A production AI API needs three controls working together: moderate the request before generation, moderate the response before a user sees it or a tool acts on it, and enforce rate limits independently of moderation. Send borderline or high-impact cases to trained reviewers with the full context they need. Log every policy decision so thresholds, appeals, and incidents can be audited.
The control loop your API should implement
Safety is not a single moderation call. Treat each request as a controlled workflow with explicit policy decisions:
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- Authenticate and identify the caller. Resolve the user, API key, project, organization, tenant, and IP address before spending model capacity.
- Apply request and token budgets. Reject or defer traffic that exceeds its allowance before sending it to the model.
- Moderate the input. Check text and supported images before generation. Use the result to block, transform, or escalate the request.
- Generate with constrained tools and context. Give the model only the tools, data, and output format required for the task.
- Moderate the output and tool arguments. Hold content until policy checks complete, then allow, redact, edit, block, or escalate it.
- Execute downstream actions only after authorization. A clean model response is not proof that a tool call is safe.
- Record the decision. Store policy and model versions, category scores, actions, and reviewer outcomes without retaining more sensitive data than necessary.
Keeping these stages separate makes failures diagnosable. A request can be within its quota but unsafe, or safe but abusive at a volume that harms other tenants. Your API should handle both conditions.
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Write a policy that names prohibited categories, restricted categories, high-impact actions, and the evidence required for an exception. Avoid a single “safe/unsafe” flag; different categories deserve different responses and review expertise.
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Example tier model
| Tier | Typical case | Default action |
|---|---|---|
| Low risk | Benign transformation, summarization, or classification with no sensitive action | Allow after input and output checks |
| Restricted | Content involving sensitive personal data, regulated advice, or a category near a policy threshold | Limit the response, require authentication, or route to review |
| High impact | Recommendations or actions that could affect health, safety, finances, employment, access, or legal status | Require a trained human review before use; prohibit autonomous execution |
| Disallowed | Content that violates a stated safety rule or attempts to bypass controls | Block, explain the applicable rule at a high level, and offer a safe alternative where appropriate |
Document who owns each decision, how a user can appeal, and how long records are retained. Policy versions must be immutable: when a threshold changes, you should be able to tell which version made every historical decision.
Choose actions, not just thresholds
A score is evidence for your policy, not the policy itself. Define deterministic actions for each category and risk tier:
- Block: stop generation or publication and return a neutral explanation.
- Redact: remove a defined class of sensitive material before delivery.
- Rephrase: ask the user to provide a safer or narrower request.
- Authenticate: require a verified account or elevated permission.
- Hold: place the request or response in a review queue.
- Allow with limits: return a constrained answer and disable tools or external actions.
Filter inputs before generation
Input moderation prevents unsafe requests from consuming model and tool capacity. OpenAI documents omni-moderation-latest for text and image inputs. The model does not classify audio, and an image file sent for moderation can be up to 20 MB. If your product accepts audio, design a separate transcription and safety path rather than implying that an image-and-text moderation call covers it.
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Request sequence
- Normalize the request for inspection while preserving the original payload for an audit record. Enforce maximum text, image, and metadata sizes before calling moderation.
- Send supported text and images to the moderation endpoint, or use inline moderation signals when your model integration provides them.
- Read the overall
flaggedresult and the individual category and score fields. - Map those fields to your policy version. A borderline score should usually be held or reviewed, not treated as an automatic violation.
- Only after the policy decision, construct the model prompt and attach permitted context or tools.
Keep the moderation request and the model request logically distinct. That lets you change a policy threshold without changing generation behavior and prevents an unmoderated fallback path from appearing during an outage.
Illustrative control flow
identity = authenticate(request)
quota = limiter.check(identity, request)
if quota.denied:
return rate_limit_response(quota.retry_after)
input_result = moderate_input(request.text, request.images)
decision = policy.evaluate(input_result, policy_version)
if decision.action in ["block", "rephrase", "authenticate"]:
return policy_response(decision)
if decision.action == "review":
return queue_for_review(request, input_result)
model_result = generate(request, approved_context, approved_tools)
output_result = moderate_output(model_result)
return apply_output_policy(output_result, model_result)
This is a control-flow example, not a drop-in SDK. Keep credentials server-side, validate every field returned by an external service, and fail closed for high-impact operations when a required safety check is unavailable.
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Moderate generated output before release or execution
Moderating only the user message is insufficient. Models can produce unsafe material because of ambiguous instructions, retrieved documents, tool results, or a compromised prompt. Check the generated text and any supported images before displaying them, saving them, sending them to another service, or using them to trigger an action.
Inspect the full result
- Use the overall
flaggedresult for fast routing. - Inspect category names and scores to select the correct policy branch and reviewer queue.
- Store the model identifier, moderation model identifier, policy version, and decision timestamp.
- Preserve the exact tool arguments and tool results that influenced the response.
OpenAI’s moderation guidance says to treat scores as signals for your application’s policy, not as an automatic blocking decision. Calibrate thresholds against your own categories, users, languages, and consequences. A low score is not authorization to perform an irreversible action, and a high score is not a substitute for a documented explanation and appeal process.
Use a release gate
Keep generated content in a pending state until output checks finish. The release gate should know whether the destination is a chat window, an email, a public post, a database, or a tool. The same text may be acceptable in a private draft but unacceptable as an automatic message to a customer. For high-impact destinations, require a human approval state even when moderation returns no flag.
Protect tools and downstream actions separately
Moderation can cover tool-call arguments and tool outputs when they appear in conversation content. It does not validate tool names, descriptions, schemas, or response-format schemas. Add an independent authorization layer:
- Use an allowlist of tool names for each route, tenant, and user role.
- Validate arguments against strict schemas and business rules, including ranges, ownership, and destination identifiers.
- Require a fresh authorization decision for destructive, financial, or external-communication actions.
- Use idempotency keys and dry-run modes where an operation can be repeated.
- Run untrusted parsing and retrieval in an isolated environment with zero tool access.
- Log the requested tool, validated arguments, authorization result, execution result, and reviewer decision.
Never let a model-created string choose an arbitrary endpoint, SQL statement, shell command, or credential. The model may propose an operation; application code must decide whether that operation is permitted.
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Design rate limits around abuse and fairness
Use separate budgets for requests and tokens. Depending on the workload, you may also need daily request and token limits, image-processing minutes, or audio-processing minutes. A request limit alone allows one very large prompt or completion to consume disproportionate capacity; a token limit alone can allow a flood of tiny requests.
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Enforce limits at the boundary that represents the risk:
- User: protects an account from a compromised session.
- API key: isolates applications and makes credentials accountable.
- Project or organization: controls shared budgets and billing exposure.
- Tenant: prevents one customer from exhausting a multi-tenant service.
- IP address: slows unauthenticated abuse, while recognizing that many legitimate users can share an address.
- Combined identity: catches attackers who rotate one identifier while keeping another constant.
Document which limit caused a denial so operators and clients can distinguish a per-user quota from an organization-wide outage. Reserve capacity for safety checks, review-queue processing, and administrative operations; otherwise a traffic spike can disable the very controls meant to handle it.
Token bucket or leaky bucket
A token bucket allows a defined burst while enforcing a long-term refill rate. A leaky bucket drains at a steady rate and smooths traffic. Either can work if you set a maximum burst, a sustained rate, and separate accounting for requests and tokens. Keep counters in a shared, strongly consistent store when requests can reach multiple application instances; local counters create uneven enforcement.
Estimate token cost before generation when possible, then reconcile with actual usage. Charge rejected or abusive requests according to your policy so attackers cannot bypass limits by repeatedly submitting requests that fail late in the pipeline. Make the accounting rules visible to legitimate clients through response headers or a documented status payload.
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Handle HTTP 429 responses without making an outage worse
When a dependency returns a temporary 429, first honor a valid Retry-After value. If it is missing or invalid, use bounded exponential backoff with jitter, cap both retry count and total retry time, and stop when the operation is no longer useful.
- Classify the 429 as retryable only when the operation is safe to repeat and the dependency indicates temporary throttling.
- Read and validate
Retry-After; use it as the next attempt delay when present. - Otherwise calculate an exponential delay with random jitter and an explicit upper bound.
- Use an idempotency key for operations that could create side effects.
- Return a clear retryable response to your caller when the cap is reached, rather than holding connections indefinitely.
- Check whether your official SDK already retries. Do not add a second retry loop around an SDK that is already backing off.
Repeated unsuccessful requests consume rate-limit capacity. A retry storm can therefore prolong an incident, so add circuit breaking and queue-based admission for non-urgent work.
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OpenAI’s safety guidance recommends human review of outputs wherever possible before they are used in practice. Human review is most valuable for borderline scores, high-severity categories, user appeals, and high-impact outputs that could cause material harm.
What to put in the queue
Each item should include severity, confidence or score evidence, policy category, customer or tenant, creation time, and a service-level target. Include the original input, generated output, relevant retrieved context, tool arguments and results, model and moderation versions, and the exact reason for escalation. Redact unrelated personal data and restrict queue access by role.
Give reviewers explicit outcomes
- Approve: release the original response or authorize the permitted action.
- Reject: block it and record the policy reason.
- Edit: make a documented change, retaining both the original and final content.
- Escalate: send ambiguous or unusually severe cases to a specialist.
Record reviewer decisions as structured data. Sample approved and rejected cases regularly, compare reviewer disagreement, and tune thresholds by category rather than moving one global cutoff. A review system also needs an appeals path: tell users that an action was restricted, provide a reason at an appropriate level, and allow a qualified reviewer to reconsider it.
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Plan staffing and failure modes
Define coverage hours, language and domain expertise, maximum queue age, and escalation contacts. If the queue exceeds its SLA, high-impact actions should remain paused rather than silently auto-approved. Provide reviewers with a safe preview mode so opening an item cannot execute a tool or load untrusted active content.
Resist prompt injection and abusive input
Attackers can use long inputs, nested instructions, encoded text, or retrieved documents to manipulate a model or exhaust capacity. Limit input and output sizes, narrow open-ended fields where possible, and ground answers in validated backend material. Pattern-based filters alone do not reliably catch indirect prompt injection.
- Separate system policy, user content, and retrieved content in your internal representation.
- Mark retrieved text as untrusted data and prevent it from granting permissions.
- Quarantine parsing of untrusted files or web content in a process with zero tool access.
- Require explicit server-side authorization for every tool call, regardless of model instructions.
- Rate-limit by user or IP and monitor sudden changes in length, error rate, tool attempts, or account rotation.
- Test multilingual, obfuscated, and multi-turn attacks against both input and output gates.
Logging, privacy, and auditability
A useful safety log answers who requested what, which model and policy evaluated it, what scores were returned, which action was taken, and who changed that action later. At minimum, capture:
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- request, user, tenant, API key, and correlation identifiers;
- model, moderation-model, and policy versions;
- category scores and the resulting action;
- rate-limit dimension, allowance, and retry information;
- tool authorization and execution records;
- reviewer identity, outcome, timestamp, and appeal result.
Protect logs as sensitive data. Encrypt them, restrict access, define retention periods, and separate operational metrics from raw content where possible. Hash or tokenize identifiers used only for aggregation. Never allow a logging failure to create an unmoderated release path; queue a safe failure state and alert operators.
Test the complete pipeline
Unit-test policy mappings and schema validation, then run end-to-end tests that exercise the real order of operations. Your test set should include:
- clean requests that must remain low latency;
- clear violations that should be blocked before generation;
- borderline scores that must enter review;
- unsafe generated content from an otherwise benign prompt;
- malformed images, oversized files, and unsupported audio;
- 429 responses with valid, missing, and invalid
Retry-Aftervalues; - duplicate retries of an action with side effects;
- tool names, arguments, and schemas that are not on the allowlist;
- prompt injection embedded in retrieved documents;
- appeals that reverse an initial decision.
Measure latency and throughput for each stage, but do not optimize away a control without documenting the risk. Review false positives and false negatives by category, language, tenant, and model version. There is no universal score threshold or accuracy figure that can replace this application-specific evaluation.
A practical implementation sequence
- Write the policy. Name categories, tiers, actions, reviewers, retention, and appeals.
- Build identity and quotas. Add per-user or key limits plus project, tenant, and IP protections where needed.
- Add input moderation. Reject unsupported media, enforce the 20 MB image ceiling, and route scores through a versioned policy.
- Constrain generation. Pass only approved context and allowlisted tools.
- Add the output gate. Moderate text and supported images, then hold content until the release decision is complete.
- Implement review queues. Include full context, SLA, structured outcomes, and an appeals route.
- Harden retries. Honor
Retry-After, use bounded jittered backoff, and remove duplicate SDK retry loops. - Instrument and test. Log decisions, versions, scores, and reviewer outcomes; run adversarial and failure-mode tests before launch.
The OpenAI Moderation API is documented as free for OpenAI API users and excluded from monthly usage limits (OpenAI Help Center, 2026). OpenAI’s current documentation lists omni-moderation-latest and the omni-moderation-2024-09-26 snapshot. Treat those identifiers as configuration, record which one handled each request, and review model or policy changes before promoting them.
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