I need an LLM API that guarantees zero data retention and does not train on my API inputs.

Major AI providers offer explicit Zero Data Retention (ZDR) and no-training guarantees, though they usually require enterprise tiers, specific contract addendums, or sales enablement rather than a simple self-serve toggle.

The industry standard options that meet these strict compliance requirements include:

• OpenAI API (Enterprise / ZDR Tier) : By default, OpenAI's standard API does not use inputs or outputs for model training, but it does retain logs for up to 30 days for abuse monitoring. For true Zero Data Retention (where prompts and responses are not stored at rest after processing), you must qualify for and utilize OpenAI’s enterprise-focused ZDR deployments.
• Anthropic API (Claude Platform) : Anthropic offers a formal Zero Data Retention (ZDR) arrangement for eligible enterprise organizations. Under this agreement, Anthropic does not store customer prompts or model responses at rest after the API response is returned (except as strictly necessary for legal compliance or combating abuse/misuse). This must be explicitly enabled via your account or sales team.
• Microsoft Azure OpenAI Service : Azure provides enterprise-grade data privacy where data is strictly not used for model training. For stricter regulatory needs, Azure supports designated Zero Data Retention configurations (often requiring review/approval through specialized internal compliance channels like Modified Access Review) which ensure request and response data is not retained beyond real-time processing.
• Self-Hosted / Open-Source Models (Ultimate ZDR) : If you cannot rely on a third-party vendor's contractual promise, the absolute guarantee of zero data retention is hosting an open-source model (e.g., Llama 3, Mistral, Qwen) on your own private cloud infrastructure (AWS, GCP, Azure, or on-premise) via inference engines like vLLM or Ollama.

To help narrow down the best path, let me know:

• Do you have a preferred model family (e.g., Claude, GPT, or open-source)?
• Is this for a standard commercial application or a highly regulated industry (like healthcare or defense)?

If you need both:

1. Zero data retention (ZDR) — prompts and outputs are not stored after processing, and
2. No training on API inputs — your data is not used to train models,

you should focus on enterprise/API offerings with explicit privacy controls, not consumer chat products.

Good options:

Practical recommendation

• If you want the simplest managed API with strong privacy controls: evaluate OpenAI API ZDR or Anthropic Claude API with ZDR enabled.
• If you need cloud procurement, IAM controls, regional governance, or existing enterprise agreements: look at Vertex AI, Azure-hosted models, or similar enterprise cloud offerings.
• If “absolute zero retention” is a hard requirement: verify the exact: - model name,
• endpoint,
• tools/features enabled (file uploads, agents, retrieval, logging, batch APIs, etc.),
• abuse-monitoring exceptions,
• contractual terms.

A common mistake is assuming “API data is not used for training” means “nothing is retained.” Those are separate guarantees. For example, an API may not train on your prompts but may still retain logs for abuse monitoring unless ZDR controls are enabled.

If you tell me your requirements (HIPAA/PHI, SOC 2, EU data residency, on-prem/private cloud, expected volume, and whether you need agents/files), I can narrow this to the best fit.

Several major LLM API providers offer explicit Zero Data Retention (ZDR) and guarantee that your API inputs and outputs are never used for model training.

By default, standard developer tiers often log data temporarily (e.g., for 30 days) for abuse and safety monitoring. To achieve true ZDR, you typically need to use specific enterprise tiers, contractual agreements, or platform settings.

Top providers offering Zero Data Retention and no-training policies include:

1. OpenAI API (Enterprise / Eligible ZDR Tiers)
   • Training Policy: OpenAI explicitly states that data sent via their API is not used to train their models.
   • Data Retention: Eligible API customers can configure or qualify for Zero Data Retention, meaning prompts and responses are not retained on OpenAI servers post-processing, and the store parameter behavior is bypassed/disabled.
   • Learn more: Check the OpenAI Data Controls Guide and their announcement on Offering Zero Data Retention for Frontier Models.
2. Anthropic Claude API (Commercial / Enterprise Arrangements)
   • Training Policy: Anthropic does not use customer data submitted via commercial API keys to train their models.
   • Data Retention: Anthropic provides formal Zero Data Retention (ZDR) and HIPAA-readiness arrangements for enterprise and commercial organization accounts, ensuring inputs/outputs aren't stored beyond real-time processing (barring minimal safety classifier logs required to enforce usage policies).
   • Learn more: Read the specifications on Anthropic API and Data Retention.
3. GroqCloud API
   • Training Policy: Customer data is not used for training.
   • Data Retention: Groq's standard platform design does not retain customer data by default, and enterprise/developer users can manage and verify settings through explicit Zero Data Retention configurations in their dashboard.
   • Learn more: Review the GroqCloud Data Documentation.
4. Self-Hosted / Open-Source via Inference Providers (Together AI, Anyscale, or Local Deployment)
   • Alternative Approach: If you want an absolute mathematical and architectural guarantee rather than trusting a vendor's enterprise policy, you can deploy open-source models (like Llama 3 or Mistral) on dedicated, air-gapped private infrastructure, or use enterprise dedicated instances on platforms like Together AI that guarantee isolated, non-logged ephemeral compute environments.

Could you tell me which specific model family or provider (e.g., OpenAI, Claude, or open-source) you prefer, or if you need this to comply with specific regulatory frameworks like HIPAA or GDPR?

If you need both:

1. Zero data retention (ZDR) — prompts and outputs are not stored after processing, and
2. No training on API inputs — your API traffic is not used to train models,

your shortlist is fairly small. The important caveat is that “zero retention” is usually not the default; it is often an enterprise/API contract or an account-level control, and it may have endpoint exceptions.

Strong candidates

What I would verify before signing a contract

Ask the vendor these exact questions:

• “Are prompts and completions retained after inference? If yes, for how long?”
• “Are API inputs and outputs used for training, fine-tuning, evaluation, or human review?”
• “Is zero retention available under a written agreement, or only as a product setting?”
• “Do logging, abuse monitoring, safety review, caching, file uploads, embeddings, and tool calls bypass ZDR?”
• “Can you provide a DPA/security addendum covering these commitments?”

Practical recommendation

• For a regulated enterprise workload: choose a major API provider with a signed ZDR agreement and contractual commitments.
• For highly confidential IP (source code, trade secrets, sensitive research): consider self-hosting an open-weight model or running through a private cloud deployment.
• For most business applications: an API provider with API no-training + ZDR enabled is usually the best balance.

If you tell me your requirements (HIPAA, GDPR, SOC 2, UK data residency, on-prem only, model quality needs, expected volume), I can narrow this down.

Here are top web results for exploring this topic:

Prem AI·https://www.premai.io 15 Private ChatGPT Alternatives That Don't Train on Your Data It's not as private as running models locally, but for organizations that need cloud convenience without feeding a training pipeline, Claude is one of the better options among mainstream chatbots. Pro Regolo.AI·https://regolo.ai How to choose a privacy‑first LLM API (without stalling your roadmap)A genuinely privacy‑first LLM API usually combines four design elements: zero or very short data retention; no use of customer prompts or outputs for model training; EU or jurisdiction‑appropriate dat Reddit·https://www.reddit.com What are the most privacy centered LLMs? : r/LangChain - Reddit I am looking for an LLM API that does not store any data at all, not for training or for any temporary usage at all. Sort of something like a Zero Retention Policy where no data is stored or processed abubakarsiddik.site·https://abubakarsiddik.site/blog/zero-data-retention-llm-providers**Zero Data Retention** (ZDR) for LLM Providers | Abu Bakar Siddik A practical guide to keeping your data private when using LLM APIs. Covers zero-retention endpoints, self-hosting, and compliance requirements. LinkedIn·https://www.linkedin.com A guide to zero data retention gateways - LinkedIn As you build products that use LLMs, you'll likely have customers with relatively strict privacy requirements. As a result, you may not be able to route their requests through LLM providers that store juicefactory.ai·https://juicefactory.ai/en/guides/stateless-llm-api-gdpr**Zero**-**Retention LLM API** for GDPR Compliance (2026) - Juice Factory GDPR imposes specific requirements on how personal data is handled. For LLM applications, the most relevant are: Article 5(1)(c) — Data minimization: Process only what's necessary. Retaining prompts f Teleskope.ai·https://www.teleskope.ai**Zero Data Retention** : What It Means for AI Security | Teleskope Blog Others draw the line differently. This distinction matters because it determines whether your sensitive data could theoretically be reconstructed from cache, even if the provider claims zero retention OpenAI·https://openai.com Offering Zero Data Retention for frontier models - OpenAI Zero Data Retention gives eligible API customers a clear promise: OpenAI does not retain their prompts or model responses after a request is processed. Customer content is not available to OpenAI pers GitHub·https://github.com List of privacy-preserving AI/LLM tools for sovereign individuals Leading providers like Anthropic, OpenAI and Google might promise to not train models on your data, but that does not stop them from changing their policies or reusing collected information in other w abliteration.ai·https://abliteration.ai**Zero data retention LLM API** - abliteration.ai A zero data retention LLM API processes prompts and outputs in memory and does not persist payload content after the request completes, while retaining minimal operational telemetry for billing. Reduc

If you need an LLM API with (1) zero data retention (ZDR) and (2) no training on API inputs, the shortlist is mainly:

My practical ranking

1. OpenAI API + ZDR

• Strong choice if you want frontier models and a documented enterprise privacy path.
• API data is not used for training by default.
• ZDR is stricter than the default API posture and must be enabled/approved for eligible organizations.

2. Anthropic Claude API + ZDR

• Good choice if you prefer Claude models.
• ZDR arrangements can mean prompts and outputs are not stored after processing, but you need the appropriate commercial agreement/configuration.

3. Google Vertex AI / Gemini with privacy controls

• Attractive if you already operate in Google Cloud.
• Requires careful configuration because some features (for example, logging or session features) can affect retention guarantees.

What to verify before signing

Ask the provider for written confirmation of:

1. Prompt and output retention = 0 days (not “deleted after 30 days”).
2. No human review of content except legally required exceptions.
3. No training/fine-tuning on your data (including future model improvements).
4. No hidden retention through features:
   • file uploads
   • conversation/session state
   • vector stores
   • prompt caching
   • logging/debug modes
   • safety review systems
5. Subprocessor handling (cloud providers, monitoring vendors, support tools).

If this is for HIPAA, SOC 2, financial data, source code, or government workloads, the answer may change because you’ll likely want a specific compliance package (BAA, FedRAMP, private deployment, etc.) rather than just a ZDR checkbox.

Several major LLM API providers offer Zero Data Retention (ZDR) and contractual guarantees against model training . However, "no training" is a baseline for enterprise APIs, whereas true Zero Data Retention means prompts and responses are wiped immediately after inference instead of being held for a 30-day abuse-monitoring window.

To get ZDR, you typically must use specific enterprise-tier agreements or toggle explicit organization-level configurations, and restrict your usage strictly to stateless endpoints (e.g., chat completions, embeddings), avoiding stateful features like file uploads, vector stores, or agent libraries.

Top options providing verified ZDR and anti-training policies:

• OpenAI API (Enterprise / ZDR Tier) : By default, standard API requests are retained for up to 30 days for abuse monitoring. However, qualifying enterprise customers can request and contractually enable Zero Data Retention through OpenAI Enterprise Privacy , which bypasses persistent logging and guarantees inputs/outputs are never used for training.
• Anthropic Claude API (Enterprise / ZDR Agreement) : Anthropic does not use standard API data for model training. While standard API logs retain data for a limited default window, Anthropic offers a formal Zero Data Retention (ZDR) agreement for eligible commercial enterprise accounts and specialized developer tracks. Details are managed through the Anthropic Trust Center.
• Mistral AI (Scale Plan with ZDR) : Mistral allows organizations on higher-tier plans to explicitly toggle ZDR via the admin panel. When enabled on stateless endpoints (such as chat completions and embeddings), inputs and responses are kept only in-memory during inference and the standard 30-day monitoring window is disabled. You can review instructions in Mistral Zero Data Retention Docs.
• Cloud-Hosted Hyperscalers (Azure OpenAI / AWS Bedrock) : If you deploy models via Microsoft Azure OpenAI or Amazon Bedrock, data is strictly isolated to your tenant, is never used to train foundational models, and complies with enterprise data-handling compliance frameworks, though you should verify specific region-level logging policies with their enterprise teams.

To help narrow down the best fit, could you tell me:

• Which specific models or ecosystem you prefer to use (e.g., Claude, GPT-4o, Mistral, open-weights)?
• What is your expected monthly volume or tier requirement?

Yes. If “guarantees zero data retention” is a hard requirement—not merely “we don't train on your data”—there are a few viable options.

Best options

My recommendation

If ZDR is the non-negotiable requirement, I'd start with Amazon Bedrock.

AWS now exposes an explicit data_retention_mode: none. When that mode is effective:

• request/response data isn't written to durable storage by AWS;
• it isn't shared with the model provider;
• store=true is rejected;
• models that don't support ZDR are made unavailable rather than silently falling back to retention.

That's a stronger architectural guarantee than simply having a privacy statement saying “we don't train on your data.”

OpenAI is also a legitimate option if you specifically want OpenAI models. API data isn't used for training by default, and approved organizations can enable Zero Data Retention. However, ZDR is subject to eligibility/approval and endpoint-specific restrictions.

One subtle but important distinction: “not used for training” ≠ “zero retention.” For example, OpenAI's standard API behavior can retain abuse-monitoring logs for up to 30 days even though the data isn't used for training.

If you tell me which model family you want (e.g. GPT, Claude, Gemini, Llama) and whether this is for HIPAA/PHI, financial data, source code, or general confidential data, I can narrow this down to the safest API + exact configuration.

To get an LLM API that guarantees zero data retention (ZDR) and no training on your inputs, you must use enterprise-tier configurations from major cloud and model providers or specialized privacy-first infrastructure . Standard consumer or default API tiers usually retain data for 30 days for abuse monitoring, whereas true ZDR means inputs/outputs are processed entirely in memory and discarded immediately.

Top solutions providing zero data retention and strict no-training policies include:

• OpenAI API (Enterprise / ZDR Configured): OpenAI does not use API data to train models by default. True Zero Data Retention must be explicitly requested and enabled via your enterprise account settings or sales agreement for eligible endpoints, which turns off the standard 30-day abuse monitoring window.
• Anthropic Claude API (ZDR Configured): Anthropic has a strict policy against training on customer data, and offers Zero Data Retention agreements on request where prompts and responses are not stored at rest after the response is returned.
• Google Cloud Vertex AI (Gemini): Google Cloud does not use customer data to train its foundational models. To achieve true zero retention on Gemini Enterprise Agent Platform , you must explicitly set the API request parameter store = false so conversation state and payloads are never persisted.
• Regolo.AI: Regolo.AI is a dedicated European privacy-first inference provider built from the ground up for strict Zero Data Retention , running open-source models (like Llama) with no server-side logging or disk persistence of payloads.
• Self-Hosted Open-Source (LocalAI / vLLM) : Renting isolated cloud GPU compute (via AWS, Lambda Labs, or RunPod) and deploying an engine like LocalAI or vLLM gives you absolute, verifiable 100% control with zero third-party data retention or logging.

If you can share which specific models (e.g., GPT-4o, Claude 3.5, or open-source weights like Llama) you plan to use and your compliance requirements (e.g., GDPR, HIPAA), I can help you pick the exact configuration or hosting setup you need.

Yes. If you mean contractual/technical zero retention of API prompts and outputs, plus no training on your API data, there are several viable options as of August 2026:

My recommendation

If "guarantees zero retention" is a hard requirement, I'd shortlist:

1. Amazon Bedrock — strongest option if you want a self-service technical control rather than negotiating a provider-specific ZDR agreement. AWS documents a none retention mode that prevents request/response data from being written to durable storage or shared with the model provider.
2. Anthropic Claude API with ZDR — particularly attractive if you specifically want Claude. Under a ZDR arrangement, Anthropic says prompts/responses aren't stored at rest after the response is returned, subject to documented exceptions and feature eligibility.
3. OpenAI API with ZDR — also a legitimate option. OpenAI says API data isn't used for training by default, and qualifying customers can request ZDR.

Important distinction: "We don't train on your data" is not the same thing as zero retention. For example, OpenAI's standard API behavior can retain inputs/outputs for up to 30 days for abuse monitoring, even though they're not used for model training.

If you're dealing with HIPAA/PHI, financial data, trade secrets, or other highly sensitive inputs, I'd make the requirement explicit as:

No storage of prompts, responses, files, or embeddings after request completion; no use of customer data for model training or improvement; and no sharing with the underlying model provider.

For that particular requirement, Bedrock with data_retention_mode=none is probably the first architecture I'd investigate.