A Formula 1 engine is an engineering marvel. But no one wins a race with an engine on a test bench. It needs a chassis, a driver, a pit crew, and a team reading the telemetry.
Baseten builds some of the fastest engines in AI. Its motto is "inference is everything," and the market agrees. Its annualized revenue reportedly tripled in the first quarter of 2026 to around $600M, and in June it raised a reported $1.5B at a valuation of up to $13B. Notion, Harvey, Cursor, and HubSpot run on it.
If your hardest problem is serving a model fast, Baseten is built for you.
But if you're shipping an agent, the model is the engine. You still need the rest of the car.
Baseten's strengths
Credit where it's due. Baseten is excellent at:
- Deploying your own models. Package any open-source, fine-tuned, or custom model and serve it on dedicated, autoscaling GPUs.
- Performance. Its Inference Stack is tuned for low latency across LLMs, transcription, speech, image generation, and embeddings.
- Model APIs. Pre-optimized endpoints for open models like GLM, DeepSeek, and Kimi.
- Flexible deployment. Baseten Cloud, self-hosted in your VPC, or hybrid with flex capacity on Baseten Cloud.
- Training. SFT and reinforcement learning through its Loops SDK.
- Compliance. SOC 2 Type II and HIPAA.
If you have an ML platform team and custom models to serve at scale, that's a strong stack.
The rest of the car
1. Frontier and open models, one key
Baseten's Model APIs focus on open models. For closed frontier models, you need a separate account, a separate key, and a separate bill.
Most production agents use both. On Vivgrid, gpt-6-astra, claude-opus-5, and gemini-3.8-flash sit next to deepseek-v4-pro, glm-5.3, and kimi-k3 — one OpenAI-compatible endpoint, one API key. Use a frontier model where quality matters and an open model where cost does.
2. Your agent needs tools, not just models
Baseten Chains is great for compound AI: chaining several models into one pipeline, such as transcription, then summarization, then embeddings.
An agent needs a different kind of step. It has to look up an order, update a ticket, or send an email. Those are tools, and on a pure inference platform they live in your application code.
Managed Skills moves them to the cloud. Write a typed Function Tool or MCP Tool, run viv deploy, and every agent calls the new version right away. API keys live with the skill, not in your client. Every call shows up in your logs.
3. Change the agent without a release
Prompts and models live on the Vivgrid server, not in your app. Your API calls don't even need a model field. Swap a model or fix a prompt in the console, and every agent picks it up on the next request.
4. Test the agent, not just the endpoint
A load test tells you your endpoint handles 1,000 requests per second. It doesn't tell you your agent still answers the refund question correctly after a prompt change.
Alchemist runs recursive and regression evaluations on real agent behavior before every prompt change, tool update, or model swap goes live.
5. Telemetry for the business
GPU utilization, latency percentiles, and replica counts keep an endpoint healthy. They don't tell you what customers are asking or where the agent lets them down.
Observability & Insights gives you real-time request logs, latency breakdowns, and cost per request. On Growth and Enterprise, Agent Insights shows what customers ask, what it costs, and where the agent wins.
6. Hybrid by sensitivity, not just capacity
Baseten's hybrid mode is about capacity: run on your own infrastructure and burst to Baseten Cloud when you need more.
Vivgrid Enterprise BYOC is about data. Connect your own GPU cluster, and Vivgrid automatically detects requests containing sensitive data and routes them there. Everything else keeps using the frontier and open models you've chosen.
Sensitive data stays on hardware you control, without writing the routing logic yourself.
Cold starts and scaling: who owns the problem?
When you deploy your own model on Baseten, you also own its capacity plan. You set the minimum and maximum replicas, a concurrency target, and how long to wait before scaling down.
Every setting is a trade-off:
- Keep a replica warm, and you pay for GPUs that sit idle overnight.
- Scale to zero, and the first request after a quiet period waits for a replica to boot and load weights.
- Set the concurrency target too high, and latency climbs under load. Set it too low, and you pay for replicas you don't need.
Baseten has put serious engineering into making cold starts fast, and its Model APIs take this off your plate for the open models they host. The knobs come back as soon as you deploy weights of your own.
Agents make the trade-off harder. One agent turn can call a planning model, a cheap model for routine steps, and an embedding model, and each dedicated deployment needs its own capacity plan.
On Vivgrid, there's no capacity plan. You don't pick GPUs, size replicas, or schedule warm-up traffic. You pick a model in the console and call one endpoint.
If you need to serve your own weights, Baseten's control is the point. If you don't, it's work you can skip.
Baseten vs. Vivgrid, feature by feature
| Baseten | Vivgrid | |
|---|---|---|
| Built for | Deploying and scaling your own models | Building and running production AI agents |
| Models | Your models + open-model APIs | Frontier (OpenAI, Anthropic, Google) + open-weight, one key |
| Composition | Chains: pipelines of models | Managed Skills: Function and MCP tools, deployed with viv |
| Operations | You configure deployments and autoscaling | Fully managed, no model servers |
| Prompt and model changes | In your application code | Server-side, no client release |
| Evaluation | Bring your own | Alchemist, for release-gating agent behavior |
| Insights | Deployment and performance metrics | Request logs, cost per request, Agent Insights |
| Hybrid | Self-hosted with flex capacity on Baseten Cloud | BYOC with automatic sensitive-request routing |
| Custom model serving | Yes | Not offered |
| Training | SFT and RL via Loops | Not offered |
On shared open models, per-token prices are in the same range. As of September 2026:
| Model | Baseten (input / output per 1M) | Vivgrid (input / output per 1M) |
|---|---|---|
| GLM 5.3 | $1.40 / $4.40 | $1.20 / $4.20 |
| Kimi K3 | $3.00 / $15.00 | $3.00 / $15.00 |
Some models are cheaper on Vivgrid and some are cheaper on Baseten. Don't choose on price. Choose on what you're building.
When to pick which
Choose Baseten if you're serving your own models. You have custom or fine-tuned weights, strict latency targets, and a platform team that wants control over every deployment.
Choose Vivgrid if you're shipping an agent. You want frontier and open models on one key, tools that deploy without an app release, and proof that every change works before customers see it.
Baseten builds a great engine. Vivgrid gives you the whole car, and the pit crew.
Vivgrid is the Managed Skills platform: cloud-hosted LLM function calling that lets enterprise AI agents run their tools in the cloud — with observability, evaluation, and globally distributed inference built in. Start with the Quick Start, see pricing, or talk to us at hi@vivgrid.com.
Baseten details are based on Baseten's public website, pricing page, and press coverage as of August 22, 2026. Product names are trademarks of their respective owners.
FAQ
Is Vivgrid a Baseten alternative?
For teams shipping agents, yes. Baseten deploys and scales your own models. Vivgrid runs the agent: frontier and open models on one key, cloud-hosted tools, and evaluation before release, with no deployment to manage. If serving custom weights is the job, Baseten is the better fit.
Can I deploy my own model on Vivgrid?
No. Vivgrid serves a catalog of frontier and open models and does not host custom or fine-tuned weights. Baseten is built for that.
Do I configure replicas or autoscaling on Vivgrid?
No. There are no replicas, GPU types, or autoscaling settings to manage. You pick a model in the console and call one OpenAI-compatible endpoint.
What is the difference between Baseten Chains and Vivgrid Managed Skills?
Chains composes several models into one pipeline, such as transcription, then summarization, then embeddings. Managed Skills hosts the tools an agent acts with: typed Function Tools and MCP Tools that look up orders, update tickets, or send email, deployed with viv deploy.
How is Baseten hybrid different from Vivgrid BYOC?
Baseten hybrid is about capacity: run on your own infrastructure and burst to Baseten Cloud when you need more. Vivgrid Enterprise BYOC is about data: it detects requests that contain sensitive data and routes them to your own GPU cluster, while everything else uses the models you have chosen.