LLM Observability · Now in Beta

Stop flying blind
with LLM calls

Add 3 lines of Python. Instantly see what each LLM call costs, who's using it, if it's failing, and whether quality is dropping — all in one dashboard.

Start for free → See how it works ↓
prism · live stream
14:02:31 200 POST /api/chat → claude-sonnet 312ms · $0.0021
14:02:34 200 POST /api/summarize → gpt-4o 891ms · $0.0084
14:02:38 429 POST /api/embed → text-embedding-3 rate limit hit ⚠
14:02:41 200 POST /api/classify → claude-haiku 128ms · $0.0004
14:02:45 eval quality score 8.4 / 10 ↑ +0.3 vs yesterday
14:02:50 200 POST /api/chat → claude-sonnet 278ms · $0.0018
2ms
avg overhead
100%
calls captured
5min
setup time
6+
LLM providers

You're shipping AI features blind

  • No idea how much you're spending per user, per feature, or per model
  • Errors silently swallowed — you find out from users, not dashboards
  • Response quality drifting after prompt changes? You won't know
  • Existing tools like Langsmith are complex and expensive for small teams

Prism gives you full visibility

  • Real-time cost tracking per endpoint, user, and model
  • Instant alerts on failures, latency spikes, and rate limits
  • Automated quality scoring with AI — track response quality over time
  • Per-client budgets — know what each customer costs you, with alerts before limits are hit
  • Session tracking — group traces by conversation and see cost + quality per flow
  • One SDK, 3 lines of code, works with any LLM provider

Everything you need.
Nothing you don't.

Built by developers who got tired of paying for bloated observability platforms.

📊
Find where money is leaking

See spend broken down by model, endpoint, and user. Know instantly which feature or customer is costing the most.

Catch slow endpoints before users do

P50, P95, P99 latency per call. Spot which prompts or models are dragging performance before a user complains.

🧠
Detect prompt regressions automatically

Every response gets an AI quality score. See immediately if a prompt change made things worse — before it ships to users.

🔔
Get notified before users complain

Slack or email alerts on errors, rate limits, cost spikes, or quality drops. Set your own thresholds.

🗂️
See the full cost of a conversation

Group traces by session and see total cost, latency, and quality per conversation thread — not just per call.

👤
Know what each client costs you

Pass a user_id and track spend per customer. Set monthly budgets per client with automatic alerts.

🟢
Know system health at a glance

Healthy, Warning, or Critical — based on live error rate, latency, and quality thresholds. No digging through logs.

🔍
Debug any LLM call in seconds

Inspect every prompt and response. Search, filter, replay — full audit trail with tokens, cost, and metadata.

🔌
Integrate in 3 lines of Python

Works with OpenAI, Anthropic, Gemini, Mistral and any custom LLM. No vendor lock-in, no infrastructure to manage.

Python ✓ TypeScript · coming soon Node.js · coming soon

Up and running
in minutes.

No infrastructure to manage. No complex config. Just wrap your existing LLM client and you're done.

STEP 01
Install the SDK

One package. No dependencies beyond your existing LLM SDK.

# install via pip
pip install useprism
STEP 02
Wrap your client

One line around your existing Anthropic or OpenAI client. No changes to your call logic.

import prism, anthropic

prism.init("your_api_key")

client = prism.wrap(
  anthropic.Anthropic(),
  user_id="customer-123"
)
STEP 03
See everything in Prism

Every call is automatically tracked — cost, latency, tokens, quality score, and full prompt/response.

# your existing code, unchanged
response = client.messages.create(
  model="claude-haiku-4-5",
  messages=[...]
)

# → traced automatically ✓

Simple pricing.
No surprises.

Start free, scale when you need. No credit card required to get started.

Hobby
€0
forever free
For prototypes & side projects
  • Up to 10k calls/month
  • 7-day log retention
  • Cost & latency dashboard
  • Email alerts
  • 1 project
Get started free
Scale
€99
per month
For production AI companies
  • High-volume usage
  • 1-year log retention
  • Custom eval models
  • Priority support
  • Unlimited projects
  • SSO + unlimited team
Contact us

No credit card required. Start tracking your first LLM calls in minutes.

Common questions.

Not exactly — Prism focuses on lightweight production observability: costs, latency, errors, traces, and quality monitoring. If you've tried LangSmith or Helicone and found them too complex or expensive for your needs, Prism is built for you.
By default yes — prompt and response previews are stored so you can debug traces. Logging can be disabled or limited per project. API keys are never logged. EU-friendly hosting and data redaction controls are on the roadmap.
OpenAI, Anthropic, Gemini, and Mistral work natively via prism.wrap(). Any custom or self-hosted LLM can be tracked using the manual context manager.
Most Python apps are sending traces in under 5 minutes. Install the package, call prism.init(), wrap your client — done. No infrastructure to manage.
The SDK is developer-focused, but the dashboard is useful for anyone on the team — founders tracking AI costs, product teams monitoring quality, or ops teams watching for errors. No code needed to use the dashboard.
You'll get an alert before you're close. During the beta, limits are enforced softly — we'll reach out before cutting anything off. Upgrade or contact us if you need more.

Know what your LLMs
are doing. Right now.

Join the beta — first 100 devs get Pro free for 3 months.