Pezzo

Pezzo is an open-source LLMOps platform for prompt management, tracing and caching across Node, Python and LangChain.

Pezzo is a cloud-native open-source LLMOps platform for observing AI calls, managing prompts in one place, caching responses and shipping prompt changes without a redeploy. Official clients cover Node.js, Python and LangChain, each with prompt management, observability and caching. The stack uses PostgreSQL, ClickHouse, Redis and SuperTokens, and can be started with Docker Compose. A development mode needs Node.js 18+ and Docker. Architecture notes, tutorials and provider recipes are in the documentation. Looking for another client is done through GitHub issues.

Pezzo at a glance

Best for: Software engineer
Runs on: open source, self-hosted, Web
Pricing: Free and open source
Released: April 2023
Category: AI coding tool
Website: pezzo.ai

Key features

Prompt management for Node.js, Python and LangChain
Tracing and observability of LLM calls
Response caching
Docker Compose for a full local stack
PostgreSQL, ClickHouse, Redis and SuperTokens
Docs with architecture and provider recipes

Use it for

See latency and cost for every LLM call in an app
Change a prompt without redeploying the service
Run the full Pezzo stack on a laptop

Companions

Tools that articles about Pezzo recommend using with it, each with its job.

LangChain — Built to work with LangChain.
LangGraph — Durable execution, checkpoints and human approval under create_agent.
LangSmith — Step-by-step traces and evals that show what an agent actually did
Pinecone — Managed retrieval for the fastest path to production.
Qdrant — Self-hosted vectors when cost or data residency matter.
Ollama — Fully self-hosted inference paired with Qdrant.

Alternatives to Pezzo

llm-d — Deploys production-grade LLM serving on existing hardware clusters.
OpenMonoAgent — Runs locally on your own hardware with no recurring subscription fees.
LiteLLM — One OpenAI-format API for 100+ LLM providers
Sulci Oss — Caches responses semantically to skip repeat LLM calls and reduce latency.
oMLX — It uses paged SSD KV caching to reduce agent TTFT to under 5 seconds.
LMDeploy — Accelerates inference with high-performance engines and quantization tools.
MTPLX — It runs local models twice as fast via native MTP speculative decoding.
GoRoute — Consumes 5.1MB of RAM with sub-10ms latency for coding agents.
LLMSchema — Provides AI coding agents with accurate database schemas without context bloat.
Free Coding Models — It pings 271 models in parallel to show live latency, stability scores, and verdicts.
Mesh LLM — Run large local models across multiple machines through one OpenAI-compatible endpoint.
AirLLM — Runs massive 70B+ parameter models on small 4GB consumer GPUs.

All Pezzo alternatives