Dike vs llama.cpp

Dike and llama.cpp 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.

Dike

Route your OpenAI-compatible traffic through Dike and every prompt, retrieval step, and completion becomes a sealed, cryptographically verifiable audit record — the kind an auditor can check, not just a log you printed yourself. PII is stripped before anything touches storage, flagged responses queue for human sign-off, and when a serious incident fires, Dike opens the Article 73 case and starts the 15-day reporting clock automatically. The gateway is fail-open, so if audit storage goes unreachable, your requests still reach the model. The ceiling appears when your compliance requirements go beyond what a passive proxy can enforce — custom risk-scoring logic, multi-jurisdiction rules, or on-premises data residency all require architecture Dike does not currently offer.

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.

Attribute	Dike	llama.cpp
Pricing	Paid	Free
Price	€49/mo	—
Free trial	No	No
Open source	No	Yes
Has API	Yes	Yes
Self-hosted option	No	Yes
Platforms	Web gateway	Linux, macOS, Windows, Android, ChromeOS, iOS, Web (WebGPU)
Released	—	2023-03
Pros	Hash-chained, tamper-evident audit records generated automatically for every call, so when an auditor asks for cryptographic proof that logs were not altered, you export a file instead of defending a claim. PII redacted at the gateway before anything is written to storage, which means GDPR exposure from prompt contents does not accumulate in your audit logs over the legally required 6-month retention window. Article 73 incident reporting opens a case and tracks the 15-day regulatory clock automatically, so serious incidents do not slip past the deadline while your team is still triaging. RAG-specific retrieval logging records which documents the model actually used per response, satisfying the Article 12(2) evidence requirement that a plain chat transcript cannot meet. Fail-open gateway design means an audit storage outage does not take down your production service — requests still reach the model provider, so compliance infrastructure does not become an availability liability.	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.
Cons	No self-hosted deployment option exists; all traffic routes through Dike's hosted infrastructure. Teams whose security policy prohibits third-party proxies on the production inference path, or whose legal team requires data residency guarantees beyond EU-region cloud storage, cannot use Dike without a policy exception — and teams in that position typically move to building the compliance layer in-house or evaluating enterprise gateway vendors that offer on-premises deployment. The gateway is a passive proxy: it redacts, logs, blocks, and routes, but does not evaluate custom compliance rules. Teams needing per-user-role flagging logic, multi-jurisdiction rule sets, or dynamic risk scoring based on response content will reach the proxy's ceiling quickly and find themselves maintaining custom middleware on top of Dike — at which point they are running two systems. Dike is in closed beta at the time of writing; the vendor states teams must join a waitlist. Production availability, SLA commitments, and enterprise support terms are not publicly documented, which makes procurement sign-off harder for teams with formal vendor assessment requirements.	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.

Bottom line

Dike is paid while llama.cpp is free; llama.cpp is open source; only llama.cpp can be self-hosted; Dike runs on Web gateway; llama.cpp on Linux, macOS, Windows, Android, ChromeOS, iOS, Web (WebGPU). Pick the difference that actually blocks you.

Frequently asked questions

What is the difference between Dike and llama.cpp?

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

Is Dike better than llama.cpp?

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.

Dike vs llama.cpp: which should I pick?

Pick Dike if its pricing model, openness, or platform fit matches your constraints; pick llama.cpp otherwise. Check free-trial availability on each listing if you want to test before committing.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.