Choosing an LLM observability platform is not just a matter of comparing dashboards.

One vendor may give you detailed traces. Another may be stronger at evaluations. A third may have better integrations with your existing stack. The problem appears when the demo looks good but the platform cannot answer the questions your engineering, security, procurement, and finance teams will ask after implementation.

That is why an LLM observability RFP should go beyond feature checklists.

You need to know how the platform captures LLM calls, retrieval steps, tool use, latency, token usage, and failures. You also need to understand how it handles sensitive data, integrates with your existing systems, supports evaluations, and fits your deployment and budget requirements.

OpenTelemetry’s current GenAI semantic conventions include attributes for providers, models, token usage, operations, tool calls, retrieval, and other GenAI telemetry. That gives buyers a useful baseline for asking vendors exactly what their platform can capture rather than accepting vague claims about “AI visibility.”

This LLM observability RFP template gives you 47 questions to use when evaluating vendors, plus a practical scoring rubric.

How to Run the Evaluation

Do not send the same questionnaire to every vendor and simply count the number of “yes” answers.

Start by defining what your team actually needs the platform to observe. Understanding where observability fits within the broader AI stack can also help you identify which parts of your system need monitoring. For example, a production RAG application may require visibility into retrieval, model calls, prompts, token usage, latency, and evaluation scores. An agentic application may additionally require tracing across tools and multi-step workflows.

Modern observability platforms increasingly connect traces with evaluations. Phoenix, for example, describes tracing as a way to inspect execution while evaluations add quality signals to those runs. Braintrust similarly connects experiments, traces, and evaluation results.

Before sending an RFP, decide which capabilities are mandatory, which are preferred, and which are irrelevant to your use case.

Questions 1-5: Evaluation Process

1. What specific LLM applications and use cases does the platform need to support?

Ask vendors to respond against your actual workloads rather than giving a generic product overview.

2. Which capabilities are mandatory for the first production deployment, and which can be added later?

This prevents a vendor from scoring well because of features that do not matter to your initial rollout.

3. Can the vendor demonstrate the platform using a workflow similar to ours?

A real RAG, agent, chatbot, or multi-step workflow is more useful than a curated demo.

4. What evidence will the vendor provide for each major capability?

Ask for documentation, screenshots, technical specifications, trial access, or a proof of concept where appropriate.

5. How should we evaluate a capability that is on the vendor roadmap but not currently available?

For procurement purposes, consider separating available functionality from future commitments rather than giving roadmap features full credit.

Architecture & Integration Questions

LLM observability becomes useful when it can follow a request across the entire application.

A single model response may depend on retrieval, embeddings, prompt construction, several tool calls, database queries, and downstream processing. This becomes even more important in AI agents, where a single workflow can involve multiple tools, decisions, and actions. A platform that only records the final LLM response can leave the most important part of the debugging process invisible.

Tracing platforms commonly represent this execution as traces and spans. Phoenix, for example, describes traces that cover model calls, retrieval, tool use, and custom application logic. Braintrust similarly represents LLM calls, retrieval, functions, tools, and scoring as spans within traces.

Questions 6-15: Architecture and Integration

6. What LLM providers, models, and model gateways does the platform support?

Ask whether support is native, community-maintained, or dependent on custom instrumentation.

7. Does the platform support OpenTelemetry or another open telemetry standard?

OpenTelemetry can provide a useful interoperability layer when your stack changes over time.

8. What instrumentation methods are available for our programming languages and frameworks?

Ask specifically about your languages, SDKs, orchestration frameworks, and application architecture.

9. What does a complete trace contain?

Ask vendors to demonstrate whether a trace can show model calls, prompts, responses, retrieval, tool calls, metadata, errors, latency, and token usage.

10. Can traces correlate multiple LLM calls and downstream operations under one request or session?

This matters for multi-step agents and RAG systems where a failure may originate several operations before the final response.

11. How are token usage and latency captured?

Ask whether the platform captures input tokens, output tokens, cached tokens where available, time to first token, total latency, and other relevant performance data. OpenTelemetry’s GenAI conventions include model, provider, token, and time-to-first-chunk attributes as part of its evolving GenAI telemetry model.

12. What integrations are available with our existing observability, logging, data, and workflow tools?

Ask about integrations with systems you already operate, not just the vendor’s complete integration list.

13. Does the platform provide APIs, SDKs, webhooks, or export options for custom integrations?

A closed platform can become difficult to operate if your engineering team needs data outside the vendor interface.

14. How does the platform handle failures, retries, rate limits, and partial traces?

Ask the vendor to show what happens when a model provider or downstream tool fails halfway through a workflow.

15. What deployment models are supported?

Clarify SaaS, private cloud, self-hosted, hybrid, or customer-managed options and ask what functionality changes between deployment models.

If self-hosting is part of your evaluation, compare the operational trade-offs around data control, infrastructure, and maintenance before treating it as a simple deployment preference.

Security & Compliance Questions

Security questions should be specific enough to expose how the platform actually handles AI telemetry.

This matters because observability data can contain prompts, model outputs, retrieved documents, tool arguments, user identifiers, and other sensitive information. OpenTelemetry explicitly warns that GenAI input and output message attributes may contain sensitive or personally identifiable information.

Do not stop at “Are you SOC 2 compliant?” Ask what is covered, which deployment model the certification applies to, how data is protected, and what evidence the vendor can provide.

Questions 16-24: Security and Compliance

16. What customer data does the platform collect and store?

Ask for a field-level description of telemetry, including prompts, outputs, metadata, traces, evaluations, and user information.

17. Are prompts and model outputs stored by default?

If yes, ask whether customers can disable, redact, truncate, or selectively capture them.

18. How is sensitive information such as PII detected, masked, or removed?

Ask whether redaction happens before data reaches the platform or only after ingestion.

19. How is customer data encrypted in transit and at rest?

Request the actual encryption controls and key-management approach rather than accepting “enterprise-grade encryption.”

20. How is tenant isolation implemented?

Ask how customer data, traces, evaluations, credentials, and administrative access are separated between tenants.

21. What identity and access controls are supported?

Ask about SSO, SAML, SCIM, RBAC, MFA, service accounts, API keys, and granular project or team permissions where relevant.

22. What audit logs are available, and how long are they retained?

Ask whether administrative actions, authentication events, configuration changes, and data-access events can be audited.

23. Which security and compliance certifications or attestations apply to the exact service and deployment model we would purchase?

Request supporting documentation and scope details. Do not assume that a certification for one product or deployment option covers another.

24. Where is customer data processed and stored, and what data-residency options are available?

This question becomes particularly important for organizations with regional processing or regulatory requirements.

Eval Capabilities Questions

Observability tells you what happened. Evaluation helps determine whether what happened was good enough.

That distinction is important. A trace can show that an LLM returned a response successfully, but a successful request does not prove that the answer was accurate, relevant, safe, or grounded. Phoenix’s documentation makes the same distinction between traces and evaluations, with evaluations providing scores or labels that can be compared across runs.

A strong evaluation workflow should also help you move from a production failure to a reusable test case. Braintrust, for example, describes datasets, tasks, scorers, experiments, and production traces as connected parts of its evaluation workflow.

Questions 25-33: Evaluation Capabilities

25. What types of evaluations does the platform support?

Ask about code-based evaluators, deterministic checks, LLM-as-a-judge evaluators, human feedback, and custom scoring.

26. Can we create custom evaluation criteria and scoring rubrics?

Your team may need domain-specific measures that are not available as built-in metrics.

27. Can evaluations run against datasets, traces, production traffic, or all three?

The ability to evaluate real production traces can be valuable when creating test cases from actual failures.

28. Can we compare prompts, models, and application versions using the same evaluation dataset?

Controlled comparison is important when deciding whether a change genuinely improves quality.

29. Can evaluation results be tracked over time to identify regressions?

Ask whether previous experiment results are preserved and comparable. Braintrust, for example, describes experiments as immutable snapshots that can be compared over time.

30. Can evaluations run as part of CI/CD?

If your team wants quality gates before deployment, ask whether evaluations can run automatically during pull requests, builds, or release workflows.

31. Can human feedback be attached to traces and evaluation results?

Human review can provide valuable signals for cases that automated evaluators cannot reliably judge. Phoenix supports annotations that attach labels or scores to collected entities such as spans.

32. How are evaluation results connected back to the underlying trace?

The useful question is not just whether a vendor gives you a score. You want to know why a test failed and which part of the workflow contributed to the failure.

33. Can production failures be converted into evaluation datasets or regression tests?

This closes the loop between observability and evaluation. A failure should become something the team can reproduce and test rather than an incident that disappears into a dashboard.

Cost & Contract Questions

Pricing an LLM observability platform is rarely as simple as comparing subscription tiers.

Your actual cost may depend on telemetry volume, trace retention, seats, evaluation runs, data storage, ingestion, premium integrations, or enterprise features.

Ask vendors to model pricing against your expected workload.

Questions 34-40: Cost and Contract

34. What pricing metric does the platform use?

Clarify whether pricing is based on seats, events, traces, tokens, telemetry volume, evaluation runs, usage, or another metric.

35. What is included in the base plan, and which capabilities cost extra?

Ask specifically about retention, evaluations, advanced security, integrations, API access, and enterprise controls.

36. Are there usage limits, overage charges, or rate limits?

Request examples showing what happens when usage exceeds the contracted amount.

37. How does pricing change as trace volume and application usage grow?

Ask the vendor to model your expected usage at current, 2x, and 5x scale.

38. What costs should we expect outside the platform subscription?

Consider implementation, instrumentation, storage, cloud infrastructure, support, custom integrations, and internal engineering time.

39. What contract terms govern price increases, renewal, termination, and data export?

A low first-year price does not necessarily mean a low total cost of ownership.

40. Can we export our traces, evaluations, datasets, and other generated data if we leave the platform?

Data portability should be part of the initial evaluation, not an afterthought.

Support & Roadmap Questions

A technically strong platform can still create problems if your team cannot get help when instrumentation breaks, an integration changes, or a production issue requires vendor assistance.

Support also needs to be evaluated against your operating model. Ask who is responsible for upgrades, incident response, troubleshooting, and platform maintenance.

Questions 41-47: Support and Roadmap

41. What support channels are included with our plan?

Ask about email, chat, ticketing, dedicated support, and technical account management.

42. What are the support response and resolution targets for critical incidents?

Ask for the actual SLA rather than a general statement that “24/7 support” is available.

43. What implementation assistance is available?

Clarify whether the vendor provides onboarding, instrumentation help, architecture reviews, migration assistance, or professional services.

44. Who owns platform upgrades and breaking changes?

This is particularly important for SDKs, integrations, APIs, and telemetry schemas.

45. How frequently does the vendor release product and security updates?

Ask for recent release history rather than relying only on a future roadmap presentation.

46. What is the vendor’s roadmap for the capabilities we consider important?

Separate committed releases from exploratory or tentative roadmap items.

47. What happens if we need to migrate away from the platform?

Ask for data-export formats, API access, retention after termination, migration documentation, and any contractual restrictions.

How to Score LLM Observability Vendors

A scoring rubric prevents the RFP from becoming a collection of opinions.

Without predefined weighting, vendors can win by being excellent in areas that matter less to your team. A structured scoring model makes the trade-offs visible and gives engineering, security, procurement, and finance a common framework.

A useful starting point is:

Evaluation Area	Weight
Evaluation Process	10%
Architecture & Integration	20%
Security & Compliance	20%
Evaluation Capabilities	20%
Cost & Contract	15%
Support & Roadmap	15%
Total	100%

Score each question from 1 to 5:

Score	Meaning
1	Missing, unclear, or evasive
2	Partially supported or dependent on significant work
3	Meets the requirement with reasonable evidence
4	Strong fit with documented capability
5	Excellent fit, demonstrated in our environment or supported by strong evidence

Then apply the category weights.

For example, a vendor that scores 4 out of 5 for security receives 80% of the 20 points allocated to that category, giving it 16 points.

Do not let the total score override a critical failure. If a vendor cannot meet a mandatory security, data residency, integration, or deployment requirement, mark it as a no-go even if its overall score is high.

That approach is consistent with broader AI procurement guidance that recommends predefined weighting and evidence-based scoring rather than treating every “yes” answer equally.

Red Flags in an LLM Observability RFP Response

Some answers deserve a second look.

Watch for:

• “Enterprise-grade” without technical details
• A security certification without explaining its scope
• A roadmap feature presented as if it already exists
• An integration that actually requires custom development
• Pricing that excludes important usage components
• No clear explanation of data retention
• No way to export your data
• A trace that captures the final LLM response but not retrieval or tool execution
• Evaluation scores without access to the underlying test cases or traces
• A vendor that cannot demonstrate your actual workflow

Ask for evidence whenever a claim matters to the buying decision.

A current AI infrastructure RFP guide makes a similar recommendation: vendors should explain architecture, security boundaries, deployment dependencies, and operating requirements instead of relying on generic product claims.

Download the LLM Observability RFP Template

You do not need to build this questionnaire from scratch.

Use the downloadable LLM Observability RFP Template to send the 47 questions to vendors, record their responses, compare evidence, and score each platform using the evaluation rubric.

The template is most useful when you customize it before sending it out. Add your required integrations, deployment constraints, compliance requirements, expected telemetry volume, and evaluation workflows.

That turns a generic vendor questionnaire into an evaluation framework built around your environment.

FAQs

What should I ask an LLM observability vendor?

Ask about architecture, tracing depth, integrations, security, data retention, evaluation capabilities, pricing, deployment, support, and data portability. The most useful questions require the vendor to provide evidence rather than simply answer yes or no.

How do I score RFP responses?

Define category weights before reviewing vendor responses, then score each requirement using a consistent scale. A 1-to-5 system works well when each score has a clear meaning. Keep mandatory requirements separate from the weighted score so a critical security or architecture failure cannot be hidden by strong performance elsewhere.

What are the red flags in an LLM observability RFP?

Common red flags include vague security claims, undocumented integrations, roadmap capabilities presented as current features, unclear data retention, limited export options, incomplete tracing, and pricing that does not clearly explain how costs scale.

Can I reuse this for eval tools?

Yes. The architecture, security, integration, cost, support, and procurement sections can be reused for many AI evaluation platforms. For an evaluation-focused vendor, give additional weight to dataset management, evaluator flexibility, experiment tracking, regression testing, CI/CD integration, and production evaluation.

Why should I use an LLM observability RFP template?

A structured template makes vendors answer comparable questions. It also reduces the chance that a polished demo or long feature list hides gaps in security, integration, evaluation, pricing, or operational fit.

Final Takeaway

The best LLM observability platform is not necessarily the one with the longest feature list.

It is the one that gives your team enough visibility to understand what happened, enough evaluation capability to determine whether the result was good, and enough operational control to improve the system without creating a new dependency problem.

Use the 47 questions to evaluate the platform across the full buying process:

Architecture → Security → Observability → Evaluation → Cost → Support → Exit

Most importantly, ask vendors to prove their answers.

A trace demo can show what a platform looks like. A properly structured RFP tells you whether you can actually operate it.