Stop AI Hallucinations
in Production
Simple, transparent pricing for teams of all sizes. Integrate detection directly into your pipeline with zero friction.
Individual Plans
Free
For hobbyists and side projects.
Pro
For individual developers who need reliable detection.
UltraBest Value
For power users who need advanced features.
Teams Plans (2-20 members)
Per-seat pricing with team collaboration features. Contact us to join the waitlist.
Teams ProTeams
Team collaboration with Pro-level features.
Teams UltraTeams
Full power for teams that need it all.
Enterprise (20+ members)
Custom solutions for large organizations with SSO, advanced audit logs, dedicated support, and custom SLAs.
Unlimited checks
SSO / SAML
Dedicated support
Custom SLA
Compare All Features
Detailed feature breakdown across all plans
| Feature | Free | Pro | Ultra | Teams Pro | Teams Ultra | Enterprise |
|---|---|---|---|---|---|---|
| UsageChecks per day | 3 | 15 | 40 | 25 | 50 | 100 |
| Models per check | 1 | 2 | 4 | 2 | 4 | 6 |
| Max context length | 5K | 12K | 32K | 16K | 32K | 100K |
| RAG queries / month | 50 | 300 | 2K | 1K | 3K | 10K |
| IDEConnected IDEs | 1 | 2 | 4 | 2 | 4 | 10 |
| Stored conversations | 250 | 1K | 3K | 1K | 3K | 5K |
| Retention | 30 days | 90 days | 1 year | 180 days | 1 year | 1 year |
| KnowledgeIndexed documents | 250 | 10K | 50K | 100K | 250K | 1M |
| Prompt templates | 5 | 25 | 50 | 50 | 50 | 200 |
| Smart archives | 0 | 5 | 20 | 10 | 20 | 50 |
| FeaturesHallucination check | ||||||
| Context health |
16 Models Across 6 Providers
Cross-check against the best LLMs — higher tiers unlock more models
OpenAI
7 models
Anthropic
5 models
3 models
xAI
1 model
Perplexity
1 model
Moonshot
1 model
Detect hallucinations
programmatically.
Don't rely on manual checks. Integrate our lightweight Python SDK directly into your evaluation pipeline or CI/CD workflow. Catch drift before it reaches your users.
import os
from hallucinated import Client
client = Client(api_key=os.getenv('API_KEY'))
# Check your LLM response for factual consistency
response = client.check(
prompt='Explain quantum entanglement',
completion=llm_output,
strictness=0.8
)
if response.hallucination_score > 0.5:
print(f"Risk detected: {response.reasoning}")
else:
deploy_to_production(llm_output)Need more? Use credits.
Pro and Ultra users get access to a flexible credit system. When you exceed your monthly allocation, credits kick in automatically. No interruptions, no surprises.
How Credits Work
Credits never expire
Use them whenever you need extra capacity
Flexible credit packs
Purchase credits in bulk at discounted rates
Spending limits
Set a default cap — adjustable anytime in settings
Auto-top-up
Optionally auto-purchase when credits run low
Free plans pause until next cycle. Credits are available on Pro, Ultra, and Teams plans only.
16
LLM Models
6
Providers
5
IDEs Supported
<100ms
API Latency