and-guardrails platform billed on traces, free to 5k then $100/mo billed yearly, with guardrails on quote-based Enterprise. Maxim is an agent-simulation platform billed per seat plus logs, free to 3 seats then $29/seat. All three are closed source. The table lines up every dimension.

Pricing and Transparency

Braintrust publishes its full price and runs self-serve from $0 to $249/mo flat. Galileo and Maxim publish a free and a first paid tier, then move the top to quote-based Enterprise. Galileo's real-time guardrails and Maxim's in-VPC deployment both live behind that sales motion, so the number you most want to see is the one you cannot. The three meters differ too: scores, traces, and seats plus logs.

Braintrust's plans are sized in scores, plus processed data and tokens, and the price is flat. The free Starter plan ships $10 of credits, 1 GB of processed data, 10k scores, 14-day retention, and unlimited users. Pro is $249/mo for 5 GB, 50k scores, 30-day retention, and RBAC, with no tier in between, which is the cliff Cekura's teardown flags: teams that outgrow Starter go straight to $249/mo with no hard spending cap. Overage on Starter is $4/GB plus $2.50 per 1k scores; on Pro it drops to $3/GB plus $1.50 per 1k scores. Tokens are billed separately at $0.06/M input and $0.40/M output. On-prem and hybrid deployments are Enterprise.

Galileo's plans are sized in traces. The free plan covers 5,000 traces a month with unlimited users, but no RBAC, advanced analytics, or dedicated support. Pro is $100/mo billed yearly, or about $150/mo billed monthly, for 50,000 traces, RBAC, advanced analytics, and dedicated support, and the pricing page notes pricing scales with trace volume. Enterprise is quote-based and adds unlimited traces, VPC and on-prem deployment, SSO, and the real-time guardrails. That last gate is the one a Cekura teardown calls out: the real-time guardrails are "only on the Enterprise plan" and not available on Free or Pro, so the number stays behind a sales cycle.

Maxim's plans are sized per seat plus monthly logs. The free Developer plan covers up to 3 seats, 10k logs a month, and 3-day retention, with no simulation runs. Pro is $29 per seat per month for 100k logs a month, plus the simulation runs and online evals the free tier withholds, verified on the Maxim pricing page. Business is $49 per seat per month for 500k logs a month and 30-day retention. Enterprise is quote-based and adds in-VPC deployment with SOC2 and HIPAA controls. Because the meter is per head, a 10-person team is $290/mo on Pro or $490/mo on Business before anyone runs a single eval.

The shape of each bill follows its meter. Braintrust's Pro is $249/mo no matter how many people log in; the cost moves with how many scores and how much data your evals produce. Galileo bills the record of a request, so a service handling production traffic generates a trace on every request whether or not anyone evaluates it. Maxim bills the seat, so the cost tracks headcount, not eval volume. A small team running heavy evals pays Braintrust for the scores; a service watching live traffic pays Galileo for the traces; a larger team that wants everyone in one agent platform pays Maxim for the seats.

What Agent Quality Means at Each

Agent quality means a different mechanism at each vendor. Braintrust scores your outputs against fixed datasets with scorers you write. Galileo scores traffic with its proprietary Luna-2 models running 20-plus built-in metrics you do not author. Maxim generates multi-turn conversations with synthetic personas before deploy. One is your eval logic, one is a vendor engine, one is simulated traffic, and the difference decides how much code you write and how much you can inspect.

Braintrust owns the scaffolding, you own the scoring. Datasets, scorers, and the diff between runs are the primary objects. You bring your own scoring functions, write run-to-run diffs as first-class objects, and drop regression gates into CI. The eval logic is yours, so when a score looks wrong you open the scorer and fix it. It is framework-agnostic by design and does not assume you live in any one library or buy any one set of metrics.

Galileo ships the scoring engine. It runs 20-plus out-of-the-box evaluation metrics for RAG, agents, safety, and security, including hallucination, context adherence, and chunk attribution, all computed by its own Luna evaluation models rather than scorers you author. That is less code to write and a faster path to a populated dashboard, in exchange for leaning on Galileo's metric definitions. Braintrust's own teardown makes the tradeoff plain: Galileo's Luna evaluation logic is proprietary, vendor-maintained, and opaque, so when a score looks wrong you cannot open the scorer the way you can with your own.

Maxim manufactures the test traffic. Where Braintrust scores datasets you already have, Maxim generates the interactions: multi-turn conversations driven by synthetic users and custom personas across thousands of scenarios, run before live traffic. For a single-shot prompt that machinery is overkill. For a multi-step agent, a static dataset cannot capture the third turn where the agent loops or the persona that pushes it off-policy, and simulation exists to surface those before a user finds them. The caveat is that simulation is still offline: a synthetic persona is a model's guess at a user, scored before deployment, so it narrows the gap to reality without closing it.

Galileo's Real-Time Guardrails

Galileo ships a genuine inline guardrail layer, and neither Braintrust nor Maxim has an answer to it. Its Luna-2 small language models score traffic before a tool executes, which Galileo reports at sub-200ms latency. The edge is real. The catch is access: the guardrails are listed as an Enterprise-tier feature, so the inline protection comes with a quote, not the self-serve Pro plan.

Luna-2 is a family of small language models distilled from LLM-as-judge evaluators, where each metric is a lightweight head on a shared backbone so many specialized scorers run concurrently. Galileo reports Luna-2 runs at sub-200ms latency and roughly $0.02 per million tokens, which is what makes it cheap enough to score production traffic rather than samples. Braintrust's scorers and LLM-as-judge evals run offline on samples in the experiment loop, and Maxim's evaluators run in simulation before deploy. Neither blocks a malicious input or a bad agent action inline the way Galileo's guardrail layer does.

So if your problem is real-time protection, Galileo is the only one of the three built for it, and that is a real reason to pick it. The honest qualifier is the same one its own pricing carries: real-time guardrails are an Enterprise capability, so the inline protection lives behind the quote-based platform, not the self-serve Pro plan. The capability is real; the way you buy it is a sales conversation, and the Cisco acquisition adds a question about how that capability evolves inside Splunk.

When Braintrust Wins

Braintrust wins when your bottleneck is offline evaluation and you want to own the eval logic on a published, self-serve price. You bring your own scorers, datasets, and run-to-run diffs as first-class objects, drop regression gates into CI, and read your cost off the page in scores rather than negotiating it. It is the better pick for a framework-agnostic team that wants a neutral eval scaffold, not a prebuilt metric set or a sales motion.

• You want to own the eval loop: your own scorers, datasets, and run-to-run diffs as first-class objects.
• Your bottleneck is offline evaluation, "is this prompt or model change better" on fixed datasets, with regression gates in CI.
• You want self-serve, published pricing tied to scores, the unit your experiment loop actually generates.
• You are framework-agnostic and do not want a tool that assumes one library or one set of metrics.

When Galileo Wins

Galileo wins when you want real-time protection and prebuilt metrics instead of scorers you author. Its Luna-2 models score traffic inline before tools execute and run 20-plus out-of-the-box evaluations, which is the faster path to a populated dashboard for an enterprise buyer who needs VPC or on-prem deployment and SSO. The tradeoff is that the inline guardrails live on the quote-based Enterprise tier, so the price is a sales conversation, and the Cisco acquisition is a roadmap variable.

• Your problem is real-time protection: blocking bad inputs or agent actions inline before tools execute.
• You want 20-plus out-of-the-box metrics computed by a proprietary engine, not scorers you author yourself.
• You are an enterprise buyer who needs VPC or on-prem deployment, SSO, and a dedicated success motion.
• You are comfortable opening a sales cycle for the inline guardrails and weighing the Splunk roadmap.

When Maxim Wins

Maxim wins when you are shipping a multi-step agent and need to simulate it across personas and scenarios before production, with experimentation, eval, and tracing in one platform. It fits a team large enough that per-seat access to one platform beats wiring up separate tools, before the per-seat bill starts to compound. The published Pro and Business tiers let you start self-serve, and in-VPC deployment waits on the quote-based Enterprise tier.

• You are shipping a multi-step agent and need to simulate it across personas and scenarios before production.
• You want experimentation, simulation, eval, and observability in one platform rather than stitched together.
• You want production tracing organized into sessions that show how context evolves turn by turn.
• Your team is small enough that per-seat pricing has not started to compound across many users.

The Fourth Option: Own the Stack

There is a path none of the three markets, and a growing number of teams take it: skip the per-score, per-trace, and per-seat meters and instrument yourself. You wire open-source tracing into a datastore and dashboards you run, give up the polished managed UI and the proprietary metrics, and in return owe no per-unit meter and no enterprise contract for inline scoring. It is the only option here that is open source end to end.

For the always-on signals, you instrument with OpenLLMetry (Apache-2.0, ~7.2k stars, free), store the spans in your own ClickHouse (Cloud Basic around $66/mo, or self-host the Apache-2.0 build for free), and dashboard it with Grafana OSS. You give up the polished experiment UI, the managed retention, Galileo's proprietary Luna metrics, and Maxim's simulation surface, and you take on the operational weight in return for no per-unit meter and no sales motion. We walk through the whole build in build your own LLM observability.

What They All Miss: Semantic Signals in Production

All three score a proxy for production, not the turn in front of the user. Braintrust runs LLM-as-judge offline on samples. Galileo scores inline but only on the quote-based Enterprise tier. Maxim simulates synthetic personas before deploy. A wrong refund-policy answer still returns a 200 with normal latency and a normal token count, a frustrated user produces the same span as a happy one, and the batch or simulation that would catch it ran before the user arrived.

A Reflex closes that specific gap: an inline per-turn classifier you call directly, self-serve, regardless of which platform you picked. Where Braintrust's LLM-as-judge runs offline on samples, Galileo's inline layer is gated behind enterprise, and Maxim's checks run before deployment on synthetic users, a Reflex returns the label as an API response, in under 90 milliseconds, cheap enough to score every turn rather than a sample, on the same self-serve plan as everything else Morph ships.

For the multi-step agents all three platforms target, two built-ins matter most: stuck-in-a-loop, which fires the instant an agent starts repeating itself, and incomplete-thought, which catches a turn that trails off before it finished the task. Those are exactly the failures a simulation hopes to predict and a live turn actually produces. The built-in signals cover jailbreak, guardrail, leaked-thinking, stuck-in-a-loop, incomplete-thought, ambiguity, difficulty, and domain, and you can train a custom signal in under an hour in the Reflex dashboard. Pricing is realtime at $0.0005 per event, where one event is 2,048 tokens, which is what makes scoring every turn affordable instead of sampling. It is live and self-serve today.

curl -X POST "https://api.morphllm.com/v1/reflex/predict" \
  -H "Authorization: Bearer $MORPH_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "stuck-in-a-loop", "text": "<the agent turn>"}'

# {
#   "model": "stuck-in-a-loop",
#   "mode": "single_label",
#   "classes": [
#     { "class_id": 0, "label": "progressing", "score": 0.04, "selected": false },
#     { "class_id": 1, "label": "looping",     "score": 0.96, "selected": true }
#   ],
#   "inference_time_ms": 88
# }

The label with "selected": true is the answer; there is no separate top-level field to read. Because it comes back as an API response and not a dashboard panel, it composes with whichever platform you picked: write it onto a Braintrust span as a score, attach it to a Galileo trace, attach it to a Maxim session, alert on it in Slack, or route on it inline. It complements an eval, guardrail, or agent platform; it does not replace one. Full docs are at docs.morphllm.com.

Frequently Asked Questions

Braintrust vs Galileo vs Maxim: what is the core difference?

Three meters, three mechanisms. Braintrust is the developer experiment loop, framework-agnostic and metered by scores. Galileo is an enterprise eval and observability platform built on its own Luna models and real-time guardrails, metered by traces. Maxim is an agent platform built around multi-turn simulation, metered per seat plus logs. See what agent quality means at each for how to pick.

How much do they cost?

Braintrust Starter is free ($10 credits, 10k scores); Pro is $249/mo for 50k scores. Galileo Free covers 5k traces; Pro is $100/mo billed yearly ($150 monthly) for 50k traces; Enterprise is quote-based. Maxim Developer is free (3 seats, 10k logs); Pro is $29/seat for 100k logs and Business is $49/seat for 500k logs. The pricing section has the full overage math.

Are any of them open source?

No. All three are closed source, with self-hosting on quote-based Enterprise only. If open source is a requirement, look at Braintrust vs Langfuse or the DIY stack.

Which one has real-time guardrails?

Galileo. Its Luna-2 models power an inline guardrail layer that scores traffic before tools execute; Braintrust scores offline on samples and Maxim simulates before deploy. The caveat is that Galileo lists real-time guardrails as an Enterprise-tier feature. See Galileo's real-time guardrails.

How do they compare to LangSmith and other tools?

LangSmith is trace-first monitoring tied to LangChain, a different center of gravity. The Braintrust vs LangSmith breakdown covers the eval-versus-trace split, and Braintrust vs Arize vs Opik and Braintrust vs Vellum vs PromptLayer map the rest of the field.