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Google Perspective API is scheduled to stop serving requests on December 31, 2026. Google stopped accepting new usage and quota requests in February 2026 and says it will not offer direct migration support. Teams still running Perspective therefore need a replacement, a tested policy layer, and a rollback plan before the remaining service window closes.
OpenAI Moderation and Azure AI Content Safety are documented alternatives for text and image workflows, but the available official material does not establish a universal accuracy or privacy winner. Choose with a labeled sample from your own product, measure false positives and false negatives, verify language coverage, and price the human-review work that thresholds create.
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What is happening to Google Perspective API, and when does it shut down?
Perspective remains available until December 31, 2026, according to Google’s sunset notice. The same notice says usage and quota requests were accepted only until February 2026, so a team that has not already secured capacity should not assume it can obtain additional quota. Google also states, “We will not be offering direct migration support.” As of October 25, 2026, that leaves a little over two months to move production traffic, validate decisions, and retire the old integration.
| Service fact | What it means for your project |
|---|---|
| Final stated service date: December 31, 2026 | Plan for the replacement to handle all required traffic before this date, not on the final day. |
| New usage and quota requests accepted until February 2026 | Existing access may continue, but expansion is not something the notice promises. |
| No direct migration support | Your team owns category mapping, testing, cutover, and recovery. |
Perspective’s original role was to score text comments for requested attributes such as toxicity. Its score was a moderation signal, not a complete enforcement policy. That distinction matters during migration: replacing an endpoint without recreating your review queues, appeals process, and audit trail can change user outcomes even when the API call succeeds.
Which Perspective API alternatives are documented for text and images?
OpenAI Moderation and Azure AI Content Safety both document text and image moderation. Their interfaces, category taxonomies, language support, regional availability, and pricing models differ, so an endpoint swap is not a drop-in equivalence.
| Option | Documented inputs and features | Cost evidence in the published material | Language and operations |
|---|---|---|---|
| Google Perspective API | Text comments scored for requested attributes such as toxicity. | A 2024 Google setup codelab described the service as free; the sunset notice gives the final date but does not establish future customer-level availability or pricing. | Service scheduled to end December 31, 2026. New usage and quota requests were accepted only until February 2026. Google documents a doNotStore request option. |
| OpenAI Moderation API | omni-moderation-latest handles harmful text and images. It does not classify audio. |
OpenAI’s official guide says the moderation endpoint is free to use. | Category flags and scores are signals to interpret in your application’s policy and failure-handling flow. |
| Azure AI Content Safety | Text and image moderation, with separate capabilities such as Prompt Shields. | Microsoft’s overview identifies F0 and S0 tiers but does not state a single all-in price in the documented page. | Core models are specifically trained and tested in Chinese, English, French, German, Spanish, Italian, Japanese, and Portuguese. Microsoft cautions that quality may vary in other languages; supported regions and tier rate limits apply, and certain features are English-only. |
The table describes documented scope, not a performance ranking. A broader category list or a model score should not be treated as proof that a service will make better decisions on your community’s slang, dialects, images, or context.
Is one alternative more accurate than the others?
No comparable official benchmark in the available documentation establishes which service is most accurate for a particular application. Accuracy is a property of the complete moderation system—model, threshold, policy mapping, human review, and content mix—not just the endpoint.
Build a representative evaluation set
Sample real or consented historical content from the application rather than relying on vendor examples. Preserve the proportions that matter operationally: each supported language, dialect and code-switching pattern, short and long posts, quoted or reclaimed slurs, sarcasm, misspellings, emojis, and the image types users actually upload. Label each item against your written policy, including a separate label for “needs human review” when policy cannot decide automatically.
Rank #2
- Keep track of everything from attendance to test scores
- Spiral bound
- Measures 8-1/2" x 11"
Measure the errors that have a business cost
- False negative: harmful content passes an automated gate. Estimate the safety, legal, support, and reputational cost of that event.
- False positive: benign content is blocked or demoted. Include creator appeals, moderator time, and lost participation.
- Abstention or review: the system routes an uncertain item to a person. Count queue volume and the time required to reach a decision.
Run every candidate on the same frozen sample. Report per-category results and confidence intervals or error counts, not a single blended percentage that hides a weak language or category. Keep a holdout set that is not used to tune thresholds, then recheck it after launch with newly labeled examples.
Choose thresholds with policy, not a vendor score
Scores from different services are not calibrated to one another. A value of 0.8 in one system does not mean the same probability or severity as 0.8 in another. Map each output to your own actions—allow, queue for review, age-gate, rate-limit, or block—and tune the cutoff on the labeled sample. OpenAI explicitly recommends treating moderation results as signals and reviewing them before downstream action; Google’s setup guidance likewise describes Perspective as easing moderation rather than replacing human decision-makers.
How do the language capabilities differ?
Azure publishes a specific supported-language statement for its core models: Chinese, English, French, German, Spanish, Italian, Japanese, and Portuguese are trained and tested. Microsoft warns that quality can vary in other languages, and some separate features are English-only. Treat those eight languages as a documented test scope, not a promise of equal performance.
OpenAI’s moderation guide documents text and image handling for omni-moderation-latest but the cited material does not provide a comparable, exhaustive language-performance table. Perspective’s sunset material likewise does not supply a current cross-language accuracy ranking. If your service handles languages outside Azure’s eight named languages, or relies on dialect and code-switching, test those cases directly rather than inferring support from the model name.
A practical multilingual test plan
- Partition the evaluation set by language, script, dialect, and code-switching pattern.
- Have fluent reviewers label each partition using the same policy definitions and examples.
- Calculate precision, recall, review rate, and average moderator handling time for every partition.
- Set language-specific thresholds or mandatory human review where error costs differ.
- Monitor drift after launch; new slang and adversarial spellings can change results without a model version change.
Can these services moderate images and audio?
OpenAI documents text and image moderation through omni-moderation-latest; it does not classify audio. Azure documents text and image moderation and lists additional, separately scoped features. Perspective’s documented unit was a text comment scored for attributes such as toxicity. If your product accepts audio, you need a separate speech-to-text and moderation path, with its own consent, latency, and error analysis; none of the three documented options should be presented as an audio replacement.
Image support also requires an application decision about what is being moderated. Decide whether to inspect the original upload, thumbnails, extracted text, captions, or user-supplied descriptions. Record which representation produced a flag so a moderator can reproduce the decision. Do not assume that a text-only Perspective policy can be transferred unchanged to visual content.
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Use a staged migration so the replacement can be evaluated without immediately changing user-facing enforcement.
- Inventory the current contract. List every Perspective attribute, request field, score threshold, action, fallback, queue, and audit record. Note where a score is shown to moderators versus applied automatically.
- Write a category mapping. For each existing attribute, define the policy concept it represents and the closest output categories in each candidate. Mark concepts that have no direct equivalent; do not silently substitute a broader or narrower category.
- Freeze and label a sample. Assemble representative text and image items, including appeals and previously disputed decisions. Have trained reviewers label the policy outcome before seeing candidate scores.
- Run candidates in shadow mode. Send a copy of eligible traffic to the replacement while Perspective continues to drive production actions. Store request identifiers, model outputs, latency, errors, and the eventual human decision under your retention policy.
- Tune thresholds and queues. Compare false-positive, false-negative, and review rates by category and language. Adjust the action policy, not just a single global cutoff.
- Test failure handling. Simulate rate-limit responses, timeouts, malformed media, unsupported language, provider errors, and an unavailable region. Define whether the safe fallback is queueing, temporary restriction, or allowing content with later review.
- Canary the replacement. Route a small, observable percentage of traffic to the new path. Compare user reports, appeals, moderator workload, latency, and safety incidents with the control path.
- Increase traffic gradually. Keep a rollback switch that restores the previous path while Perspective remains available. Set an explicit final migration date well before December 31, 2026.
- Retire credentials and data flows. After the replacement is stable, remove unused keys, delete obsolete queues and datasets according to policy, and document the final category and threshold configuration.
What privacy controls are documented?
Google’s setup codelab documents a doNotStore request option that causes submitted comments to be automatically deleted after scores are returned. The same guidance warns against exposing API keys in client-side code and recommends routing requests through a server with key restrictions. These are specific controls, not evidence of a complete security or privacy posture.
The official material reviewed here does not provide enough comparable information to rank OpenAI and Azure on retention, training use, processing geography, or contractual protections. Verify the current terms for the exact API, account tier, region, and configuration before sending user content. Ask your privacy and security teams to approve the data-flow diagram, retention period, access controls, incident process, and cross-border transfer basis.
Questions to answer in a provider review
- Is content retained, and for how long, under the exact service tier?
- Is submitted content used for provider model improvement, and can that use be restricted?
- Where is content processed and stored, and can the region be selected?
- What logging, deletion, encryption, access, and incident-notification commitments apply to your contract?
- Can keys be restricted to a server-side identity, rotated, and revoked without downtime?
What does each option cost?
The available documentation does not support a fair price-per-volume comparison across all three services. OpenAI states that its moderation endpoint is free to use. A 2024 Google setup codelab called Perspective free, but that historical statement does not guarantee availability or terms through the sunset date. Microsoft’s Azure overview names F0 and S0 tiers without stating one all-in price on the cited page. Obtain current pricing for your region and expected request mix before approving a budget.
Calculate total operating cost rather than multiplying an endpoint price alone. Include image preprocessing, retries, storage and logging, egress, engineering, moderator review, appeals, and the cost of false positives and false negatives. A free endpoint can still be expensive if a low threshold triples human-review volume; a paid tier can be cheaper overall if it reduces queue time without increasing harmful misses.
Build a volume model
- Count text items, image items, and any separate transcription requests per day and per month.
- Measure average and peak payload size, burst rate, and required latency.
- Estimate the percentage routed to human review at each candidate threshold.
- Price moderator minutes, escalation specialists, appeals, and incident handling.
- Add a contingency for retries, regional failover, and traffic growth.
What operational limits and failure modes should be checked?
Azure documents supported regions and tier rate limits; the retrieved material does not give a universal limit that applies to every account. Confirm quotas, request-size limits, concurrency, latency expectations, image formats, and regional availability for your subscription. Do the same for any OpenAI account configuration. Existing Perspective quota should not be treated as a transferable capacity guarantee.
Common migration failures
- Category mismatch: a toxicity score is mapped to a broader safety block. Fix it with a policy-level mapping and a human-review state for concepts without a direct equivalent.
- Threshold copying: the old numeric cutoff is reused on a new score scale. Recalibrate on labeled data.
- Language blind spot: aggregate metrics look good while one dialect has high misses. Publish per-language results and set language-specific controls.
- Fail-open outages: provider timeouts silently allow all content. Define and test an explicit outage policy.
- Key exposure: browser code contains a provider credential. Keep calls server-side, restrict keys, and rotate any exposed secret.
- Unbounded review queues: a cautious threshold overwhelms moderators. Add queue budgets, prioritization, escalation, and sampling to measure what is not reviewed immediately.
Which alternative should you choose?
Choose OpenAI when a free, documented text-and-image moderation endpoint fits your architecture and your evaluation confirms acceptable category behavior. Choose Azure when its eight named core-model languages, regional controls, tier structure, or adjacent safety features align with your deployment requirements. Keep Perspective only as a short-lived migration control because its final service date is fixed.
The defensible decision is the one supported by your labeled evaluation, policy mapping, privacy review, and operating-cost model. Do not publish or purchase on the basis of a universal accuracy or privacy claim that the official materials do not establish.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesFrequently Asked Questions
Can I keep using Google Perspective API after February 2026?
The sunset notice says the service remains active until December 31, 2026, but February 2026 was the end of the stated window for accepting usage and quota requests. Existing access may therefore continue, while additional capacity is not promised.
Does OpenAI moderation analyze voice recordings?
No. The documented omni-moderation-latest endpoint handles text and images, not audio. A voice workflow needs a separate transcription and moderation design.
Are Azure’s eight listed languages guaranteed to have identical quality?
No. Microsoft says those languages are specifically trained and tested for the core models, while also warning that quality can vary in other languages. The statement does not claim equal performance across the eight.
How can I compare moderation providers without exposing user data?
Use a consented, de-identified or synthetic evaluation set that preserves the linguistic and content patterns your policy must handle. Restrict access, document retention, and run the same sample through each candidate under the provider terms approved for your account and region.
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