Patronus AI
Simulation-first evaluation and training infrastructure for AI agents, built on Digital World Models.
Buy Patronus AI when your agents have to survive long, multi-step tasks and you want training environments, not just a score. The June 2026 $50M Series B and the first Digital World Model are real roadmap signals, and Lynx and FinanceBench are research-grade proof points you can cite internally. Skip it if you need lightweight chat coverage — a generic eval tool plus your own test set is cheaper. Also skip it if you want push-button evaluation without RL training infrastructure; Percival RL Environments assumes ML depth.
Verified 23h ago · liveness 69/100 · cite: rightaichoice.com/tools/patronus-ai
- AI research teams needing state-of-the-art hallucination detection
- Financial firms evaluating LLMs on domain-specific Q&A
- Agent developers training long-horizon task planners
- Enterprise safety and reliability teams wanting explainable guardrails
- Teams wanting push-button evaluation without ML or prompt-engineering depth
- Solo developers running high-volume evaluations on a tight budget
- Teams that need static chatbot Q&A testing only
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Skip Patronus AI if you only need lightweight chatbot Q&A spot-checks or lack ML depth to run RL training environments — a cheaper eval tool plus your own test set covers that case.
Every API call is metered on top of your plan: $10 per 1k small evaluator calls and $20 per 1k large evaluator calls, so a high-volume eval sweep can outrun the subscription price.
Individual is free and usable for exploration with $10 in API credits, Base at $25/mo fits a small team running up to 600 pages, and Enterprise is contact-sales for unlimited pages plus on-prem VPC, SSO, custom fine-tuning, and volume discounts. Against general-purpose eval tools priced around $20/mo, Patronus costs more once API calls are metered — but those tools don't ship training environments or a 70B hallucination detector.
In short
Patronus AI — Simulation-first evaluation and training infrastructure for AI agents, built on Digital World Models. Best for AI research teams needing state-of-the-art hallucination detection, Financial firms evaluating LLMs on domain-specific Q&A, Agent developers training long-horizon task planners. Free to start; paid plans from $25/mo.
What's new in Patronus AI
Checked todayAcross the latest 4 updates: 2 launches, 1 changelog entry and 1 news mention.
Introducing SpeedrunBench: A Challenging Benchmark for Frontier Agents
Patronus AI released SpeedrunBench, a benchmark aimed at evaluating frontier agents.
Introducing FigmaTrace: A comprehensive training dataset for Figma design workflows
Patronus AI published FigmaTrace, a training dataset covering Figma design workflows.
Getting GLM-5.2 NVFP4 Post-Training off the ground
Patronus AI detailed post-training work on GLM-5.2 in NVFP4 precision.
Announcing our $50M Series B to Simulate the Entire World's Intelligence and Unveiling our First Digital World Model for AI Agent Training and Simulation
Patronus AI raised a $50M Series B and launched a digital world model for agent training and simulation.
What people actually say about Patronus AI — is it worth it?
We scanned public community sources for Patronus AI on Sep 9, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 0 of the posts we fetched could be positively tied to Patronus AI. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Patronus AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Digital World Models that simulate agent actions in digital workflows
- First Digital World Model for interactive agent training environments
- Percival RL Environments for reinforcement-learning training sandboxes
- Percival Chat eval copilot with explainable verdicts over agent logs
- Lynx 70B hallucination detection model
- GLIDER evaluation with high-quality reasoning chains
- FinanceBench benchmark with 10,000 financial Q&A pairs
- BLUR dataset with 573 tip-of-the-tongue Q&A pairs
- MEMTRACK benchmark for agent memory
- Generative Simulators for autonomous scenario scaling
- SpeedrunBench benchmark for frontier agents
- FigmaTrace training dataset for Figma design workflows
- Multi-turn dialogue evaluation
- Long-horizon task planning spanning days to months
- Deep research comprehension and synthesis
About Patronus AI
Patronus AI is a frontier research lab building Digital World Models — simulations that predict and replicate agent actions inside digital workflows so frontier models can train on them. Instead of static prompt/response checks, Patronus generates interactive worlds spanning UI/UX navigation, finance workflows, customer service, deep research, and software development, then measures how agents hold up on long-horizon planning, multi-turn dialogue, and memory. The company raised a $50M Series B in June 2026 and released its first Digital World Model for agent training alongside it. The product line splits into Percival RL Environments for reinforcement-learning training sandboxes and Percival Chat, an eval copilot that inspects agent logs and returns explainable verdicts. Research artifacts include Lynx, a 70B hallucination detection model the company says is the first to beat GPT-4 on hallucination tasks, GLIDER for reasoning-chain guardrails, FinanceBench (10,000 financial Q&A pairs), BLUR (573 tip-of-the-tongue items), and MEMTRACK for agent memory. FigmaTrace, a training dataset for Figma design workflows, shipped in August 2026, and SpeedrunBench, a benchmark for frontier agents, arrived in September 2026. It is aimed at AI research and engineering teams rather than non-technical evaluators.
Behind the Verdict
Patronus AI is unusual in the eval market because it sells training infrastructure as much as testing. Most competing products score a prompt and a response. Patronus builds Digital World Models that simulate agent actions across digital workflows, then uses those worlds to generate training data — the company reports 1M+ world data artifacts, 85% UI/UX feature parity against real products, and a 30-40% model lift measured on long-horizon tasks, with contributions from 5k+ vetted experts. Whether or not you accept those numbers at face value, the framing is coherent: simulation scales where hand-written benchmarks stall. Strengths. The research output is genuinely differentiated. Lynx is a 70B hallucination detector that the company says is the first model to beat GPT-4 on hallucination tasks — a specific, checkable claim. GLIDER produces reasoning chains so guardrail decisions are explainable rather than opaque. FinanceBench (10,000 Q&A pairs from public financial documents) and MEMTRACK (agent memory) target measurement gaps that general benchmarks ignore. The June 2026 Series B, the first Digital World Model launch, FigmaTrace in August 2026, and SpeedrunBench in September 2026 show a lab shipping on multiple fronts, not one product. Where it fits. Research and engineering teams building agents for finance, customer service, data science, and design tooling. Anywhere agents run for days or months and memory plus planning failure is the actual risk. Percival Chat works as an eval copilot over existing agent logs; Percival RL Environments is for teams that intend to train, not just measure. Where it doesn't. Non-technical evaluators will find the surface research-heavy. Teams that want a Slack-shaped, push-button eval tool will be frustrated. The free Individual tier is genuinely usable for exploration — $10 in API credits, 2 projects, 5 experiments per project — but data access is limited to the last 2 weeks, so it is not an archive. Base at $25/mo raises pages to 600 and adds page add-ons at $0.50/month per page in 50-page blocks. API pricing is per call: $10 per 1k small evaluator calls, $20 per 1k large evaluator calls, $10 per 1k eval explanations, so volume testing gets expensive before Enterprise volume discounts kick in. On-prem or dedicated VPC, custom data retention, SSO, custom eval model fine-tuning, and eval dataset generation sit on the Enterprise tier. The honest summary: this is deep infrastructure with a research lab attached, priced for teams that will actually run evaluations at volume. It is not the cheapest way to spot-check a chatbot.
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Real-world workflow fit
Concrete scenarios for the personas Patronus AI actually fits — and what changes day-one when you adopt it.
You have a RAG agent producing summaries of earnings documents and need to know how often it invents numbers. You start on the free Individual tier, spend the $10 in API credits running Lynx over your agent's logged outputs, and pull the eval explanations to see which claims lacked support.
Outcome: A ranked list of hallucinated claims with explanations you can hand to the model team, produced without provisioning infrastructure.
Your customer-service agent handles multi-turn conversations but forgets context after several exchanges. You move to Base at $25/mo, load agent logs into Percival Chat, and run MEMTRACK to quantify memory recall alongside multi-turn dialogue evaluation.
Outcome: A per-conversation breakdown of where memory drops off, giving you a concrete target for the next training run.
You need agents trained for long-horizon finance workflows and cannot send data to shared infrastructure. You go through sales for Enterprise, deploy on dedicated VPC with SSO, and use Percival RL Environments to train planners against simulated finance worlds.
Outcome: Training environments running inside your own VPC with custom data retention, plus volume-discounted API rates for ongoing evaluation.
Use Cases
- Detect hallucinations in financial reports using the Lynx detection model.
- Evaluate agent memory recall against the MEMTRACK benchmark.
- Train long-horizon task planners inside Percival RL Environments.
- Audit customer service agent responses for safety with GLIDER reasoning chains.
- Simulate UI/UX navigation tasks across web and mobile apps at 85% feature parity.
- Benchmark frontier agent behavior with SpeedrunBench.
- Test design-workflow agents against FigmaTrace training data.
- Use Percival Chat to inspect agent logs and get explainable verdicts.
Models Under the Hood
as of 2026-09-22
Limitations
- The Individual tier is capped at 2 projects with 5 experiments per project, and Patronus Experiments, Logs, and Traces only retain the last 2 weeks of data — you lose the archive.
- Base at $25/mo raises the cap to 600 pages and adds page add-ons at $0.50/month per page in 50-page blocks, so heavy page use climbs.
- API calls are billed on top: $10 per 1k small evaluator calls, $20 per 1k large evaluator calls, and $10 per 1k eval explanations, which makes large-scale evaluation runs costly until Enterprise volume discounts apply.
- On-prem or dedicated VPC deployment, custom data retention, SSO, custom eval model fine-tuning, eval dataset generation, and 24/7 support sit behind a sales conversation.
- The platform is simulation and training infrastructure for agent evaluation — it is not a general-purpose end-user assistant.
as of 2026-09-29
Verification history
We have re-verified Patronus AI 17 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 17 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Patronus AI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Individual
$0/mo
Ideal for
Solo researcher or engineer exploring agent evaluation before committing budget, with under 1k evaluator calls per month.
What this tier adds
Free entry point: 20 pages, last 2 weeks of Experiments/Logs/Traces, 2 projects with 5 experiments each, and $10 in free API credits.
Base
$25/mo
Ideal for
Small team running regular evaluation passes on a defined agent surface and needing more page headroom than the free tier.
What this tier adds
$25/mo raises the cap to 600 pages, adds 50-page add-ons at $0.50/month per page, and unlocks more advanced features than Individual.
Enterprise
Contact us
Ideal for
Financial services or large platform teams needing dedicated infrastructure, SSO, custom fine-tuning, and volume API pricing.
What this tier adds
Contact-sales tier adds unlimited pages, on-prem or dedicated VPC, custom data retention, SSO, evaluation runs and webhooks, higher API rate limits, custom eval model fine-tuning, and 24/7 support.
Where the pricing makes sense
The company stage and team size where Patronus AI's pricing actually pencils out — and where peers do it cheaper.
Individual is free and usable for exploration with $10 in API credits, Base at $25/mo fits a small team running up to 600 pages, and Enterprise is contact-sales for unlimited pages plus on-prem VPC, SSO, custom fine-tuning, and volume discounts. Against general-purpose eval tools priced around $20/mo, Patronus costs more once API calls are metered — but those tools don't ship training environments or a 70B hallucination detector.
Setup time & first value
How long it actually takes to get something useful out of Patronus AI — broken out by persona, not the marketing-page minute.
On the free Individual tier you can register and run your first evaluator call the same day — no credit card required and $10 in API credits are applied at signup. Base at $25/mo is self-serve and immediate. Enterprise timelines depend on the on-prem or dedicated VPC deployment, custom data retention configuration, and SSO integration, and should be scoped with the vendor.
Switching to or from Patronus AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual test sets: export your prompt/response pairs and re-run them through Patronus Experiments to get structured verdicts instead of pass/fail notes.
- →From a generic LLM eval tool: port your existing evaluator prompts into the Patronus API and map prior scores to Patronus Experiments for comparison.
- →From in-house hallucination scripts: point Lynx at the same logged agent outputs to get a 70B detector's verdict alongside your heuristic checks.
- ↗To a lightweight eval tool: export Patronus Experiments, Logs, and Traces before the last-2-weeks retention window closes, since older runs are not retained on Individual.
- ↗To in-house evaluation: use Patronus Datasets and Comparisons to extract your labeled examples, then rebuild scoring with your own model.
- ↗To a static benchmark suite: rebuild from FinanceBench, BLUR, and MEMTRACK published datasets rather than porting platform results.
Integrations
Resources & Guides
Tutorials & Learning
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Official links
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