Replicate is the fastest way to call an open-source model. Thousands of public models are one HTTP request away, priced per second of hardware time or per token for some first-party models, and you can push your own model as a Cog container. Cold starts and per-second metering are the trade for never thinking about servers.
packet.ai is the GPU underneath that kind of platform, sold directly. The comparison here is about the point at which a team stops calling models and starts owning the hardware they run on, and what that switch saves.
Platform overview: packet.ai vs Replicate
What Replicate offers
Replicate bills per second of hardware while a prediction runs, including setup time on cold starts. Hourly equivalents: T4 $0.81/hr, L40S $3.51/hr, A100 80GB $5.04/hr, H100 $5.49/hr, with multi-GPU shapes up to 8× for larger models. Some first-party models are priced per token or per output instead. There is no scale-to-zero cost when idle unless you pay for minimum instances.
- Thousands of ready-to-call public models, one API for all of them
- Custom models via Cog containers with a defined predict interface
- Per-second billing including setup; cold starts on infrequently used models can be long
- Hardware rates are 2–4× neocloud GPU-hour prices; multi-GPU capped at 8×
What packet.ai delivers
Replicate's per-second hardware rates carry the cost of the platform around them: instant cold starts, thousands of ready models, Cog packaging. packet.ai sells the same silicon underneath, direct, by the hour, without that layer. The gap is largest on exactly the cards most teams use for production inference: A100 80GB at $1.43/hr against Replicate's $5.04/hr hardware rate, a 72% difference, and L40S at $0.92/hr against $3.51/hr, a 74% difference.
- Dynamic: scheduler-enforced multi-tenant, hourly, always warm once running. RTX 6000 Pro at $0.66/hr, B200 starting at $3.75/hr, neither of which Replicate lists at all.
- Dedicated: a whole card, single-tenant, 99% SLA, no cold starts ever. RTX 4090 at $0.39/hr, L40S at $0.92/hr, A100 80GB at $1.43/hr, B200 at $6.99/hr.
What Replicate has that packet.ai does not: thousands of models one API call away, Cog's predict() packaging, and genuinely zero cost for traffic that never arrives. That trade makes sense while you are discovering which model works and how much traffic it will get. Once a model is in steady production, packet.ai's hourly rate on the same hardware, with no cold starts, is the cheaper and faster-serving option.
Billing is hourly with monthly commits up to 20% off. There are no platform fees and no ingress charges; egress is $0.04/GB. Up to 2 TB of local NVMe per node is included. Dynamic instances are SSH-ready in under five minutes, Dedicated in five to ten. Capacity is live in the US (California, Virginia, Texas, Oregon) and Europe (Frankfurt, Amsterdam, Paris, London, Dublin). H100 SXM, H200 and RTX 5090 are on the notify list.
Against Replicate, packet.ai's GPU-hour is a small fraction of Replicate's hardware rate: the A100 80GB is $1.43/hr against $5.04/hr, the L40S is $0.92/hr against $3.51/hr. The trade is that on packet.ai you run the server; the payoff is that once a model is getting steady traffic, the same hardware costs 70% less and there are no cold starts.
packet.ai vs Replicate at a glance
Published starting rates in USD per GPU-hour, on-demand unless noted. Percentages compare the first price in each cell.
| Category | packet.aiYOU | Replicate |
|---|---|---|
| A100 80GB | $1.43/hr Dedicated−72% | $5.04/hr per-second equivalent |
| L40S (48GB) | $0.92/hr Dedicated−74% | $3.51/hr per-second equivalent |
| B200 (180–192GB) | $3.75/hr Dynamic · $6.99/hr Dedicated | Not listed |
| RTX 6000 Pro (96GB) | $0.66/hr Dynamic | Not offered |
| RTX 4090 (24GB) | $0.39/hr Dedicated | Not offered (T4 $0.81/hr is the entry card) |
| H100 (80GB) | Launching soon (notify list) | $5.49/hr per-second equivalent |
| Billing | Hourly | Per second of prediction time, including setup |
| Idle cost | Hourly while instance is up | None, unless minimum instances are configured |
| Cold start | None once the instance is up | Seconds to minutes for cold models |
| Model catalogue | Bring your own; Pixel Factory API for image/video | Thousands of public models, one API |
| Custom model packaging | Any container or bare process | Cog container with predict() interface |
| Multi-GPU | Multi-GPU nodes, bare metal, InfiniBand clusters to 1,024+ | Up to 8× per prediction |
Detailed comparison
Price per hardware hour
Replicate's per-second rates work out to $5.04/hr for an A100 80GB and $3.51/hr for an L40S. packet.ai charges $1.43/hr and $0.92/hr for the same cards. That is a 72–74% difference, and it is before counting Replicate's setup time, which is billed, and before counting the fact that you cannot get an RTX 6000 Pro or RTX 4090 on Replicate at all.
The difference is justified when traffic is sporadic: ten predictions an hour on Replicate costs you ten predictions' worth of seconds, while a packet.ai instance costs you the hour regardless. The moment a model is running most of the time, the per-second premium becomes pure overhead.
Cold starts and predictability
Replicate models that have not been called recently boot from cold, and for a large diffusion or LLM checkpoint that can mean tens of seconds to minutes of latency on the first request, with setup time on your bill. Minimum-instance settings fix it at a cost that approaches renting the GPU outright.
A packet.ai Dedicated instance is always warm because it is always yours, backed by a 99% SLA. For anything user-facing with a latency budget, that alone often decides it.
Control and packaging
Replicate's Cog format is elegant and constraining: a predict function, declared inputs and outputs, one model per container. packet.ai gives you a machine. Serve three models on one GPU, run a custom scheduler, pin a specific CUDA and driver version, use a framework Cog does not anticipate. The flexibility cuts both ways: you also handle the ops.
Where Replicate is the right call
Prototyping, low-volume products, and any situation where you want to try twelve models before committing to one. The catalogue is the product. packet.ai's Pixel Factory covers the image and video generation API case for teams that want that without a GPU, but for breadth of public models Replicate is unmatched.
Who should choose which
Choose packet.ai if
- Your model serves enough traffic that per-second billing at $11.52/hr effective has become the primary cost
- You want full container control, multi-GPU instances, or cluster compute
- Your model is a custom fine-tune or architecture that does not fit Cog packaging cleanly
- You want an RTX 6000 Pro at $0.66/hr or B200 at $3.75/hr that Replicate does not offer
Choose Replicate if
- You want to call a model from Replicate's catalogue with a single API call and no infrastructure
- Volume is low or bursty enough that scale-to-zero saves more than the per-GPU premium costs
- You want Cog packaging and deployment tooling for a model you plan to publish or share
- You need the model to be accessible as a hosted endpoint without running your own serving stack
Conclusion
Replicate is where you discover which model works. packet.ai is where you run it once you know. The hardware underneath costs roughly a quarter as much per hour on packet.ai, and it never cold-starts. Start on Replicate, graduate to packet.ai when the traffic curve says so.
Same silicon. Smarter economics.
Every rate on this page is a published starting price you can deploy against today. Dynamic launches in under five minutes; no credit card to start.
Additional resources
- packet.ai GPU pricing: the full Dynamic, Dedicated and Clusters rate card.
- packet.ai documentation: API, CLI and platform guides.
- How intelligent scheduling works: why Dynamic keeps peak performance at a lower price.
- Clusters and wholesale quotes: multi-node InfiniBand capacity at ~30% below retail.
- Rent GPUs on packet.ai: specs, pricing and workload fit for every card.