Automatic instrumentation
Patches LLM calls at startup so traces, latency, and cost flow into the dashboard without changing the rest of your application.
Fluiq
Fluiq is an AI ops stack for LLM applications that adds tracing, security scanning, caching, and automated evaluation through a Python SDK. It is built for teams running production LLM workflows and managing prompts, quality checks, and repeated requests.
Overview
Fluiq is an AI ops stack for LLM applications that combines observability, security scanning, caching, prompt management, and automated evaluation in one SDK and backend. The core workflow is simple: instrument your app once, then let Fluiq trace LLM calls, score outputs, and handle repeated requests on its infrastructure.
The product is aimed at Python teams building production AI systems with frameworks such as OpenAI, Anthropic, Gemini, LangChain, LangGraph, CrewAI, Google ADK, and MCP. Fluiq positions the free tier as observability-only, with paid tiers adding caching and security features as usage grows.
What Fluiq includes
Server-side security scanning
Scans prompts and responses for jailbreaks, prompt injections, PII, secrets, and indirect injection before or after the model call, depending on mode.
Trace-driven caching
Builds a cache profile from trace history and serves repeated prompts from a dedicated Redis cache to reduce duplicate LLM calls.
Automated evaluation gates
Scores responses with LLM-as-judge metrics such as hallucination, faithfulness, relevance, and toxicity, and can warn or block based on thresholds.
Prompt management
Provides prompt template editing, version history, environment deployment, and side-by-side model comparison for prompt workflows.
Framework-agnostic integrations
Supports function-call level tracing across OpenAI, Anthropic, Gemini, LangChain, LangGraph, CrewAI, Google ADK, MCP, and raw HTTP via `@trace`.
Common use cases
Production LLM observability
Add observability to an existing LLM pipeline so every call is traced with latency, cost, and per-node breakdowns in the dashboard.
Request-time security scanning
Block jailbreaks, prompt injections, PII, and secret leaks before risky content reaches the model, or run the same checks in warn mode for review.
LLM response caching
Reduce repeated model spend by letting Fluiq profile your trace history and serve duplicate prompts from cache automatically.
Automated quality checks
Gate releases or app responses on quality metrics such as hallucination, faithfulness, relevance, and toxicity.
Prompt iteration and deployment
Manage prompt templates across dev, staging, and production with version history and side-by-side model comparison.
Pros and Cons
Pros
- Combines tracing, security, caching, evaluation, and prompt management in one product.
- Uses trace history to power caching and cache profiling instead of requiring manual setup.
- Offers clear setup flow: install, get an API key, instrument once, and start tracing.
- Provides practical control modes such as warn versus block for security and evaluation gates.
- Supports a wide integration surface across common LLM providers, agent frameworks, vector databases, and MCP.
Cons
- Some capabilities are tied to paid tiers, so the free plan is limited to observability and smaller evaluation volume.
- The source material is strongest on Python and framework integrations; non-Python support is not clearly described in the available pages.
FAQ
How do you get started with Fluiq?
Fluiq instruments LLM and agent calls from startup with `fluiq.instrument()`. The docs say you install the package, create an account to get an API key, and then call `instrument()` once; supported calls from OpenAI, Anthropic, Gemini, LangChain, and MCP are traced automatically.
What does Fluiq trace?
The documentation says Fluiq traces supported LLM calls automatically after instrumentation, and it also exposes a `@trace` decorator for other Python functions that hit an LLM or vector database.
When do you need `fluiq.secure()`?
`fluiq.secure()` is the server-side security layer. The pricing and FAQ pages say it is included on Growth and Enterprise, and it can run in warn mode or block mode to flag or stop risky prompts.
How does caching work in Fluiq?
`fluiq.optimize()` is the caching layer. The pricing and FAQ pages say it is included on Team and above, and it can run in cache mode or observe mode to either intercept repeated prompts or preview savings.
Can you switch frameworks later?
Yes. The pricing FAQ says Fluiq instruments at the call level, so the same SDK can work across supported frameworks simultaneously. The contact page also lists integration support as a reason to reach out.
Quick Facts
- Category
- AI ops / developer tool
- Primary platform
- Python SDK
- Integrations
- OpenAI, Anthropic, Gemini, LangChain, LangGraph, CrewAI, Google ADK, MCP, and vector databases
- Core workflow
- Instrument once, then trace, secure, cache, and evaluate requests
- Pricing surface
- Free plan plus paid Team, Growth, and Enterprise plans
- Website
- getfluiq.com
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