Production AI systems fail silently. Hallucinations slip through, quality regresses, prompts drift — and nobody notices until users start complaining.
Five platforms are competing to fix this for engineering teams: Braintrust, Arize, Maxim, Galileo, and Fiddler. Most comparisons you’ll find are enterprise feature checklists that don’t help a team of 5 to 15 engineers without dedicated MLOps resources figure out what they can actually implement and afford.
This article is for that team. We’ve pulled together the SMB cost context, team-size fit, and a recommendation matrix built around real-world constraints. The two modes of production evaluation — offline pre-deployment testing and online post-deployment monitoring — give this comparison its structure. If you want the broader governance context first, start with benchmark governance.
What is production AI evaluation and why does it differ from benchmark testing?
Benchmark testing tells you what a model can do under controlled conditions. Production evaluation tells you what it actually does when real users get their hands on it.
That gap matters more than most teams realise. Gartner reported that 85 per cent of GenAI projects fail because of bad data or models that weren’t properly tested. Air Canada was held legally liable after its chatbot gave out false refund information. Apple suspended its AI news feature in January 2025 after it started generating misleading headlines. Without production evaluation, you’re relying on user complaints as your quality signal.
Production evaluation works in two complementary modes:
Offline evaluation (pre-deployment) runs your AI outputs against labelled datasets and automated scoring criteria before code reaches production. It catches regressions from prompt edits, model swaps, or parameter changes before they reach users.
Online evaluation (post-deployment) scores live production traffic automatically, picking up hallucinations, policy violations, and quality degradation that curated test sets never anticipated.
You need both. Offline catches regressions before release. Online catches distribution shifts after. And if you want to understand how this connects to a broader governance approach, benchmark governance is where policy becomes a quality gate.
What is offline evaluation and how does it integrate with CI/CD?
Offline evaluation is your pre-deployment quality gate. You maintain versioned evaluation datasets, run your current model or prompt against them on every change, and compare scores to established baselines. If a prompt edit or model swap drops scores below a threshold you’ve defined, the deployment is blocked.
In practice: a developer makes a prompt change and opens a pull request. A GitHub Actions step triggers the evaluation suite against the versioned dataset, compares results to the last passing baseline, and fails the CI job if any metric drops below threshold. Braintrust has the most documented implementation of this pattern — its native GitHub Action runs evaluations on every pull request and posts results as comments.
Start your evaluation dataset with 50 to 100 representative inputs covering common queries, edge cases, and inputs you already know trigger hallucinations. Build it out from production logs over time. For regression thresholds, a reasonable starting point is blocking deployment if faithfulness drops more than 3 per cent from baseline — but validate any threshold against 50 or more manually reviewed outputs before you automate it.
For how to operationalise evaluation as part of a governance process, the internal governance framework walks through the steps.
What is online evaluation and what does it catch that offline evaluation misses?
Your pre-deployment test sets can’t anticipate everything. Production traffic is messier, stranger, and more adversarial than anything you’ll build into a curated dataset.
Online evaluation scores live outputs as they happen, catching prompt injection attempts on real traffic, hallucinations triggered by unusual inputs your test dataset never included, quality degradation as underlying model APIs update without warning, and session-level failures in multi-step agents where a trajectory breaks down across turns.
There’s also a data flywheel worth setting up early. Flagged production outputs become new entries in your offline evaluation datasets — production failures improve test coverage, which catches more pre-deployment regressions, which reduces production failures. It compounds over time.
Evaluating every production output is expensive, so score 5 to 10 per cent of random outputs and supplement with targeted sampling of negatively-rated outputs and known problem categories. Adjust the sampling rate once you have baseline data — if your issue rate is low, 5 per cent is fine; if you’re catching frequent regressions, push toward 20 per cent until things stabilise.
Multi-step agent evaluation requires session-level tracing — preserving a full correlation ID from user click to final answer, capturing tool calls and reasoning steps across the entire interaction. Not all five platforms handle this equally, which is exactly why Maxim exists.
What is LLM-as-a-judge and what are its reliability limits?
All five platforms use a capable language model — typically GPT-4 or equivalent — to score production AI outputs against defined criteria. It replaces or supplements human reviewers at a fraction of the cost. This is not a differentiator between platforms. It’s a shared dependency with known failure modes.
Those failure modes: self-preference bias (models rate their own outputs higher), format gaming (well-structured outputs score higher regardless of accuracy), position bias (first options in a list score higher), and verbosity bias (longer answers score higher regardless of relevance).
The industry target is 85 to 90 per cent agreement between the LLM judge and human reviewers on the same rubric. Validate on 50 manually reviewed samples; if agreement is below 85 per cent, narrow your evaluation criteria before automating. Recheck quarterly or whenever prompts, models, or content types change.
On cost: GPT-4 as judge at one million daily evaluations runs approximately $2,500 per day. For SMB teams at 10K to 100K evaluations per month, GPT-4o costs are typically well under $100 per month. Galileo Luna-2 brings this down to approximately $0.02 per million tokens — roughly 97 per cent lower. ChainPoll uses multi-model consensus to reduce single-judge bias without multiple GPT-4 calls.
For what to look for when assessing vendor claims about AI system quality, see requiring evaluation artefacts from vendors.
How does Braintrust compare for small developer teams?
For early 2026, Braintrust is our pick for best overall production AI evaluation platform. The pitch: offline experiments, online scoring, CI/CD integration, and regression tests in a single platform connected directly to your development workflow.
Developer experience is where it pulls ahead. Python and TypeScript SDK, native GitHub Actions integration, an Autoevals library for common scoring patterns, and an AI assistant (Loop) that generates evaluation components from production data. The core workflow is clean: production failure converts to a test case in one click, prompt change triggers automatic evaluation before shipping, quality regression blocks the pull request.
SMB cost context: The free tier covers 1M trace spans/month, 10K scores, and unlimited users. Pro is $249/month. No “contact sales” required to find out the price — you know what you’re getting into before you commit.
The limitations: agent tracing for multi-step tool-call chains requires external instrumentation, and governance and compliance controls are still maturing. It’s not open-source.
Best fit: Developer-led teams of 5 to 15 engineers starting from zero evaluation infrastructure who want a single platform for offline evaluation, online monitoring, and CI/CD gating without a procurement process.
How does Arize compare for teams with compliance requirements?
Arize is actually two distinct products — a dual model unique among these five platforms.
Arize Phoenix is fully open-source, built on OpenTelemetry standards, and self-hostable at zero licensing cost. You get complete multi-step agent tracing, scalable storage adapters, and a plugin system for custom evaluation judges. Teams can start self-hosted and migrate to Arize AX without re-instrumenting.
Arize AX (managed cloud) is built for enterprise compliance: SOC 2 Type II, HIPAA support, ISO certifications, audit trails, and role-based access control. The data flywheel is built in — trace collection, online evaluations, and human annotation workflows all feed continuous model refinement from production data.
SMB cost context: Phoenix is free to self-host. Arize managed cloud starts from $50/month. AX free tier: 25K spans/month for 1 user. AX enterprise compliance requires custom pricing negotiation.
The limitations: AX evaluation features depend on external tooling for structured offline experiments. Self-hosting Phoenix requires DevOps competence for upgrades and storage management — someone needs to own it.
Best fit: Compliance-driven teams in healthcare, finance, or government should look at Arize AX. Cost-sensitive teams should use Arize Phoenix, with an AX upgrade path if compliance requirements emerge later.
How do Maxim, Galileo, and Fiddler address specialised evaluation needs?
These three each target a specific evaluation niche rather than competing as general-purpose platforms.
Maxim AI specialises in multi-step agent simulation and pre-production scenario validation — evaluating complete agent decision paths (tool-call chains, multi-turn conversations, reasoning sequences) rather than individual responses. A single response can look fine while the full trajectory fails. Maxim’s simulation suite catches this before production.
SMB cost: Free (3 seats, 10K logs/month, no online evaluation). Professional: $29/seat/month — a 10-person team reaches $290/month quickly.
Best fit: Teams building multi-step AI agents who need pre-production trajectory simulation.
Galileo AI differentiates through Luna, its purpose-built evaluation model family, and ChainPoll multi-model consensus. Luna-2 handles hallucination detection, factuality scoring, prompt injection identification, and PII detection at approximately 3 per cent of GPT-4 cost.
SMB cost: Free: 5,000 traces/month. Pro: $100/month (50,000 traces).
Best fit: Teams with high production traffic where LLM-as-a-judge cost and bias are the primary concerns.
Fiddler AI targets regulated industries with explainability, compliance scoring, and in-environment guardrails. Fiddler Trust Models run inside your own environment — no proprietary data exposure, no unpredictable per-call API costs. Hierarchy drill-down from app to session to agent to span supports forensic investigation of agentic failures for audit purposes.
SMB cost: Free Guardrails tier with limited scope. Full platform: enterprise custom pricing, contact sales only. There’s no self-service entry point — a practical barrier for teams under 50 engineers.
Best fit: Regulated industries with enterprise procurement budget requiring in-environment evaluation and governance audit trails.
Which tool fits which team profile and budget?
Here is the platform comparison in a scannable format:
- Braintrust: Free tier covers 1M spans/month with 10K scores and unlimited users; Pro at $249/month. Strong offline eval and native CI/CD. Partial open-source. Good SMB fit.
- Arize: Free managed cloud covers 25K spans/month for 1 user; managed cloud from $50/month; AX enterprise is custom. Strong online monitoring. Full open-source via Phoenix. Good SMB fit via Phoenix.
- Maxim: Free tier covers 10K logs/month for 3 seats (no online eval). Professional at $29/seat/month. Strong agent simulation. No open-source. Medium SMB fit.
- Galileo: Free tier covers 5K traces/month; Pro at $100/month. Automated hallucination detection with Luna. No open-source. Good SMB fit.
- Fiddler: Free Guardrails tier only; full platform enterprise custom. In-environment Trust Models. No open-source. Low SMB fit without enterprise budget.
Recommendation matrix
Team Profile 1 — Developer team starting from zero (5–10 engineers, no MLOps, minimal budget): Start with Braintrust free tier. Set up offline evaluation with 50–100 representative inputs, integrate GitHub Actions for CI/CD gating, add online monitoring after your first production release. Arize Phoenix is the zero-licensing-cost alternative if the team has containerised service experience and someone who will actually own the infrastructure.
Team Profile 2 — Compliance-driven team (5–15 engineers, regulated industry, SOC 2/HIPAA requirements): Arize AX for audit trails, role-based access control, and certified compliance posture. Fiddler if in-environment guardrails and explainability are required and budget allows enterprise pricing. Arize Phoenix self-hosted in a compliant environment is a viable middle ground.
Team Profile 3 — Agent-focused team (5–15 engineers building multi-step AI agents): Maxim for pre-production agent simulation and trajectory evaluation. Complement with Braintrust for general offline evaluation of non-agentic components.
Team Profile 4 — High-volume production team (10–15 engineers, large production traffic, cost-sensitive): Galileo with Luna model — approximately 97 per cent lower per-evaluation cost than GPT-4. Evaluate every trace rather than a sampled fraction at Pro pricing.
Zero-to-eval sequencing
Regardless of which platform you choose, here’s the order to do things:
- Offline evaluation in CI/CD first — baseline three metrics (faithfulness, relevance, coherence), build a starting dataset of 50–100 inputs, set regression thresholds
- Online monitoring after the first production release — sample 5 to 10 per cent of traffic, alert on regressions
- Feed production failures back into the offline dataset — continuous improvement from there
Open-source (Arize Phoenix) gives you zero licensing cost in exchange for operational overhead. One engineer comfortable with containerised services makes self-hosting viable. Without that, managed platforms justify their per-trace cost fairly quickly. Either way, start with three to four well-calibrated metrics rather than trying to track everything at once — three good metrics beat ten poorly understood ones.
Once your evaluation tooling is running, connecting it to a governance framework is the next step. The approach is covered in the AI evaluation governance overview and the internal governance framework guide.
Frequently asked questions
Can we use open-source tools instead of paying for a platform?
Yes. Arize Phoenix is the most complete open-source option — tracing, evaluation, and dataset management at zero licensing cost. Braintrust has partial open-source components, but the managed platform is the primary product. The trade-off is zero licensing cost in exchange for hosting and maintenance responsibility. One engineer comfortable with containerised services makes self-hosting viable. Otherwise, managed platforms are the better choice.
How do we set regression thresholds for AI evaluation?
Establish baseline scores using two to three metrics (faithfulness, relevance, coherence). Set thresholds as percentage drops from baseline — block deployment if faithfulness drops more than 3 per cent, for example. Validate each threshold against at least 50 manually reviewed outputs before automating, and recheck when anything significant changes: prompts, underlying models, or content types.
What does LLM-as-a-judge cost at scale for a small team?
GPT-4 as judge at one million daily evaluations costs approximately $2,500 per day. For teams at 10K to 100K evaluations per month, costs are typically well under $100 per month with GPT-4o. Galileo Luna-2 is worth considering at higher volumes — approximately $0.02 per million tokens, making it practical to evaluate every trace rather than a sample.
Is Braintrust’s free tier enough for a small team getting started?
Yes, for teams of 5 to 10 engineers at early-stage volumes. The free tier (1M trace spans/month, 10K scores, unlimited users) covers core evaluation, dataset management, and CI/CD integration — it’s not a crippled demo. Upgrade to Pro ($249/month) as evaluation volume grows. Use the free tier to validate that evaluation workflows fit your development process before committing budget.
Which platform is best if we need SOC 2 or HIPAA compliance?
Arize AX has the strongest documented compliance posture — SOC 2 Type II, HIPAA support, ISO certifications, audit trails, and role-based access control. Fiddler targets regulated industries with in-environment evaluation but requires enterprise pricing negotiation. Arize Phoenix self-hosted in a compliant environment provides compliance through infrastructure control. Braintrust, Maxim, and Galileo don’t prominently position compliance certifications as differentiators.
How do I integrate AI evaluation into GitHub Actions or CI/CD?
Define your evaluation suite in code, run it on every pull request, compare results to a stored baseline, and fail the CI job if any metric drops below threshold. Braintrust has the most fully documented GitHub Actions integration — it posts evaluation results as pull request comments and blocks the merge on regression. Start with one metric and one evaluation dataset; expand as you learn what regressions look like in your system.
