Architecture Comparison
Both approaches exist because enterprise document sets do not fit cleanly into one context window. RAG solves that by breaking the corpus into chunks, embedding them, retrieving a subset per query, and reinjecting those chunks every time. LATCH solves it by compiling the corpus once into a persistent model-level representation and then querying the compiled memory directly.
That difference changes almost everything downstream: latency, cost model, portability, operational surface area, and the kinds of reasoning errors the system produces under load.
Side-by-Side
The numbers below use the currently published LATCH benchmark profile: 0.11s time-to-first-token, 1.6ms cache reload, 97% cost reduction after amortization, and 50% less VRAM on the benchmarked H100 setup.
| Dimension | RAG | LATCH |
|---|---|---|
| Architecture | Chunk -> Embed -> Retrieve -> Inject per query | Compile once -> Query against persistent memory |
| Per-query document processing | Yes, every query | None after compilation |
| Cross-document reasoning | Limited by chunk boundaries | Full corpus awareness |
| Chunking artifacts | Yes (hallucination seams at boundaries) | None |
| Cold start latency | High (embed + retrieve + inject) | 0.11s |
| Persistence | Embeddings in vector DB; no model-level state | .latch/.latchdoc binary on disk |
| Portability | Requires vector DB + source docs + config | Single binary file, reload in 1.6ms |
| VRAM overhead | Context window grows per query | 50% less than baseline |
| Cost model | Linear with query volume | Amortizes to near-zero after 25 queries |
| Infrastructure | Vector DB + embedding model + orchestrator | Single Docker container |
RAG keeps the original document-processing path alive on every query. The system still has to choose chunks, retrieve them, and inject them back into the prompt path each time. LATCH moves that cost up front into a compilation step, which means the runtime path after compilation is materially simpler and does not revisit the raw corpus for normal querying.
With RAG, per-query work never really stops. Every request reopens the retrieval problem, and the cost scales with usage volume. With LATCH, the expensive conversion step is paid once, so repeated query volume improves the unit economics rather than punishing them.
RAG can work well when the answer sits inside one or two relevant chunks. It becomes less reliable when the answer depends on relationships across sections, files, or documents that are not retrieved together. LATCH is designed around whole-corpus compiled state, so the query path is not bounded by chunk selection in the same way.
Chunk boundaries are not just a storage detail. They introduce seams where evidence can be separated, context can be truncated, and partial retrieval can distort the answer. LATCH removes chunking from the main reasoning path, which is why the product framing is "not RAG" rather than "better retrieval."
The current benchmarked LATCH profile reports 0.11s time-to-first-token against a 23.1s baseline cold start on the H100 benchmark path. That delta matters in real operator workflows because it changes the product from a slow analytical batch experience into something that behaves like an interactive system.
RAG persists embeddings, indexes, and supporting metadata, but not model-level document memory. LATCH persists the compiled state itself as a binary file that can be reopened later. That persistence is the foundation for portability, team sharing, and amortized query economics.
A RAG deployment is usually tied to a vector store, source-document availability, and orchestration config. LATCH reduces the portable unit to a .latch or .latchdoc file that reloads in 1.6ms. That is a different operational model because the portable artifact is the intelligence package itself, not just the raw document set plus infrastructure recipes.
When a workflow depends on repeatedly reinjecting large context, the runtime memory burden keeps showing up per query. LATCH's current benchmark profile reports 50% less VRAM than the baseline path, which directly affects density and cost per node.
RAG tends to scale linearly with usage because every request redoes retrieval and reinjection work. LATCH pushes cost toward the front of the lifecycle. After roughly 25 queries on the benchmark path, the amortized cost reduction reported on the site is 97%.
RAG often implies a stack: vector database, embedding model, retrieval service, prompt builder, and orchestration logic. LATCH is currently shipped as a single self-hosted Docker runtime with an OpenAI-format compatible API, which simplifies the operator surface even though the underlying compilation mechanism is proprietary.
RAG is still a reasonable choice when the corpus changes constantly and recompilation cost would dominate the workflow, or when the product requirement is live retrieval from the open web rather than repeated querying over a fixed private corpus.
It is also the more obvious fit when an organization already has a mature retrieval stack and only needs incremental quality gains rather than a new runtime model. If you are evaluating tradeoffs instead of looking for a categorical replacement, the FAQ page is a faster starting point.
If the compiled-memory model is the right fit, the next useful pages are the main site for benchmark framing, the documentation for deployment and API details, and the self-hosted purchase page for the current evaluation license.