DeepRails
DeepRails is an API that checks every large language model output against six quality guardrails, then automatically rewrites the ones that fail. It sits between any model and your users, adding correction rather than detection alone.
What is DeepRails?
DeepRails is a reliability layer that sits between a large language model and the people who read its answers. It does not generate content of its own: it inspects what another model has produced and decides whether that answer is good enough to ship.
The product comes in three surfaces. Monitor evaluates outputs and returns scores, flags and written explanations, tracking drift, latency, token usage and cost over time. Defend goes a step further: when an output falls below your thresholds it repairs it, with FixIt reworking the existing answer from the failure analysis or ReGen writing a fresh one, then re-scores the result before handing it back, streaming over Server-Sent Events for latency-sensitive endpoints. The Playground is a no-code sandbox for trying both, and it is the only thing the free plan includes.
Six built-in metrics do the scoring: Correctness, Completeness, Instruction Adherence, Context Adherence, Ground Truth Adherence and Comprehensive Safety, the last of which looks for PII, hate speech, self-harm content, prompt injection and CBRN material. Scores are continuous on a 0-100 scale. Pro and Enterprise customers can add custom metrics, either by registering their own evaluation prompt or by commissioning one from DeepRails engineers with a stated accuracy guarantee above 99.5%. Two further metrics, Agentic Performance and Tool Call Correctness, are marked as coming soon.
Configuration is deliberately granular. Six run modes trade accuracy against compute cost, from Super Fast up to Precision Max Codex with dual-model consensus. Three tolerance levels, with thresholds that either self-calibrate after about 25 runs or are pinned by hand, decide what counts as a failure, and up to 100 improvement cycles can run on a single output. A workflow is defined once and reused across Defend and Monitor, production and staging.
The company behind it is DeepRails, Inc., a Delaware corporation. It publishes an in-house benchmark against AWS Bedrock Guardrails claiming margins of 45% on correctness, 53% on completeness and 51% on safety, with the raw data in a public spreadsheet, and advertises a 99.53% average detection rate. It also sells a separate consulting practice that builds and hardens production AI systems, starting at 5,000 USD a month.
What it does
- Score every model output on correctness, completeness, safety and three adherence metrics
- Repair a failing output automatically with FixIt, or regenerate it from scratch with ReGen
- Re-evaluate the corrected output before it is delivered to the end user
- Flag unsafe content, including PII, hate speech, self-harm material, prompt injection and CBRN
- Track quality, latency, token usage and cost drift across an entire AI stack
- Keep per-run audit logs that can be produced as compliance evidence
- Award and publicly verify a Hallucination-Safe certificate for a protected product
When to use DeepRails / When not to
A quick filter to help you decide if DeepRails is the right fit.
When to use DeepRails
- Engineering teams shipping LLM features where a wrong answer carries real cost, typically in healthcare, finance or legal products
- Developers running RAG pipelines who need every claim traced back to the context they supplied
- Teams building multi-agent systems that need planning steps and tool calls scored hop by hop
- Product and support teams operating customer-facing chatbots on the web, in mobile apps or in Slack
- Compliance and risk owners who must produce audit evidence showing how AI outputs were checked
When not to use DeepRails
- Anyone looking for a model that writes content: DeepRails only evaluates and repairs output from an LLM you already run
- Solo users on a tight budget, since the free tier stops at the Playground and the first plan with API access costs 49 USD a month
- Teams that want a mobile app or a browser extension, as the product is an API plus a web console
- Organisations required to keep data inside the European Union, because all processing runs in the United States
- Non-English-speaking teams, as the interface, documentation and support are English only
How to use DeepRails
A typical end-to-end flow, from setup to results.
- Create an account on the DeepRails console; no credit card is required to start
- Open the Playground and paste a prompt and a model response to watch detection and correction run with no code
- Install one of the official SDKs for Python, TypeScript, Go or Ruby
- Configure a workflow: choose the guardrail metrics that matter, set hallucination thresholds and pick an improvement action
- Select a run mode, from Super Fast to Precision Max Codex, to set the accuracy against compute cost trade-off
- Leave thresholds on automatic so they self-calibrate after roughly 25 runs, or set exact values per metric
- Call the Defend endpoint with your workflow ID, model input and model output to get a corrected answer back
- Call Monitor instead when you want scores, flags and explanations without automatic remediation
- Watch scores, latency, token usage and cost in the console, and open any run for per-metric detail
- Set the auto top-up threshold and recharge amount so production is never interrupted by an empty credit balance
Pros & Cons
Pros
- Corrects rather than merely flags: FixIt or ReGen produces a new answer, which is re-scored before delivery
- Model-agnostic, so it sits in front of OpenAI, Anthropic, Google, Cohere, Mistral, Meta Llama, DeepSeek, xAI or a self-hosted model
- Continuous 0-100 scores across six documented metrics rather than a coarse rating scale
- Quick to try and quick to wire in: a free no-setup Playground, SDKs in four languages and a claimed five-minute integration
- No lock-in on self-serve plans, with upgrades taking effect immediately and pro-rated credit for unused time
- Unusually thorough privacy paperwork for a young company: named sub-processors, SCCs, a DPA on request and a reachable DPO
- Configurable credit auto top-up keeps production traffic from stalling when the balance runs out
Cons
- The free tier includes no API access at all, so real integration testing starts at 49 USD a month
- Usage beyond the included credits is billed separately, at 20, 18 or 15 USD per 1,000 calls depending on plan
- The headline benchmark against AWS Bedrock Guardrails is published by DeepRails itself, not by an independent party
- Security controls are described as designed to meet SOC 2 Type II standards, which is an intent rather than a certification the site claims to hold
- All processing happens in the United States, with no European hosting region and no Article 27 EU representative named
- Interface, documentation and support are English only, and there is no mobile app or browser extension
- A comparison article still quotes an outdated 5 USD Basic plan, contradicting the pricing page
Pricing & Plans
There is a permanent free plan, limited to the Playground. The cheapest paid plan is Basic at 49.00 USD per month, or 490 USD when billed annually. Every paid plan includes a monthly credit allowance and bills usage beyond it separately, per 1,000 API calls.
- Playground access only
- with restricted usage
- one seat and no ticket support. Aimed at developers exploring the product.
- API access to Defend and Monitor
- unrestricted Playground
- one seat
- standard ticket support
- 50 USD of monthly credits and 20 USD per 1
- 000 calls beyond them.
- priority API access
- custom guardrail metrics on request
- Hallucination-Safe certification
- ten seats
- priority technical support
- 150 USD of monthly credits and 18 USD per 1
- 000 calls.
- private API endpoint
- new bespoke metrics
- training
- white-labelling and custom deployment
- unlimited seats
- 24/7 support
- 500 USD of monthly credits and 15 USD per 1
- 000 calls.
- dedicated infrastructure
- SLAs
- volume discounts and white-labelling.
- AI consulting and implementation
- billed separately from 5
- 000 USD per month
- with fractional CTO or CAIO options.
Data, GDPR & hosting
A consolidated view of how DeepRails handles your data.
GDPR overview
The legal page carries a GDPR Compliant badge and backs it with substance rather than a slogan. There is a dedicated legal-basis section for the EEA, the United Kingdom and Switzerland, a rights section covering access, deletion and the right to complain to a local supervisory authority, and separate CCPA provisions for California residents. Data is transferred to and processed in the United States, and those transfers rely on the European Commission's Standard Contractual Clauses under Decision 2021/914, supported by encryption and access controls. A Data Processing Addendum is available on request from the Data Protection Officer at privacy@deeprails.com, with responses promised within 30 days. One gap stands out: no Article 27 representative in the European Union is named anywhere on the site, despite the policy addressing EEA users directly.
Who owns the data?
The Master Services Agreement splits ownership cleanly. Customer Data stays with the customer: DeepRails states it acquires no ownership rights in it, and that all intellectual property rights in it remain with the customer. Everything on the other side of the line, meaning the platform, its APIs, algorithms, machine learning models, interfaces, documentation, trade names and service marks, remains the exclusive property of DeepRails, Inc. or its licensors. Customer Content is defined as the prompts, model responses and context documents sent to the endpoints. Both the Privacy Policy and the Master Services Agreement carry an effective date of 1 January 2026.
Reuse rights
DeepRails commits to three negatives in its Privacy Policy: it does not sell customer data, it does not use Customer Content to train its own or third-party foundation models without explicit written consent, and it does not share Customer Content with third parties beyond what is needed to run the service. The sub-processors it does use are named. Amazon Web Services provides infrastructure, database hosting and Bedrock inference in US regions; Stripe, Inc. handles payments; and OpenAI, Anthropic, Google Cloud and Microsoft Azure may be called for evaluation and remediation. A full sub-processor list is available on request, and the Data Processing Addendum promises notice before any change. Nothing on the site documents a customer-facing switch to exclude data from training, which is consistent with a policy that does not train on it in the first place.
Data retention & training
Hosting summary
DeepRails runs on Amazon Web Services, which supplies cloud infrastructure, database hosting and Bedrock model inference in US regions. The Privacy Policy states plainly that customer data may be transferred to and processed in the United States, and no European hosting region is offered. For transfers out of the EEA, the United Kingdom or Switzerland, the company relies on the European Commission's Standard Contractual Clauses under Decision 2021/914, supported by encryption and access controls, and it holds Data Processing Agreements with every sub-processor. Evaluation and remediation work may be routed to foundation model providers, namely OpenAI, Anthropic, Google Cloud and Microsoft Azure through the Azure OpenAI Service. Payments go through Stripe, Inc., a PCI-DSS Level 1 processor, and DeepRails states it does not store full card numbers or CVVs. The complete sub-processor list is available on request. The vendor itself is DeepRails, Inc., a Delaware corporation, so United States law governs the arrangement.
Things to keep in mind
Risks and trade-offs to weigh before adopting DeepRails.
- Automatic correction breeds complacency: a team that trusts the guardrail may stop reading model output at all, which moves the single point of failure rather than removing it
- The advertised detection rate is 99.53%, not 100%, so a residual share of hallucinations still reaches users, and the ones that survive an automated check are the subtlest
- The comparative benchmark is self-published, so the percentages are vendor claims until reproduced on your own data
- Every prompt, model response and context document you evaluate leaves your systems and is forwarded to sub-processors including OpenAI, Anthropic, Google Cloud and Microsoft Azure
- API interaction logs are kept for the lifetime of the account by default, and only Enterprise customers can shorten that window
- A Hallucination-Safe badge shown to end users transfers trust from your product to a supplier's automated check, and those users cannot audit what it covers
- Costs scale with traffic through per-call overage, so a sudden spike produces a bill as well as load
Setup & Integrations
Technical difficulty
Low for a first look, moderate for production. The Playground needs no setup at all and is open on the free plan: paste a prompt and a response, read the scores. Wiring it in means an API key, one of the SDKs for Python, TypeScript, Go or Ruby, and a single call to the Defend endpoint carrying your workflow ID, model input and model output; DeepRails claims under five minutes. Thresholds self-calibrate after about 25 runs, so no tuning is required up front. The real prerequisite is that you already run an LLM in production.
Deployment
Integrations
Supported languages
Behind DeepRails
Social
Resources
All the official URLs gathered for verification and reference.
Alternatives
Tools that compete with or complement DeepRails.
Frequently asked questions
What does DeepRails actually do to a bad answer?
Which language models does it work with?
Is there a genuinely free option?
How is usage billed?
Which guardrail metrics are available?
Where is my data processed, and is it used for training?
How long is data kept?
What is Hallucination-Safe certification?
Should you pick DeepRails?
DeepRails answers a narrow question well: what should happen after an evaluation decides an answer is wrong. Most tools in this space stop at detection and hand the problem back to an engineer. DeepRails corrects the output, re-scores the correction and only then delivers it, and that single architectural choice is the reason to look at it.
The rest of the picture is solid rather than remarkable. Pricing is public and legible, with a permanent free tier, a 49 USD entry point and no lock-in on self-serve plans. The legal paperwork is more thorough than a company this young usually bothers with: sub-processors named, Standard Contractual Clauses in place, a Data Processing Addendum on request and a Data Protection Officer who is actually reachable. Integration is genuinely light, at four SDKs and a single API call.
The caveats deserve weighing before committing. The benchmark that everything else leans on is DeepRails' own, although the raw data is public. SOC 2 Type II appears as a standard the controls are designed to meet, which is not the same as a certification held. All processing happens in the United States, with no European region and no Article 27 representative named, which will settle the question on its own for some European buyers. And the free tier excludes the API, so serious evaluation starts at 49 USD a month.
Reserve it for cases where a wrong answer costs something real, such as clinical, financial, legal or educational products, RAG systems and multi-agent pipelines, and where automatic remediation is worth paying for. For an internal tool whose mistakes are merely annoying, monitoring alone is probably enough. Test it against your own data before believing any percentage, including the ones on the homepage.
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