Baseten
FreemiumUltra-low-latency inference platform for custom AI models
By Tanmay Verma, Founder · Last verified 05 Jul 2026
5.2k views
Added 4/3/2026
95/100Safe BetIn short
Baseten — Ultra-low-latency inference platform for custom AI models. Best for Engineering teams deploying custom LLMs or GenAI models at scale, Companies requiring sub-300ms latency for real-time transcription or voice agents, Organizations needing multi-cloud deployment with hybrid flexibility. Free to use.
Compared withvs Together Ai
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Editorial Verdict
Best for
Engineering teams deploying custom LLMs or GenAI models at scaleCompanies requiring sub-300ms latency for real-time transcription or voice agentsOrganizations needing multi-cloud deployment with hybrid flexibilityTeams building compound AI systems with granular hardware controlBusinesses looking to monetize their own models via Frontier Gateway
Not ideal for
Hobbyists or small teams prototyping with low trafficTeams seeking low-cost, pay-per-token inference API with transparent pricingUsers who need a plug-and-play solution without deep infrastructure managementApplications that can tolerate higher latency or less frequent scalingTeams that need a built-in vector database or RAG pipeline
Baseten delivers on its promise of ultra-low-latency inference with deep customization, but its enterprise focus and opaque pricing put it out of reach for small teams. If you need sub-300ms response times and have the budget, it's a strong pick. Otherwise, consider alternatives with transparent pay-per-token pricing.
Skip Baseten if Skip Baseten if you need a simple, low-cost, transparent pay-per-token API without managing infrastructure or paying for dedicated compute.
Last verified: July 2026
What's new in Baseten
Checked 4 days agoAcross the latest 9 updates: 6 feature updates and 3 launches.
LaunchChangelog·8 days agoNewest
Try the new baseten CLI
New CLI for the whole Baseten model workflow: deploy, call models, stream logs, manage deployments.
FeatureChangelog·9 days ago
Connect coding agents to Baseten
Connect coding agents via MCP server and Baseten skill to manage workspace from agent.
FeatureChangelog·10 days ago
Configure scale-down rate
Cap autoscaler scale-down rate between 1% and 50% per deployment.
FeatureChangelog·14 days ago
Log downloads
Download deployment logs as CSV/JSON for up to 7 days from the dashboard.
FeatureChangelog·15 days ago
Model Deprecation (DeepSeek v3.1, MiniMax m2.5)
DeepSeek v3.1 and MiniMax M2.5 Model APIs deprecated June 24.
FeatureChangelog·17 days ago
Filter and stream model logs from the CLI
truss model-logs now supports filters: --since, --start, --end, --min-level, --includes.
LaunchChangelog·23 days ago
Kimi K2.7 Code available on Baseten
Kimi-K2.7-Code available via OpenAI-compatible Model API and dedicated deployments.
LaunchChangelog·23 days ago
GLM 5.2 available on Baseten
GLM 5.2 available via OpenAI-compatible Model API and dedicated deployments.
FeatureChangelog·27 days ago
New sidebar navigation
Rolled out new sidebar nav across Models, Chains, Model APIs, Training for easier navigation.
Viability Score
95/100
Safe BetHow likely is Baseten to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
100
funding runway
80
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Dedicated inference with GPU selection (T4 to B200)
- Pre-optimized Model APIs with OpenAI-compatible endpoints (GLM 5.2, Kimi K2.7 Code, DeepSeek V4)
- Real-time audio streaming for text-to-speech
- Baseten Embeddings Inference with 2x throughput improvement
- Baseten Chains for compound AI with hardware autoscaling
- Frontier Gateway for model monetization
- One-click training deployment to inference
- Self-hosted and hybrid (VPC + cloud) deployments
- CLI log filtering with time window and log level
- Container restart tracking in metrics dashboard
- Native vLLM and SGLang metrics
- Log export to OTLP endpoints (Datadog, Grafana)
- Log download as CSV or JSON from dashboard
- Sub-300ms latency for transcription and voice
- MCP server integration for coding agents
About Baseten
FreemiumAdvancedAPI availableWeb · API · CLI
Baseten is a high-performance inference platform built for engineering teams deploying custom, open-source, and fine-tuned AI models in production. It leverages proprietary optimizations like custom kernels, advanced caching, and cross-cloud high availability to deliver sub-300ms latency for demanding GenAI applications. The platform serves customers such as Abridge, Cursor, Notion, and Writer, and recently raised a $1.5B Series F at a $13B valuation. Key features include Dedicated Inference with GPU selection (T4 to B200), Pre-optimized Model APIs (GLM 5.2, Kimi K2.7 Code, DeepSeek V4) with OpenAI-compatible endpoints, and Baseten Chains for compound AI with hardware autoscaling. The platform also offers real-time audio streaming for text-to-speech, Baseten Embeddings Inference with 2x throughput, and a Frontier Gateway to monetize custom models. Deployment options span Baseten Cloud, self-hosted VPC, or hybrid. Recent updates have expanded the developer experience: a new CLI enables deploying, calling, and streaming logs; coding agents can connect via MCP server; and you can now configure scale-down rates and download logs as CSV/JSON. The platform also added native vLLM/SGLang metrics and log export to OTLP endpoints like Datadog and Grafana. Compared to alternatives like Replicate or Together AI, Baseten focuses on deep infrastructure control and enterprise-scale reliability. Its opaque usage-based pricing and enterprise orientation make it less suitable for hobbyists or small projects, but for teams needing sub-300ms latency and granular control over inference, it is a top-tier choice.
Behind the Verdict
We'd reach for Baseten when latency is non-negotiable—think real-time voice agents, transcription, or interactive coding assistants. The platform's proprietary optimizations and cross-cloud redundancy are real differentiators for high-scale production deployments. The recently raised $1.5B round signals strong market conviction, and the steady stream of developer experience improvements (new CLI, MCP connection for coding agents, log downloads) shows they're investing where it matters. Where it bites: pricing is usage-based and not transparent beyond the Basic tier. You'll need to talk to sales for Pro or Enterprise, and the per-minute GPU pricing (e.g., $0.10833/min for H100) can add up fast if you don't manage autoscaling tightly. The platform also lacks a built-in vector database or RAG pipeline, so you'll need to integrate those separately. The closest alternative is Together AI, which offers similar model APIs with transparent per-token pricing and a more accessible free tier. However, Together AI doesn't match Baseten's sub-300ms latency guarantees or self-hosted deployment options. For hobbyists or teams with under 1M tokens/month, Baseten's basic tier (free pay-as-you-go) is worth exploring, but the real value comes at scale with dedicated deployments. In practice, the new CLI is a game-changer for dev workflows—deploy, call models, and stream logs from the terminal. The ability to connect coding agents via MCP server is also forward-thinking. Just be prepared for the enterprise sales process if you need volume discounts or self-hosted deployments.
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Real-world workflow fit
Concrete scenarios for the personas Baseten actually fits — and what changes day-one when you adopt it.
ML Engineer
You fine-tune a Llama model on custom data and want to deploy it in production with low latency.
Outcome: Deploy with a dedicated deployment in your Baseten cloud or VPC, using the CLI and Truss. Monitor latency and throughput via native vLLM metrics. Achieve sub-300ms response times.
Startup CTO
Your product requires real-time transcription for thousands of concurrent users.
Outcome: Use Baseten's optimized Whisper deployment with auto-scaling. Pay only per minute of compute. Integrate with Datadog for observability. Achieve consistent sub-200ms transcription.
Use Cases
- Deploy a fine-tuned LLM for a customer-facing chatbot with sub-300ms latency
- Serve real-time image generation with ComfyUI workflows
- Run transcription and speaker diarization at scale
- Monetize a custom model through the Frontier Gateway
- Train a model on GPU instances and deploy in one click
- Migrate from on-prem inference to a self-hosted VPC environment
- Build compound AI systems with granular hardware control (Baseten Chains)
- Stream ultra-low-latency text-to-speech for voice agents
Models Under the Hood
GLM 5.2Kimi K2.7 CodeDeepSeek V4
as of 2026-07-06
Limitations
Primarily designed for developers and ML engineers; manages custom model deployment but does not include a built-in development IDE; pricing for high-volume pre-optimized Model APIs can be significant; some GPU types may require access requests; self-hosted deployment requires substantial engineering effort.
as of 2026-06-29
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
Free
Billed monthly
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Baseten tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Basic
$0/mo, pay as you go
Ideal for
Solo developers or small teams experimenting with model deployment and low-volume production
What this tier adds
Starting tier with $0/month base; pay-as-you-go for compute and Model API usage; includes email/in-app chat support.
Pro
Volume discounts
Ideal for
Scaling teams that need priority GPU access, dedicated compute, and hands-on engineering support
What this tier adds
Adds priority access to high-demand GPUs, dedicated compute, higher Model API rate limits, Slack/Zoom support, and volume discounts.
Enterprise
Custom
Ideal for
Large organizations requiring custom SLAs, self-hosted deployments, advanced compliance, and global regions
What this tier adds
Adds custom SLAs, self-host and hybrid deployment, on-demand flex compute, bring-your-own-cloud commitments, advanced RBAC, and custom global regions.
Integrations
DatadogGrafana Cloud
Where the pricing makes sense
The company stage and team size where Baseten's pricing actually pencils out — and where peers do it cheaper.
Baseten's pricing fits scaling startups and enterprises with dedicated inference needs and volume discounts. Per-minute GPU pricing (e.g., $0.108/min for H100) is competitive with cloud GPUs but adds convenience. Compared to simpler APIs like OpenAI, Baseten gives you more control. Compared to self-managed vLLM, you pay a premium for managed infrastructure.
Setup time & first value
How long it actually takes to get something useful out of Baseten — broken out by persona, not the marketing-page minute.
For ML engineers familiar with Truss: first dedicated deployment in under 30 minutes. Model API (e.g., GLM 5.2) works in minutes with an OpenAI-compatible endpoint. Self-hosted setup in your VPC may take a few days with Baseten's forward-deployed engineers.
Switching to or from Baseten
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
- →From self-hosted vLLM: package your model as a Truss container and deploy on Baseten Cloud or your VPC with minimal code changes.
- →From OpenAI API: swap the endpoint URL to Baseten's Model API for supported models; adjust authentication to use your Baseten API key.
Migrating out
- ↗To self-hosted vLLM: export model artifacts and configuration, then deploy on your own GPU cluster.
- ↗To another inference provider (e.g., Replicate, Together AI): redeploy using their SDK; note that custom optimizations may not transfer.
Resources & Guides
Frequently Asked Questions
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