How to choose AI evaluation software

01

Define what you need to evaluate

• Single prompts or model choices (prompt and model comparisons)
• RAG pipelines (retrieval relevance, faithfulness, answer quality)
• Multi-step agents (tool calls, handoffs, task completion)
• Safety and security (jailbreaks, prompt injection, PII leakage)
• Classic ML models alongside LLMs (drift and predictive quality)

Write down the three to five quality criteria that matter most and how you would judge them by hand. That becomes your first eval set.

02

Decide on offline, online, or both

• Offline evals: run a dataset through a new version before release, often in CI, and compare scores.
• Online evals: score live production traces as they arrive, then alert on regressions.
• Guardrails: block or rewrite outputs in real time. Check latency and cost per call.

Most teams start offline and add online scoring once traffic is real. Confirm the tool can turn production traces into new dataset rows.

03

Check how scoring works

Ask which scorers ship built in, whether you can write code scorers, and how LLM-as-a-judge prompts are versioned. Ask who pays for judge-model tokens: some plans include model credits, others bill your own provider keys. Plan to check judge scores against human labels on a sample before trusting them.

04

Choose a hosting model

• Hosted SaaS: fastest to start; traces leave your network.
• Bring-your-own-cloud or hybrid: the vendor runs the UI, your cloud stores the data.
• Self-hosted open source: you run everything, including upgrades and scaling.
• Enterprise self-hosted or on-prem: licensed, usually custom priced.

Check the license, not just the word “open”. MIT and Apache 2.0 are permissive. Elastic License 2.0 is source-available with restrictions. Some repositories keep enterprise features under a separate license.

05

Understand the pricing unit

Vendors price by seats, traces, spans, events, scores, data volume (GB), or a mix. The same workload can cost very different amounts depending on the unit. Estimate monthly trace and span volume, payload size, retention needs, and number of users, then price two or three tools against the same numbers. Check overage rates and retention add-ons.

06

Security and compliance evidence

Ask for current reports, not badges: SOC 2 Type II report, ISO/IEC 27001 certificate and scope, HIPAA BAA if you handle health data, GDPR DPA and data regions. Confirm which plan the certification covers; some vendors list compliance only on Enterprise plans. For self-hosted deployments, your own controls carry most of the weight.

07

Ownership and roadmap

This market has seen many acquisitions. Check who owns the tool now, which entity you contract with, whether the open-source project keeps its license, and whether the owner competes with you. Ask for written commitments on data handling and support terms.