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PromptScout.
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Early Access Waitlist Next-Gen LLM Observability

Observability & Performance Telemetry for Production AI Pipelines

PromptScout tracks inference latency, monitors token consumption, and applies automated semantic caching to reduce AWS cloud compute and model API costs by up to 40%.

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Join the waitlist by email Access invitations at launch

Savings depend on workload, cache hit rate, and provider pricing.

Workspace / Production overview
Simulated live

Platform Interface Preview

Interactive product preview · Sample data, not a live deployment

Avg. latency

142ms

↘ 24.8% vs. previous period

Cost saved

$1,280

This month · estimated

Token throughput

4.2M

Tokens processed this month

Cache hit rate

38.6%

Semantic caching active

Inference latency

● Median ● P95
Sample inference latency over 24 hours Illustrative median latency stays below P95, with both trending lower through the day. 3002001000
00:0006:0012:0018:0023:59

Recent traces

DEMO
chat.completion142 ms
cache.hit12 ms
vector.search38 ms
All sample traces healthy

Built for the infrastructure you already use

AWS BedrockAmazon SageMakerAWS LambdaOpenTelemetry

From prompt to production

Less guesswork.
More performant AI.

Bring latency, quality, and spend into one view. Find the bottleneck, understand the impact, and ship with confidence.

Real-Time Latency Tracing

Follow distributed traces across LLM calls and vector stores. Pinpoint slow spans before they become slow experiences.

Dynamic Semantic Caching

Slash repetitive compute bills with low-latency prompt deduplication. Reuse relevant responses, even when wording changes.

Cloud & AWS Native Telemetry

Connect your AI infrastructure across AWS Bedrock, SageMaker, ECS, and Lambda for a consistent view of production.

Guardrails & PII Redaction

Apply automated compliance filtering and redact sensitive fields for enterprise workloads, with policies your team controls.

Anomaly & Drift Detection

Get real-time alerts for token spikes, prompt drift, and potential hallucinations so your team can investigate sooner.

Cost Allocation & Tagging

Attribute spend to each tenant, environment, and feature. Turn a single cloud bill into clear, actionable unit economics.

Your stack, connected

Cloud native.
Developer first.

Connect application traces with model performance and cloud spend. OpenTelemetry compatibility brings a familiar instrumentation approach to your AI workloads.

  • Inference visibility across Bedrock and SageMaker
  • Telemetry from services running on Lambda and ECS
  • Amazon DynamoDB in the cloud architecture
  • Private VPC deployment options for enterprise teams
Explore integration guidance

Pricing that scales with you

Start small. Ship something big.

From your first trace to enterprise-wide visibility.

Developer

For experiments becoming products.

$0 /mo

Join Waitlist
  • 50k events / month
  • 7-day retention
  • Community support
FOR GROWING TEAMS

Pro

For teams running AI in production.

$49 /mo

Request Access
  • 1M events / month
  • 30-day retention
  • Semantic caching layer
  • Real-time alerts

Enterprise

For infrastructure at your scale.

Custom

Request Access
  • Unlimited events
  • Private VPC deployment
  • Custom SLAs
  • SOC 2 compliance requirements review

Planned launch pricing in USD. Join the waitlist to request access; availability and deployment details will be confirmed before onboarding. Enterprise limits and commitments are defined in your agreement. Request current security documentation for SOC 2 requirements.

Developer resources

A clear path to your first trace.

Request integration information, OpenTelemetry setup guidance, and AWS deployment details from our team.

Request Integration Details

Good questions

A little more clarity.

Have a specific architecture in mind?
Let’s talk through it.

Can I deploy PromptScout in my own AWS environment?

The Enterprise plan includes a private VPC deployment option. Contact us to review your AWS regions, networking, IAM policies, and data residency requirements before onboarding.

How much latency does instrumentation add?

Overhead depends on your integration, sampling settings, and deployment. Benchmark with representative traffic during the beta. Dashboard values on this page are simulated examples, not performance guarantees.

How should we handle sensitive prompt data?

Configure collection and redaction policies before sending production telemetry. Review retention, access controls, and deployment boundaries with our team. Automated PII filtering is an additional control; validate it against your organization’s requirements.

Will semantic caching work for every request?

Caching is best suited to repeatable requests with reusable answers. Personalized, time-sensitive, or permission-dependent responses need appropriate isolation and freshness rules. Savings vary with workload similarity and cache configuration.

Make every inference count

Your next optimization starts here.

See what your AI pipelines are doing. Understand what they cost. Build a better production experience.

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