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Once you've fine-tuned a model, you need a GPU to serve it from. The four serverless platforms developers reach for disagree about one thing that follows you for years — the format you package the model in.
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Between two spec revisions in 2025, MCP servers quietly stopped being their own authorization servers. The one parameter that change forces your client to send is the whole security story.
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The three options differ by orders of magnitude in GPU memory — but the part that actually decides your result isn't the rank, and it isn't the quantization.
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Three popular repos all build a knowledge graph for your LLM. They were built for three different jobs, and the one axis that decides between them is whether your corpus sits still.
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Buyers shop for these cards by peak FLOPS. Token generation barely uses them. The spec that actually moves inference throughput is the one most spec sheets bury — and a single NVIDIA card proves it.
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Teams pick a multimodal embedder by its ImageNet zero-shot score. For retrieval that is the wrong number — and chasing it lands you with two models and two indexes instead of one.
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Three ways to put a model behind an endpoint — and they increasingly run the same engine underneath, so the thing you are actually choosing is not speed.
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Naive RAG retrieves once and hopes. Agentic RAG turns retrieval into a decision the model makes at runtime — paying for it on every query to win the queries that silently fail.
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Three open-source fine-tuning frameworks that look like rivals but are actually three different bets on which part of training is your real bottleneck.
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The hard part of letting an agent query your database is not the model that writes the SQL. It is feeding that model your schema. Three open-source projects bet on that, and one fine-tuned model bets against it.
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Million-token windows were supposed to kill retrieval. The benchmarks say something stranger — the choice is really between two different failure modes, and only one of them is loud.
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All three clear the recall-and-latency bar for almost any agent you'll build. The real decision is where the operational cost lives — and there's a query volume where the answer flips.
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Both libraries emit OpenTelemetry spans for your agent. They disagree on what to name the attributes — and that disagreement, not the instrumentation, is your real lock-in.
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The second wave of agent frameworks is leaner, typed, and vendor-backed — and underneath the branding, they're quietly converging on the same idea.
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They all wrap roughly the same inference engine, so they all run the same model at roughly the same speed. The thing that actually separates them is what shape they want to be — a daemon, a polished app, or an open one.
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Two of the most-cited essays on agent design say opposite things. They are both right — the disagreement is really about whether your task reads or writes.
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Three popular open-source memory frameworks that look like rivals but are actually three different bets on where memory lives — and how much of your architecture you hand over.
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They are not competing ways to give a model tools. One is the engine; the other is a distribution standard wrapped around it — and you pay for the wrapper in tokens and attack surface.
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The Model Context Protocol replaced its HTTP+SSE transport with Streamable HTTP in 2025. Choosing it does not make your server serverless-friendly — and the reason is the part nobody reads.
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The three names a JavaScript team keeps hitting when it tries to build an agent aren't competing for the same job. Two of them stack on top of the third.
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Every "voice agent framework" comparison pretends these three are the same tool. They sit at three different layers of the stack, and picking by features instead of layer is how teams end up rewriting.
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Three libraries promise the same thing — reliable JSON from a language model — and disagree completely on where to enforce it. The right pick follows one question: do you control the decoder?
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Embeddings smear error codes, SKUs, and function names into "nearby" meaning and lose the literal. Hybrid search fixes it — but the real work is in the fusion step, not the index.
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You cannot patch prompt injection out of a model. The defenses that actually hold treat it as an architecture problem — and start by taking away what a hijacked agent could do.
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The protocol everyone adopted in 2025 is simpler to build for than the hype suggests — but the part that decides whether your server works isn't the code.
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The hard part of remote MCP auth was never the login. It's proving a token was minted for *your* server and no one else's — the audience claim that turns a friendly proxy back into a locked door.
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Almost every vector-index comparison argues about query speed. Below ten million vectors that is the one thing that rarely decides it. The real choice is where your vectors live, and what it costs to change them.
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They get filed together as "LLM guardrails," but they guard three different things — format, flow, and content. Picking by stars gets you a tool that protects the wrong layer.
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Three ways to rent open-weight inference without owning a GPU — and why the fastest of them just licensed its speed to Nvidia instead of competing with it.
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The format you pick is downstream of where you run the model — and in 2025 the tooling quietly consolidated under your feet. A field guide to the three that matter and the libraries that survived.
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