Baseten is a production inference platform. You bring a model (or pick one), Baseten packages it with Truss, optimises it with TensorRT-LLM or its own engine, deploys it as an autoscaling endpoint that can scale to zero, and bills per minute of GPU time. It also runs in your own cloud account for teams with data-residency needs, and has added training in 2026.
packet.ai is the layer below: a GPU with SSH, hourly, at rates that undercut Baseten's per-GPU pricing by 40–60%. This comparison is for teams weighing the value of a managed serving layer against the cost of the hardware it runs on.
Platform overview: packet.ai vs Baseten
What Baseten offers
Baseten prices dedicated deployments per minute of GPU time, quoted hourly: T4 $0.63/hr, L4 about $0.85/hr, A10G about $1.21/hr, A100 80GB about $4.00/hr, H100 $6.50/hr ($0.10833/min), H100 MIG shapes lower, B200 $9.98/hr. Scale-to-zero means idle deployments cost nothing beyond cold starts; minimum replicas keep them warm at the full rate.
- Truss packaging, TensorRT-LLM and custom engines, speculative decoding, autoscaling and traffic splitting
- Self-hosted and hybrid deployments in your own cloud account
- Per-minute billing, scale to zero, no idle cost without minimum replicas
- GPU rates are 2–3× neocloud GPU-hour prices; no consumer or Blackwell workstation cards
What packet.ai delivers
Baseten's per-minute rates carry the cost of a real production platform: an engine that's tuned, an autoscaler that works, Truss packaging, and a team on call when the deployment misbehaves. packet.ai sells the same silicon underneath, hourly, without that layer. The gap is a straightforward 2–3× on every card: A100 80GB at $1.43/hr against Baseten's $4.00/hr, and B200 at $3.75/hr Dynamic against $9.98/hr, a 62% difference.
- Dynamic: scheduler-enforced multi-tenant, hourly, always warm once running. B200 starting at $3.75/hr, RTX 6000 Pro at $0.66/hr, neither of which Baseten prices 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 Baseten has that packet.ai does not: a tuned serving engine (TensorRT-LLM, speculative decoding), Truss packaging, working autoscaling with traffic-split tooling, self-hosted deployment into your own cloud account, and a team to call when something breaks. That is worth real money to a small team that cannot own inference ops yet. Once a model is serving steady traffic and that team exists, the same GPU costs 40–60% less on packet.ai and stays warm the whole time.
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 Baseten, packet.ai is the price of the hardware without the managed layer: an A100 80GB at $1.43/hr against about $4.00/hr, a B200 at $3.75/hr Dynamic against $9.98/hr. Baseten's engine and autoscaling are real, and worth paying for when a small team cannot own inference ops. Once they can, the same GPU costs 60% less on packet.ai and never cold-starts.
packet.ai vs Baseten 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 | Baseten |
|---|---|---|
| A100 80GB | $1.43/hr Dedicated−64% | ~$4.00/hr (per-minute) |
| B200 (180–192GB) | $3.75/hr Dynamic · $6.99/hr Dedicated−62% | $9.98/hr (per-minute) |
| L40S (48GB) | $0.92/hr Dedicated | Not listed (L4 ~$0.85/hr) |
| RTX 6000 Pro (96GB) | $0.66/hr Dynamic | Not offered |
| RTX 4090 (24GB) | $0.39/hr Dedicated | Not offered (T4 $0.63/hr entry card) |
| H100 (80GB) | Launching soon (notify list) | $6.50/hr (per-minute) |
| Billing | Hourly | Per minute of GPU time |
| Idle cost | Hourly while instance is up | None at zero replicas; full rate for minimum replicas |
| Serving stack | Yours: vLLM, SGLang, TensorRT-LLM on your GPU | Baseten engine, TensorRT-LLM, Truss packaging, autoscaling |
| Deploy in your own cloud | N/A (packet.ai is the cloud) | Self-hosted and hybrid options |
| Cold start | None once the instance is up | Seconds to tens of seconds from zero |
| Training | Any framework, hourly GPUs, clusters to 1,024+ | Training product added 2026 |
Detailed comparison
What the managed premium buys
Baseten's H100 at $6.50/hr and B200 at $9.98/hr are roughly 2–3× the neocloud GPU-hour. What you get for the premium is a serving stack that is tuned, an autoscaler that works, deployments that scale to zero, canary and traffic-split tooling, and a team on the other end when an engine misbehaves at 3 a.m. For a two-person startup with a product to ship, that is often the right trade.
packet.ai sells the GPU: a B200 at $3.75/hr Dynamic or $6.99/hr Dedicated, an A100 80GB at $1.43/hr. You install vLLM or SGLang or TensorRT-LLM, you set up your own autoscaling (or do not), you own the pager. The hardware costs 60% less; the operations are yours.
Duty cycle decides it
Baseten's scale-to-zero means a rarely used model costs almost nothing. A model that serves traffic all day runs at the full per-minute rate, which is where the 2–3× premium bites. A B200 on Baseten at $9.98/hr is $7,185 a month if always warm; the same card on packet.ai Dynamic is $2,700 a month, and the difference funds a lot of ops time.
The honest crossover is around 40% utilisation on the B200: below it Baseten's zero-idle model wins, above it packet.ai wins on price, and well above it packet.ai wins by a wide margin.
Residency and self-hosting
Baseten's self-hosted option puts the control plane in Baseten's cloud and the GPUs in your own AWS, GCP or Azure account. That is the right answer for a regulated company already on a hyperscaler. packet.ai's answer is a Dedicated single-tenant card in a known US or EU data centre with a 99% SLA, which is a different but equally direct residency story, without hyperscaler GPU prices attached.
Below the H100
Baseten's rate card jumps from small inference cards (T4, L4, A10G) to the A100 and H100. There is no 96GB Blackwell workstation card and no consumer card. packet.ai's RTX 6000 Pro at $0.66/hr serves a 70B model in 4-bit on a single card for about a tenth of a Baseten H100-hour; for teams whose models are mid-sized, that gap is the whole comparison.
Who should choose which
Choose packet.ai if
- Your model serves traffic most of the day and the managed premium has become the biggest line on the bill
- You have or want to build inference ops in-house
- Your model fits an RTX 6000 Pro, L40S or A100, which Baseten does not price competitively
- You want a Dedicated single-tenant card with a 99% SLA rather than a shared serving platform
Choose Baseten if
- You want production inference handled end to end by a specialist team
- Traffic is spiky and scale-to-zero saves you more than the per-GPU premium costs
- You need to deploy into your own hyperscaler account for compliance
- You want TensorRT-LLM tuning, canaries and traffic splitting out of the box
Conclusion
Baseten is an inference team you rent by the minute. packet.ai is the GPU you would otherwise be renting through them, at 40–60% less. Early on, or with spiky traffic, Baseten's managed layer earns its premium. At steady production volume the same B200 costs $2,700 a month on packet.ai instead of $7,185, and that is a hire.
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.