FluiqFluiq
  • ObservabilityTrace every call, cost, and latency
  • SecurityBlock attacks, redact PII and secrets
  • OptimizationCache repeated prompts automatically
  • EvaluationScore responses and whole agent runs
  • DatasetsGolden sets that capture whole agent runs
  • Prompt ManagementVersion and deploy prompt templates
  • AlertsPush eval and security events to Slack

LLM Providers

  • OpenAI
  • Anthropic
  • Google Gemini
  • Google Vertex AI

Agent Frameworks

  • LangChain
  • LangGraph
  • CrewAI
  • Google ADK
  • MCP

Vector Databases

  • Pinecone
  • Chroma
  • Weaviate
  • FAISS
  • Qdrant
14 integrations · zero wrappersView all
Pricing
  • FAQPricing, evals, security & data
  • Fluiq DocsGuides, concepts & SDK reference
  • Code SamplesCopy-paste integration snippets
  • LLM Cost CalculatorCompare OpenAI, Claude & Gemini pricing
  • polygateOpen-source unified LLM client
  • InfragerCloud diagrams to secure Terraform
Contact
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Optimization

Stop paying for duplicate LLM calls

Fluiq profiles your real trace history, provisions a cache, and serves repeated prompts automatically.

Start freeRead the docs
Fluiq/ optimize
1h6h24h7d

Hit Rate

84.3%

10.5k hits saved

Total Calls

12.4k

last 24h

Misses

1.9k

15.7% miss rate

Cache Performance

Overall hit rate84.3%
EmbeddingCache91.2%
PromptCache77.5%

What you get

Server-side

Zero infra to manage

A dedicated cache instance is provisioned for your account. Nothing to deploy or operate.

Profiled

Built from real traffic

The cache profile comes from your actual traces, not a generic one-size heuristic.

Tunable

TTL and model scope

Set time-to-live and which models are eligible. Observe mode measures savings before you commit.

Insights

See the saving before you cache

Repeated prompts are ranked by what caching them would save, alongside your top-spending models, slowest calls, and where errors cluster.

Measure first. Cache what repeats.

  • Observe mode records would-be hits without intercepting calls
  • Cache mode serves duplicates from a dedicated instance
  • Per-model scope and configurable TTL
fluiq.optimize(mode="cache")Team plan and up
app.py
import fluiq

fluiq.instrument(api_key="fl_...")

# "observe" measures savings; "cache" serves duplicates
fluiq.optimize(mode="cache")

# Repeated prompts now resolve from your cache
answer(question)

Part of the Fluiq platform

Compare plans
ObservabilityTrace every call, cost, and latency.ExploreSecurityBlock attacks, redact PII and secrets.ExploreEvaluationScore responses and whole agent runs.ExploreDatasetsGolden sets that capture whole agent runs.ExplorePrompt ManagementVersion and deploy prompt templates.ExploreAlertsPush eval and security events to Slack.Explore

Unlimited traces, always free.

Tracing, security, evaluation, datasets, and prompts all run on the free tier. Caching and Slack alerts unlock on a paid plan.

Start freeRead the docs
FluiqFluiq

Observe, protect, optimize, evaluate.

PlatformObservabilitySecurityOptimizationEvaluationDatasetsPrompt ManagementAlerts
CompanyIntegrationsPricingDocsCost CalculatorBlogContact
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