Agent observability pricing compared

The Table

Read the meter column before the price column. A run, an event, a trace, a unit, a credit and a gigabyte are not the same thing, and the cheapest plan for your team depends on how many of each your agent produces. Failproof AI's own prices are on /pricing.

What Each Meter Actually Counts

• Runs and evals (Failproof AI): agent runs ingested into Cloud, and evaluations counted separately - each session-and-evaluation pair is one billable evaluation, so two evaluations on one session count twice. The free tier is a hard cap with no overage.
• Events (Raindrop): the plans doc prices events without defining one, so count what your SDK actually sends.
• Traces (Galileo): priced by trace count; how many your agent produces depends on how you instrument it.
• Units (Langfuse): "any tracing data point" - traces, observations (spans, events and generations) and scores. An agent session with twenty tool calls is twenty-odd units, not one.
• Credits (Latitude): the pricing page does not say what a credit measures. Ask before you estimate.
• Processed data and scores (Braintrust): gigabytes of data processed plus each score, and model credits at token rates. Trace size drives the bill.
• Storage, AI credits and gateway requests (Future AGI): storage at $2 per GB above 50 GB, AI credits for evaluations, guardrails and synthetic data at $10 per 1,000, gateway requests at $5 per 100,000, plus simulation. The page does not say how many credits one evaluation uses.

Two Worked Examples

Every number below rests on assumptions you should replace with your own. One agent session is one Failproof AI run, one Galileo trace and one Raindrop event, and each evaluated session runs one Failproof AI evaluation. In Langfuse a session produces 1 trace, 20 observations and 2 scores, so 23 units. In Braintrust a session is 40 KB of processed data, and each evaluated session gets 2 scores. Overage is billed pro rata. Judge-model tokens are excluded, because on most of these platforms the judge is a model you pay for separately.

A: 20,000 Sessions a Month, 5 People, 10% of Sessions Evaluated

B: 500,000 Sessions a Month, 25 People, SSO Required, 4% of Sessions Evaluated

Drop the SSO requirement and the picture changes. Braintrust Pro would be $249 + 15 GB × $3 = $294 (20 GB processed, 40,000 scores inside the allowance), and Raindrop Pro $299 + 500,000 × $0.003 = $1,799. Failproof AI Team would be $99 + 450 × $1.00 = $549: all 20,000 evaluations fit inside its 50,000-evaluation allowance. That makes Team $50 cheaper than Scale at this usage, though Scale adds SSO/SAML, more users and substantially more included capacity. Trace size does the most work in the Braintrust figure: at 200 KB a session it becomes 100 GB and $534.

What the Numbers Mean

Three patterns hold across the table. The free tiers are for trying, not running: they cap at 1,000 to 50,000 of something, and two of them, Failproof AI and Raindrop, stop rather than bill past the cap. SSO is the real price line for a mid-sized company: on most platforms it only comes with Enterprise, which means a sales call and an unpublished price. A published price is not always a self-serve one, either: Galileo lists Pro at $100 a month but routes it through "Book a Demo", so its Free tier is the only one you can start alone. And unpublished is not the same as expensive; it is slower to find out.

An evaluation allowance and the model cost behind an LLM-based evaluation are separate parts of the estimate unless a vendor explicitly says otherwise. Failproof AI counts each session-and-evaluation pair against the plan's evaluation allowance. Braintrust bills model credits at token rates, Future AGI charges AI credits for evaluations, and Galileo prices Luna-2 by token on Enterprise. For every platform, confirm whether the published allowance includes model usage or whether you also pay the model provider, then budget sessions evaluated × tokens per judgment × model price where applicable.

What Happens After the Platform Finds a Failure?

Detection is only useful when it leads to a decision. Failproof AI uses code-based and LLM-based evaluations to find failures across production sessions, groups related failures into findings, and recommends a fix. Alerts bring the right findings to the team. When the fix is a change to agent behavior, the finding can become a policy that steers or blocks the agent.

• Failproof AI connects the finding to the fix. A generated policy can be tested against past agent activity, deployed in observe mode and then used to steer or block a tool action.
• Future AGI checks model and tool traffic sent through its gateway or SDK. Protect and Tool Permissions can block, warn, mask or log.
• Galileo offers hosted guardrails on Enterprise. Agent Control checks model and tool inputs and outputs, and can deny or steer a request before allowing it through.

Judgment Labs, Raindrop, Latitude, Langfuse and Braintrust evaluate completed activity and alert the team. They do not provide the same path from a discovered failure to a tested policy. If the agent can run commands, move money, change files or send messages, check whether the product helps your team fix the behavior as well as detect it.

When You Do Not Need to Buy Anything

If one person looks after one agent that handles a few thousand sessions a month, the free tiers above cover it, and so does a pytest file with a handful of checks. If you need everything on your own infrastructure and have someone to run it, Langfuse and Latitude self-host under MIT. Future AGI has an Apache-2.0 core with separately licensed enterprise code. In each case, the practical price includes your servers and the time required to operate the stack.

If you only need inexpensive trace storage and scoring, Failproof AI is not the cheapest option at small volume. In example A, Braintrust Starter comes to $0 and Langfuse Core to $57.80, compared with $99 for Failproof AI Team. Failproof AI becomes useful when nobody can read every session and you need the system to find recurring failure modes, explain what is going wrong and recommend a fix. Steering or blocking the agent is an additional step for failures that can be prevented at runtime.