Modal pricing and alternatives: How to choose the right infrastructure for AI workloads

Modal's production workloads cost 3.75x advertised base rates due to regional and preemption multipliers. The $0.0000131 per CPU core base rate becomes $1,768.50 for 10,000 monthly CPU-hours compared to $471.60 at base rates.

This guide breaks down Modal's actual costs and compares alternatives across two distinct infrastructure needs: GPU-focused ML workloads and CPU-focused agent sandboxing.

What is Modal?

Modal is a Python-native serverless platform for AI workloads, especially GPU-accelerated ML workloads. Developers specify compute requirements through decorator syntax. Functions launch in 2 to 4 seconds and scale from single instances to 64 H100 GPUs.

Its key features include:

• Code-first infrastructure: Define compute requirements in Python using decorators like @app.function(gpu="A100") without YAML configuration files
• Sub-second cold starts: Container launches complete in under 1 second through specialized runtime optimization
• Elastic GPU scaling: Scale from zero to hundreds of GPUs instantly with automatic scale-to-zero to eliminate idle costs
• Extensive GPU access: Support for 9 GPU types from T4 ($0.59/hour) to B200 ($6.25/hour) without long-term commitments
• Multi-node training: Coordinate up to 64 H100 GPUs across nodes for distributed training jobs
• Per-second billing: Pay only for actual compute time consumed without minimum billing increments

Teams building ML applications choose Modal to eliminate infrastructure management overhead. The platform handles container orchestration, GPU allocation, and scaling automatically so engineers can focus on model development rather than DevOps. Python-first teams particularly value Modal's decorator-based approach that keeps infrastructure definitions alongside application code.

How does Modal pricing work?

Understanding Modal's pricing structure is essential before you commit to the platform. Hidden multipliers can increase your actual costs significantly beyond advertised rates.

Base compute rates

Modal operates on usage-based pricing with significant multipliers that compound actual costs beyond advertised base rates.

According to Modal's pricing page, as of January 2026, base compute rates are:

Critical cost multipliers

Modal’s production workloads cost more than advertised due to regional and non-preemption multipliers. Regional multipliers range from 1.25x (US/EU/UK/Asia-Pacific) to 2.5x (other regions). For non-preemptible US workloads, this results in a combined multiplier of 3.75x (1.25 × 3).

For example, 10,000 monthly CPU-hours cost $1,768.50 with multipliers, compared to $471.60 at base rates.

Subscription plans and credits

• Starter (free): $30 monthly credits, 3 workspace seats, 100 containers, 10 concurrent GPUs
• Team ($250/month): $100 monthly credits, unlimited seats, 1,000 containers, 50 concurrent GPUs

Sandbox pricing

Modal Sandboxes force non-preemptible pricing at $0.00003942 per CPU core per second, a 3x premium over the base $0.0000131 rate for general serverless functions. According to Modal's pricing page, this non-preemption requirement compounds costs for agent workloads that need persistent sandboxes.

The platform's memory snapshot feature remains in early preview without production-ready state persistence. If you're building production AI agents, you'll face an expensive choice with Modal: pay for 24/7 runtime to maintain state and avoid cold starts, or accept multi-second initialization delays on every agent interaction.

For a single 4-core, 16GB sandbox running continuously, Modal costs approximately $22.83 daily ($685 monthly) at non-preemptible rates when accounting for both CPU and memory charges. Memory snapshots are still in early-preview pre-beta, so users will likely keep sandboxes running to avoid longer cold-starts. This means you'll need to either carefully manage how you use sandboxes or consider platforms built for keeping sandboxes available long-term. Perpetual sandbox platforms (like Blaxel) address this by separating compute costs from state preservation and charging only for active runtime while maintaining sandboxes in zero-cost standby mode.

Who is Modal best for?

Modal optimizes for Python-centric ML teams with variable-demand workloads where serverless architecture and scale-to-zero create economic advantages. Batch ML inference, model training, real-time AI serving applications with bursty traffic, and data processing pipelines benefit from Modal's elastic GPU scaling and per-second billing.

Modal isn't suitable for teams needing 24/7 always-on inference, enterprises with strict VPC or BYOC requirements, teams primarily working in languages other than Python, or full-stack applications requiring integrated frontends.

GPU-focused Modal alternatives

These platforms compete directly with Modal for GPU inference, model training, and batch ML processing.

RunPod

RunPod positions itself as a cost-optimized alternative to enterprise GPU platforms for teams willing to accept some reliability trade-offs for significant price advantages. Unlike Modal's pure serverless approach, RunPod offers flexibility between persistent instances and serverless compute within a single platform.

Key features

• Dual compute model with persistent pods and serverless auto-scaling
• Access to 20+ GPU models from RTX 3090 to B200
• Per-second billing with spot, on-demand, and savings plan options
• Multi-tier storage optimization scaling to $0.05/GB/month

Pros

• GPU costs run 13–40% lower than Modal on mid-tier enterprise GPUs (A100, H100, L40S)
• User-friendly interface with responsive customer support
• Flexible pricing options including spot instances

Cons

• ML community describes RunPod as "not production worthy" based on verified practitioner feedback showing reliability concerns
• Occasional machine failures reported in customer reviews
• Complex LLM setup with documented configuration difficulties

Pricing

• Serverless GPU, pod-based, and storage pricing: See RunPod’s pricing page for the most up-to-date pricing information

Who is RunPod best for?

RunPod suits early-stage startups and research teams in the experimentation phase where GPU cost optimization outweighs production reliability requirements. Organizations with dedicated ML infrastructure engineers who can build monitoring and failover systems extract maximum value from the competitive GPU pricing.

Replicate

Replicate is an API-first ML deployment platform with a model marketplace and custom deployment capabilities. This abstracts hardware complexity from developers through simple REST API integration for the fastest path from idea to deployed inference when using existing models.

While Modal optimizes for custom code deployment, Replicate prioritizes instant API access to hundreds of production-ready models without infrastructure configuration.

Key features

• Extensive model marketplace with pre-trained models ready for deployment
• API-first architecture for easy integration across languages
• GitHub-based deployment with CI/CD support
• Fine-tuning capabilities built into infrastructure

Pros

• Simple integration enabling rapid deployment
• Extensive model library with community support
• Auto-scaling infrastructure for custom models

Cons

• Custom models experience 10+ minute cold start delays making platform unsuitable for latency-sensitive applications
• Verified user reviews caution against production deployment due to bugs
• GPU costs 100% higher than Modal (A100 at $5.04/hour vs. $2.50/hour)

Pricing

• Nvidia A100 (80GB), Nvidia H100, and model-specific pricing: See Replicate’s pricing page for the most up-to-date pricing information

Who is Replicate best for?

Replicate excels during prototyping when teams need to evaluate multiple pre-trained models quickly. Product teams at seed-stage startups testing different AI capabilities benefit from instant API access. Teams should plan migration before production given the cold start latency and cost premium.

Baseten

Baseten targets production-critical workloads where uptime guarantees and embedded engineering support justify premium pricing. The platform competes on reliability rather than cost, positioning itself for teams where inference downtime creates revenue risk.

Key features

• Proprietary Inference Stack with 99.99% uptime guarantee
• Cross-cloud high availability architecture
• Hybrid deployment supporting cloud and self-hosted environments
• Embedded engineering support with direct team access

Pros

• Fast model deployment requiring no DevOps expertise with autoscaling APIs
• 99.99% uptime SLA through cross-cloud architecture
• Self-hosted deployment options for compliance

Cons

• Pricing lacks transparency requiring sales engagement
• Limited ML pipeline features without data versioning
• No on-premises deployment option

Pricing

• Basic (free): Dedicated deployments, model APIs, SOC 2 Type II and HIPAA compliance
• Pro: Custom pricing
• Enterprise: Custom pricing
• Usage-based pricing: See Baseten’s pricing page for the most up-to-date pricing information

Who is Baseten best for?

Baseten targets Series B+ companies with mission-critical inference where revenue depends on AI availability. Organizations in regulated industries requiring vendor SLAs with penalty clauses justify premium pricing through risk mitigation.

Northflank

Northflank serves teams needing complete application infrastructure beyond serverless functions. While Modal focuses on ephemeral compute, Northflank provides traditional orchestration for long-running services, databases, and persistent workloads.

Key features

• DevOps lifecycle automation with CI/CD integration
• Multi-cloud and BYOC (Bring Your Own Cloud) flexibility
• Integrated database support for stateful applications
• Advanced release management with blue-green deployments

Pros

• Traditional orchestration for containerized applications
• BYOC deployment within existing cloud accounts
• Comprehensive infrastructure including databases

Cons

• Hourly billing more expensive for intermittent workloads
• BYOC requires infrastructure expertise and DevOps resources
• Comprehensive scope may over-engineer simple use cases

Pricing

• Usage-based pricing, including GPU options and network egress: See Northflank’s pricing page for the most up-to-date pricing information

Who is Northflank best for?

Northflank suits enterprises migrating legacy applications while maintaining traditional deployment patterns. Organizations with established DevOps teams needing databases, message queues, and long-running services fit Northflank's comprehensive platform.

CPU-focused Modal alternatives

AI agents executing code in production require different infrastructure than GPU ML workloads. Key evaluation criteria include state persistence duration, resume latency, security isolation model, and idle cost structure.

Blaxel

Blaxel is a perpetual sandbox platform built specifically for AI agents that need to execute code in secure production environments. It uses micro-VM technology that resumes from standby in under 25 milliseconds.

While Modal optimizes for GPU workloads with 2- 4-second cold starts, Blaxel offers CPU-based agent sandboxing where sub-second resume and indefinite state persistence enable real-time code execution by AI. The platform solves challenges serverless GPU platforms don't address. Agents making dozens of tool calls per session compound Modal's initialization overhead into user experience failures.

Key features

• Perpetual standby with infinite duration and zero compute cost during idle
• Sub-25ms resume maintaining complete filesystem and memory state
• Micro-VM isolation preventing container escape vulnerabilities
• Network-based auto-shutdown within 1 second when connections close
• Agent co-hosting eliminating network roundtrip latency

Pros

• Only sandbox provider allowing infinite standby duration (competitors cap at 30 days or delete sandboxes entirely)
• Resume times under 25 milliseconds enable real-time agent interactions
• Up to 5x cost savings through 15-second auto-suspend vs. in-minute minimum billing

Cons

• CPU-focused only, so it's not suitable for GPU workloads like model training or GPU inference of large models
• Supports only Python,TypeScript and Go, without Ruby, Java, or Rust support

Pricing

• Free: Up to $200 in free credits, no credit card required
• Pre-configured sandbox tiers and usage-based pricing: See Blaxel’s pricing page for the most up-to-date pricing information
• Available add-ons: Email support, live Slack support, HIPAA compliance

Who is Blaxel best for?

Blaxel suits AI-first companies at Seed stage through Series D building autonomous agents that execute code as their core product. Organizations deploying coding assistants, PR review agents, or data analysis agents where users expect instant responsiveness require Blaxel's sub-25ms secure architecture. Teams managing thousands of concurrent sessions benefit from perpetual standby economics.

Choose the right infrastructure for your AI agents

Modal and serverless alternatives solve GPU compute pricing, but AI agents executing code in production face distinct infrastructure requirements. The key evaluation criteria for agent infrastructure include:

• State persistence duration: How long can sandboxes remain available without activity?
• Resume latency: What's the delay when resuming from standby versus cold starting?
• Security isolation model: Does the platform use containers or micro-VMs?
• Idle cost structure: Are you charged for standby time or only active compute?

Teams building AI agents face different requirements. Perpetual sandbox platforms like Blaxel address agent-specific needs through micro-VM isolation with sub-25ms resume times and infinite standby duration. Modal excels at GPU-focused batch processing, while Blaxel targets CPU-based agent sandboxing with stateful persistence.

Note: Based on pricing information as of February 2026

Modal's memory snapshots remain in early development without general availability status, forcing production agents to run continuously at full compute rates to maintain state. Blaxel's 15-second auto-suspend transitions idle sandboxes to standby mode automatically and charges only for storage while preserving complete state.

Consider an AI agent processing user requests for 5 minutes daily. Modal charges $171.87/month (86,400 seconds × $0.0000663/sec daily for continuous runtime). Blaxel charges $62.30/month (300 seconds active compute + 86,085 seconds standby storage daily). This means Modal costs 176% more for identical workloads when state persistence is required. If your team runs hundreds of concurrent agent sessions, you’ll face this multiplier across your entire infrastructure.

Sign up for free to get $200 in free credits to compare Blaxel's sub-25ms resume times for your agent workloads, or book a demo to discuss how perpetual standby architecture reduces infrastructure costs for production AI agents executing code at scale.

FAQs about Modal pricing

How much does Modal actually cost compared to advertised pricing?

Modal's $0.0000131 per CPU core base rate (as of February 2026) compounds through multipliers. Non-preemptible US workloads cost 3.75x base rates: 1.25 (regional) × 3 (preemption) = 3.75x. For 10,000 CPU-hours monthly, the advertised base cost is $471.60, but actual production cost reaches $1,768.50 with multipliers applied.

What costs does Modal not publish on their pricing page?

Modal provides no pricing for Volumes distributed file storage despite this being a core platform feature. Data egress and transfer fees are similarly absent from official pricing pages. Organizations should request complete pricing documentation including storage costs and data transfer fees before committing.

Does Modal work for AI agents that execute code in production?

Modal Sandboxes’ lack of stable long-term persistence and container-based isolation create challenges for production AI agents. Agents making dozens of sandbox calls per session compound the latency overhead of gVisor, where each request adds multi-second delays overall that break real-time user experiences. Modal's architecture optimizes for GPU-accelerated batch processing rather than stateful CPU workloads.

For AI agents requiring near-instant responsiveness and indefinite state persistence, perpetual sandbox platforms like Blaxel provide micro-VM isolation with sub-25ms resume times and infinite standby duration. Modal remains the better choice for GPU inference, model training, or batch ML processing where 2- to 4-second initialization overhead is acceptable.