Primate Vision vs. Roboflow / Ultralytics (2026): Detections vs. Verdicts
The Roboflow + Ultralytics ecosystem is the best per-frame object-detection stack in the world. YOLO26 runs at 1.7–11.8ms per frame, deploys to twenty edge formats, and at fleet scale costs pennies per camera-hour. If your problem is “count the vehicles” or “is a hard hat present in this frame,” they win on speed and unit cost, full stop.
But their output is detections, not answers. Boxes and class confidences per frame, with no temporal understanding, no event semantics, and no verdicts. Primate Vision answers the question the frames add up to — “did the forklift enter the loading zone?” — with a deterministic verdict (yes / no / indeterminate), a calibrated 0–1 confidence, timestamped evidence segments, and an annotated evidence video, live over managed WebRTC or against an uploaded file.
This is part of our full 2026 video-AI landscape comparison.
Different layers of the same problem
Roboflow is an end-to-end CV platform: label data, train custom detectors (YOLO26, RF-DETR), deploy to cloud or edge, orchestrate in Workflows. Ultralytics maintains the YOLO family and monetizes via AGPL-3.0 copyleft → negotiated Enterprise licenses. Together they’re the dominant “train-your-own-detector” ecosystem, with genuinely best-in-class docs and real industrial logos (BNSF, Pella).
What they sell is perception: where objects are, per frame, fast. What Primate Vision sells is judgment: whether the thing you asked about happened, with proof. Different layers of the same stack — and plenty of serious deployments should run both.
The head-to-head
| Capability | Primate Vision | Roboflow / Ultralytics |
|---|---|---|
| Per-frame latency | ✅ 45ms p50 / 316ms p95, published | ✅ 1.7–11.8ms/frame (YOLO26 on T4 TensorRT10) — best in class (YOLO26) |
| Live streams | ✅ managed WebRTC | ✅ Serverless Video Streams + self-hosted RTSP — objects only |
| Temporal / event understanding | ✅ “did X happen,” timestamped segments | ❌ per-frame detections; aggregation logic is yours to build in Workflows |
| Open-vocab prompting | ✅ full questions, live or stored | ⚠️ object nouns only (YOLO-World, YOLOE-26, SAM 3) — behavioral/temporal queries still need training or a bolted-on third-party VLM |
| Verdict contract | ✅ deterministic yes/no/indeterminate + calibrated 0–1 confidence | ❌ boxes + class confidences; no question→answer abstraction |
| Evidence artifacts | ✅ annotated overlay evidence video + timestamped clips | ⚠️ DIY overlays/notifications via Workflow blocks |
| Bounding-box JSON | ❌ overlay video only | ✅ core competency — plus pose, masks, and metric depth now |
| Edge / air-gap | ❌ cloud only | ✅ their strongest card: 20 export formats, on-device, air-gapped Enterprise |
| Determinism | ✅ deterministic verdict path, model pinning, deterministic test mode | ✅ fixed trained weights — genuinely deterministic detections |
| Training required | ✅ none — ask in plain English | ⚠️ custom classes still mean dataset + labeling + training (open-vocab nouns excepted, at reduced accuracy) |
| Compliance | ⚠️ published targets; Enterprise SLA; no SOC 2 yet | ✅ SOC 2 on all Roboflow plans — ahead of us here |
One fairness note I insist on, because the stale version of this claim is everywhere: it is no longer true that new categories always require retraining in their stack. YOLO-World, YOLOE-26 (January 2026), and SAM 3 give text-prompted open-vocabulary detection at inference time. The accurate 2026 statement is narrower: open-vocab covers object nouns only, at reduced accuracy versus custom-trained models — and behavioral, temporal, or compositional questions (“employee skipped the handwash step,” “person fell and didn’t get up”) still require training a model or bolting a per-frame VLM block onto a Workflow, which forfeits the latency and cost advantages that made you pick YOLO in the first place, and still returns no verdict.
The gap between detections and answers
A detector tells you person: 0.94, forklift: 0.91 sixty times a second. Whether the person was in the forklift’s path, whether the event happened during this shift, whether the answer is yes, no, or genuinely indeterminate — all of that is aggregation logic you write, test, and maintain yourself. Every CV team I’ve talked to has a pile of this glue code, and nobody loves it. Roboflow’s Workflows make that engineering nicer. They don’t make it disappear.
Primate Vision’s contract starts where detections end: one question in, one deterministic verdict out — a closed vocabulary computed by similarity scoring against the video (not sampled from a language model), a calibrated confidence, timestamped clips, and an annotated evidence video a human can review. Model versions are pinnable (darwin-1.3); test mode is fully deterministic for CI.
Pricing: credits vs. one flat meter
Roboflow bills subscriptions plus a universal credit meter. Core is $79/mo (annual) with 50 credits; extra prepaid credits from $4, overage “Flex” credits at $6 — 50% more. Everything burns credits at published rates (credits page), including — notably — inference you run on your own hardware: “Running models that live on the Roboflow platform, whether run locally (self-hosted) or in the Roboflow Cloud, will consume credits.” And on the Ultralytics side, the $29/mo Pro seat does not include a commercial YOLO license — commercial use of AGPL YOLO models requires a negotiated, unpublished annual Enterprise fee (Ultralytics pricing).
Normalized (estimates): managed Serverless Video Streams run $3.00–$8.00/camera-hour depending on GPU size; a dedicated GPU deployment amortized across many YOLO streams can reach ~$0.08–$0.40/camera-hour at scale.
Primate Vision has two lanes:
- Metered — $0.01 per second of source video ($0.60/min), flat, fps-independent, no subscriptions, no credit arithmetic, no license negotiations. Queued time free; failed jobs free. A 30-second clip → verdict + confidence + timestamps + evidence video = $0.30.
- Enterprise — contact us for 24/7 continuous monitoring and camera fleets: dedicated capacity or on-site deployment at a small fraction of the metered rate, under highly discounted enterprise plans — the correct comparison against a dedicated-GPU fleet deployment.
At the metered rate we normalize to $36/camera-hour† — 4.5–12× their managed streams and orders of magnitude above their amortized fleet numbers. But the rows price different goods. Theirs is raw per-frame detection, with the judgment layer coming out of your engineering budget. Ours is the answered question with evidence. Buy verdicts on the metered lane; buy fleets on the enterprise lane.
† Metered rate normalized for comparison. Primate does not sell 24/7 continuous monitoring at the metered rate — continuous and fleet workloads use enterprise plans; contact us.
Choose Roboflow / Ultralytics instead when…
Often, honestly:
- Fixed, known object taxonomy at high volume and low latency. Manufacturing defects, PPE presence, vehicle counting at line speed — train once, run for ~$0.10–$4/camera-hour. At 1.7ms/frame, YOLO is the only viable class for line-speed industrial inspection; our 45ms p50 doesn’t compete there.
- Edge or air-gapped deployment. On-device TFLite/CoreML/TensorRT, no cloud allowed — we’re cloud-only.
- You need pixel-accurate localization: boxes, masks, pose, oriented boxes, now metric depth. We return overlay video, not spatial JSON.
- You have labeled data and CV engineers, and cost-per-frame is the dominant constraint.
- SOC 2 on every tier matters today — they have it; we don’t yet.
Choose Primate Vision when…
- Your question is about events, not objects. “Did anyone climb the fence” is not a class label; it’s a temporal judgment with an evidence requirement.
- You don’t have a dataset, labelers, or a training pipeline — and don’t want to acquire them to answer one question. Plain English, first verdict in minutes via a sandbox key in one POST.
- The output feeds an alert or audit trail: deterministic verdict + calibrated confidence + watchable evidence, in one JSON contract with webhooks or
Prefer: wait— versus detection streams plus aggregation code you own forever. - You want one legible meter. $0.01/second, flat — no credit tiers, no Flex overage, no metering on your own hardware, no AGPL license negotiation.
- The taxonomy changes weekly. New question = new prompt (changeable mid-stream over one WebSocket message), not a new training run.
Try it
Try it for free — real processing, no card required to start.
Your AI agent can do it for you — right from Claude. Primate Vision ships an MCP server, llms.txt, markdown docs twins, and a sandbox key available in a single POST — your agent can get a key, upload a clip, and read back a verdict without a human touching a dashboard.
For 24/7 monitoring and camera fleets: see our enterprise plans.
Method note: every Roboflow/Ultralytics claim traces to their pricing, credits, licensing, and blog pages accessed 2026-07-31. Estimates are labeled; community-reported unverified pricing anecdotes are deliberately excluded.