A hardware store sells you everything you need to build a house: lumber, pipes, wiring, and every tool. It doesn't build the house.
Together AI is one of the best hardware stores in AI. In July 2026 it raised $800M at an $8.3B valuation, with more than $1B in annual bookings. Cursor, Cognition, ElevenLabs, and Salesforce run on it.
If you need GPUs, fast open-source inference, or a place to fine-tune, Together is a great place to shop.
But if you're shipping an agent, the question isn't only where the parts come from. It's who helps you build, test, and run the house.
What Together AI does best
Together calls itself the AI Native Cloud, and it earns the name:
- Fast open-source inference. Serverless, batch, provisioned throughput, and dedicated endpoints for models like DeepSeek, GLM, Kimi, and MiniMax.
- Research-driven performance. Custom kernels and optimizations that push speed up and cost down.
- GPU clusters. From a single instance to thousands of GPUs, billed by the hour.
- Fine-tuning. Supervised fine-tuning and DPO on open-source models.
- Building blocks. Code sandboxes and managed storage with no egress fees.
If your team wants raw infrastructure and has engineers ready to assemble it, that's a strong foundation.
Where Vivgrid is different
1. Frontier and open models, one key
Together's catalog is built around open-source models. That's its mission, and it keeps costs low.
Most production agents still need a frontier model for their hardest steps, though. 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 GPT or Claude for planning and an open model for the high-volume steps, without a second vendor, a second bill, or a second SDK.
2. Sandboxes run code. Managed Skills run your tools.
Together's sandboxes are a safe place for an agent to run code it just wrote. That's useful for coding agents and data analysis.
Most enterprise agents need something else: reliable access to the systems your business already runs, like your CRM, billing, or ticketing. That code shouldn't be improvised. It should be typed, reviewed, and versioned.
Managed Skills is built for that. Write a 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 shipping the app
On a raw inference API, your prompts, tool definitions, and model choice live in application code. Every change is a release.
On Vivgrid, they live on the server. Your API calls don't even need a model field. Pick the model in the console, update a prompt, and every agent picks up the change on the next request.
4. Know it works before customers do
A cloud can tell you a model is fast. It can't tell you your agent still refunds the right orders after this morning's prompt change.
Alchemist runs recursive and regression evaluations on real agent behavior before every prompt change, tool update, or model swap goes live.
5. See the business, not just the GPUs
Infrastructure metrics tell you about tokens, throughput, and utilization. Your product team needs different answers: what customers ask, what each conversation costs, and where the agent falls short.
Observability & Insights gives you real-time request logs, latency breakdowns, and cost per request. On Growth and Enterprise, Agent Insights turns that traffic into product decisions.
6. Private GPUs, only where they matter
Vivgrid Enterprise supports BYOC: connect your own GPU cluster, and Vivgrid automatically detects requests containing sensitive data and routes them to it. Everything else keeps using the frontier and open models you've chosen.
Sensitive data stays on hardware you control. Every other request still gets frontier quality.
Side by side
| Together AI | Vivgrid | |
|---|---|---|
| Built for | Running, training, and fine-tuning open models | Building and running production AI agents |
| Models | Open-source library | Frontier (OpenAI, Anthropic, Google) + open-weight, one key |
| Agent tools | Code sandboxes; bring your own tool hosting | Managed Skills: Function and MCP tools, deployed with viv |
| Prompt and model changes | In your application code | Server-side, no client release |
| Evaluation | Bring your own | Alchemist, for release-gating agent behavior |
| Insights | Usage and infrastructure metrics | Request logs, cost per request, Agent Insights |
| Private compute | Dedicated endpoints and GPU clusters | BYOC with automatic sensitive-request routing |
| GPU clusters | Yes, up to thousands of GPUs | Not offered |
| Fine-tuning | SFT, DPO | Not offered |
On shared open models, per-token prices are in the same range. As of September 2026:
| Model | Together AI (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 Together. Don't choose on a few cents per million tokens. Choose on what you're building.
Which one should you choose?
Choose Together AI if you're building on infrastructure. You need GPU clusters, want to fine-tune open models, or run high-volume open-source inference with a team that owns the stack around it.
Choose Vivgrid if you're building 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.
Together sells great parts. Vivgrid helps you build the house and keep it running.
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.
Together AI details are based on Together AI's public website, pricing page, and funding announcement as of July 7, 2026. Product names are trademarks of their respective owners.