A payments company paid a reported $7 billion for the layer that counts AI usage, seven months after buying the layer that invoices it. Routing was never the scarce asset — the scarce asset is one normalised record of what every model call cost and who it was for, and if that record lives in your request path you are paying a percentage on every step your agents take.
These four advertise time-to-first-audio between 40 and 200 ms, and an independent harness measures their cloud medians at 188 to 313 ms — but the number that breaks a phone call is the spread, not the median, and one vendor's jitter is nearly four times another's. Price moves about 2.5× across the field and predicts neither. The tail is bought with deployment.
These four price a search between $1 and $16 per thousand, and the spread is not margin — it is how far down the retrieval pipeline each one reads. Price a whole research turn instead of a call and the ordering inverts: the cheapest rate card produces a turn costing three times the dearest one.
Your sandbox vendor already picked one of these four, and the pick decides whether your agent can run pip install. Rank them by cold start and you get the exact reverse of ranking them by how much Linux the agent gets — because the boot time is the kernel. Answer one question, does the code install things, and the field collapses.
The quoted number is 3–7× less CPU and memory than Chromium. The consequence worth planning around is that a fresh browser per task stops being a cost you amortise by reusing sessions — and session reuse is where browser-agent state leaks live. What you trade for it is a compatibility tail that fails silently.
All four can express the policy. Only two of them answer without the caller supplying the facts — and when the caller is an agent reading attacker-controlled text, that is the entire security property. The second question is the check budget: an agent makes dozens of authorization calls per task, and filtering a retrieval set makes thousands.
A chat product needs several hundred concurrent users before a dedicated GPU beats per-token pricing; an agent needs about a dozen workers, because it re-sends its whole context every step. That arithmetic — not the price per million tokens — is what should decide which of these four you build on.
Word error rate is close to settled between the four, and a couple of points of it lands on words your intent classifier ignores. The slice that decides whether a voice agent feels human is end-of-turn detection — several times larger than the transcription latency beneath it, and the one thing the four providers genuinely disagree about.
Four platforms that stand between your agent and a user’s Gmail or Salesforce. The catalog sizes they advertise are counted in four different units and are the part you will outgrow; the token vault none of them market is the part you would hate to build. The decision you cannot retrofit is whose name is on the consent screen.
Two of the four bill the agent loop at about nine cents per vCPU-hour and their prices are 3.6% apart; the other two do not charge for it at all. What each is actually selling is a place to keep the conversation — and AWS closing Bedrock Agents Classic to new customers on 30 July 2026 is the clearest evidence yet about which half of a managed runtime you can afford to rent.
All four speak CDP, so the automation code ports in a day and the SDK comparison decides nothing. The real choice is who holds the logged-in profile, the credentials that recreate it and the exit IP whose reputation you inherit — plus the fact that the latency spread between them is entirely control plane.
The feature grids converged, so the decision is licence and billing meter — and every meter prices the trace archive that becomes your golden set, regression baseline and fine-tuning corpus. Instrument against OpenTelemetry, dual-write the stream somewhere you own, and the platform becomes a swappable backend.
Every identity vendor now sells “auth for AI agents”, and the phrase covers two opposite problems: letting an agent into your app, and letting your agent out to someone else’s API. Pick on direction first — and notice that a token vault holding a user’s full grant has relocated the credential rather than shrunk it.
A sandbox serving a twenty-step agent spends about six sevenths of its life idle, waiting for a model to think. So cold-start milliseconds and per-vCPU-hour rates — the two numbers every comparison leads with — are the two that matter least. What decides your bill is whether idle is billed; what decides your blast radius is the egress default.
A reranker touches no index and holds no state, so swapping one is an afternoon — which finally makes chasing the leaderboard rational, except the leaderboard measures the axis where these four differ least. What differs by more than an order of magnitude is the billing unit and the licence, and both bite hardest at agent scale.
Three of these four constrain the sampler so invalid output cannot be produced, and the choice between them collapses to one question: do your schemas repeat? The fourth does something categorically different, and it is the only one that can enforce the rules that actually break agents — because a grammar guarantees the enum is one of five values and says nothing about which.
Every comparison of these four opens with tokens per second on a fixed batch — the one number that transfers worst to agent traffic, where the same prompt comes back twenty times with a few hundred tokens appended. What separates them is what the KV cache is keyed on, whether constrained decoding survives a full batch, and how much of your quarter the build step eats.
The 28 July specification retires the initialize handshake and the Mcp-Session-Id header, and every write-up so far has framed that as plumbing. It is not. Dropping the held-open connection forced Sampling, Roots and Logging onto a twelve-month deprecation clock — and those were the features that made an MCP client a peer rather than a caller. The protocol just settled what it is.
Every AI gateway sells the same headline feature: automatic failover to a second provider. That feature is not an availability win — it is an untested deploy that fires only during an incident, onto a model your evals never covered. Choose instead on who operates the hop, because that is the decision you cannot reverse cheaply.
List prices for agent search APIs cluster tightly around $5–8 per thousand queries, which makes the sticker the least interesting number in the comparison. What actually differs by an order of magnitude is how many tokens each one dumps into your context per result — and in an agent loop that re-sends its transcript every step, that is the bill.
Moonshot released 2.8 trillion parameters as a free download on 27 July — and priced its own API above the model it replaced, while no single GPU on the market can hold the weights. Open weights buy agent builders exactly one thing that closed APIs cannot, and it is not cost.
Naive agent loops die on minute 29 of a 30-minute job. Durable-execution engines journal every step so the next process can pick up exactly where the previous one died — and 2026 was the year hyperscalers shipped their own. Four engines now compete on the same primitive, with very different architectures and bills.
A 128K-token context window degrades past the first thousand tokens and vanishes the moment the session ends. The agent-memory infrastructure market crossed $6 billion in 2026 because "throw it all in the context" stopped being a strategy — and four frameworks now bet differently on what memory should rank, store, and forget.
Two years from Anthropic's launch, MCP isn't a debate — it's a dependency. Every frontier vendor, every major IDE, and one Pinterest team saving 7,000 engineering hours a month all ship against it. The interesting question is no longer *should you use MCP* — it's what fails at this scale and how the 2026 roadmap plans to fix it.
Four vector stores, four nearly identical feature lists — ANN, filters, hybrid search, all of it. The thing that actually decides which one survives the agentic-RAG stack at scale is invisible there: where the index sits relative to your primary data.
All four ship traces, datasets, and evaluators — the feature lists nearly match. What separates them is which feedback loop they were built to close: the dev loop, CI, the production gateway, or model-monitoring drift.
Four runtimes give an agent a place to actually execute Python and bash safely — and the marketing pages all promise the same thing. The thing that decides which one survives production is who owns the sandbox lifecycle.