llama.cpp vs Stele

llama.cpp and Stele are both inference engines & infra tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

llama.cpp

llama.cpp is a C/C++ inference engine that runs quantized LLMs entirely on local hardware, from an Apple Silicon laptop to an H100 cluster to a Jetson edge device, using the same binary and the same hand-tuned kernels across all of them. No API keys, no telemetry, no requests leaving the machine. It exposes an OpenAI-compatible server via `llama serve`, which means drop-in compatibility with tooling already pointed at OpenAI endpoints. The ceiling appears when you need the inference engine to do more than infer — there is no planning loop, no tool-calling orchestration, no agent layer built in. Teams building autonomous workflows bolt on a framework on top, which means they are maintaining two systems.

Stele

Stele is a shared memory layer that sits between your agents and your codebase. Every agent reads the same knowledge graph — decisions, tasks, risks, lessons — before it acts, and writes back what it learns. The atomic task-claiming mechanism means two agents cannot pull the same work item simultaneously, which prevents duplicated effort across parallel sessions. The friction is real: the product is invite-only and cloud-hosted with no self-hosted option, so teams with strict data residency requirements hit a wall immediately.

Attribute	llama.cpp	Stele
Pricing	Free	Paid
Free trial	No	No
Open source	Yes	No
Has API	Yes	No
Self-hosted option	Yes	No
Platforms	Linux, macOS, Windows, Android, ChromeOS, iOS, Web (WebGPU)	Web, local plugin (MCP)
Released	2023-03	—
Pros	OpenAI-compatible server endpoint via `llama serve`, so existing client code pointed at the OpenAI API redirects to localhost without rewriting integration logic. GGUF quantization support across 4-bit to full precision, which means a 27B-parameter model runs on a single consumer GPU — without it, that model requires data-center hardware or a paid API. Single binary with hand-tuned kernels for Apple Silicon, NVIDIA, AMD, Intel Arc, and CPU, so a heterogeneous hardware fleet runs the same inference stack without per-target build pipelines. Zero telemetry and zero outbound requests by design, which means organizations with data-residency or compliance requirements can run frontier models without a legal review of what leaves the network. MIT license with no paid tier or hosted service, so there is no usage ceiling, no rate limit, and no cost that scales with inference volume.	Shared knowledge graph across agents, so switching from Cursor to Claude Code mid-project does not reset the session's understanding of prior decisions and open tasks. Atomic task claiming prevents two agents from starting the same work simultaneously, which means parallel sessions produce additive progress rather than duplicated or conflicting output. Risk and lesson records surface at the moment a relevant change is being made — not after it ships — so an agent flags a known production bug before the code guard that prevents it gets removed. Single CLI install with no dashboard configuration required, so the memory layer becomes active without adding a workflow step between prompts.
Cons	llama.cpp provides no agent orchestration — no planning loop, no tool-use management, no branching on model output. Teams building agents must add a separate framework on top, which means debugging inference failures and orchestration failures in two different systems. Quantization introduces accuracy degradation that is model- and task-specific and requires empirical validation per deployment. Teams shipping to production benchmark every quantization level against their specific task — there is no general answer, and the work is not reusable across model updates. When inference throughput at scale becomes the primary constraint — high-concurrency production APIs serving hundreds of simultaneous requests — teams move to dedicated serving infrastructure such as vLLM or TGI, which implement continuous batching and paged attention optimizations that llama.cpp does not provide. At that point, llama.cpp remains useful in development but is no longer the production inference layer.	The product is invite-only during beta. Teams that need to start using a shared memory layer immediately cannot — there is no self-service onboarding path, and the waitlist timeline is not published by the vendor. The service is cloud-hosted with no self-hosted option. Teams working under data residency requirements or corporate policies that prohibit sending codebase decisions and task data to a third-party service cannot use Stele at all — and at that point the only path forward is building a local context-passing layer themselves or using a different tool that supports on-premise deployment. There is no API surface exposed by the vendor, so teams that want to pipe Stele data into existing project management or observability tooling have no programmatic integration path beyond what the CLI and agent plugins provide.

Bottom line

Llama.cpp is free while Stele is paid; llama.cpp is open source; only llama.cpp exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between llama.cpp and Stele?

llama.cpp is Free and open source, while Stele is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is llama.cpp better than Stele?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.