Beam vs Replicate (2026): Pricing, Features & Verdict

Why choose Beam

• Higher Customization score (8/10 vs 6/10)
• Higher Cost score (7/10 vs 5/10)

Why choose Replicate

• Higher Integration score (7/10 vs 5/10)
• Higher Analytics score (5/10 vs 4/10)

Beam

Pros

• Billing is per second, or per millisecond for sandboxes, with no egress fees and no compute charge while VMs are stopped. Storage was not yet billed at the time of research.
• Beta9, the core runtime, is open source under AGPL-3.0, so teams can self-host it or add their own hardware using the same CLI across cloud, on-prem and hybrid setups.
• Agent-focused sandboxes start in about half a second and can be paused, snapshotted, forked and given a GPU. Paused VMs resume with RAM and processes intact.
• GetDeploying's comparison found Beam's H100, H200, B200, L40S, RTX PRO 6000 and A6000 machine prices cheaper than Runpod's.
• Beam publishes a SOC 2 Type II report, and GetDeploying lists GDPR coverage through a DPA and HIPAA availability on the enterprise tier.

Cons

• Beam lists 10 GPU models, compared with 34 on Runpod in GetDeploying's comparison, and Runpod is cheaper on A100 and RTX 4090.
• In GetDeploying's comparison, a medium always-on VM (4 vCPU, 8 GB) cost about $289 a month on Beam versus about $86 on Runpod.
• Beam does not publish a list of data center regions, so buyers with residency needs must ask sales.
• The entry Developer plan is capped at 1 seat and 5 concurrent GPUs, and live chat support starts only on the $89 Team plan.
• Independent user reviews are scarce, and the vendor is very small (YC lists a team of 5), which may concern buyers who need long-term support guarantees.

Replicate

Pros

• Thousands of ready-to-run community and official models can be called through one API, and Cloudflare cites more than 50,000 production-ready models.
• Public models charge only for active processing time, so prototypes cost nothing while idle.
• The open-source Cog tool packages custom models for upload, and the docs cover webhooks, streaming output and fine-tuning.
• Deployments give private endpoints that autoscale from zero, with minimum and maximum instance caps to keep warm capacity or limit spend.
• Per-second hardware pricing is published openly for every GPU type, from T4 to 8x H100.

Cons

• Cold starts on shared public and community models are the most cited complaint, with reviews reporting delays of tens of seconds or more for custom models.
• Private models and deployments bill for setup and idle time, so latency-sensitive production use costs much more than the list per-second rates suggest.
• Dedicated GPU rates, such as 5.49 USD per hour for an H100, are higher than many raw GPU clouds, according to competitor comparisons.
• No SOC 2 report or public trust center was found, and enterprise SLA terms are not published.
• Bills are hard to forecast because costs vary by model, hardware and billing method, and some canceled runs are still charged.