AI & Models · Integration
Llama 2
Add Llama 2 to your product for your customers, and give your AI agents governed access to it.
Embed a Llama 2 integration so your customers can run generation on their own Llama 2 deployment rather than on one shared key you have to meter, rotate and defend. Credentials are held per tenant, and the model, prompt template and parameters are configuration rather than code, so a customer who wants different settings is a mapping change instead of a release. Every call is logged per tenant, which means a generated output can be traced back to the request and the customer that produced it. fastn handles credential storage, retries and API upkeep, and the same connection is what your AI agents call when they need generation.
In your product
Embedded for your customers. Per-tenant auth, no per-customer code, maintained by fastn.
Add a Llama 2 backed drafting or summarising feature to your product, with each customer's own endpoint credentials held per tenant.
Let a customer pick Llama 2 for a feature and change model later without you shipping new client code.
Build prompts from records your product already holds so generated text is grounded in that customer's own data.
Keep per-tenant request logs so model usage and cost are attributable to the customer that caused them.
For your AI agents
Governed, audited access for the agents you build, through the MCP server.
An agent uses Llama 2 for generation while its access to every other connected system stays scoped and audited.
An agent compares a Llama 2 response with another model's before it acts, with both calls logged per tenant.
An agent drafts content with Llama 2 and files it through another connected system in the same governed run.
Example prompt
Draft a release summary with Llama 2 from these tickets, then post it to the channel I name.
Set up Llama 2 in 4 steps
- 01Enable the Llama 2 connector in your fastn dashboard.
- 02Have each customer supply credentials for their own Llama 2 endpoint so calls run on their deployment.
- 03Set the model, prompt templates and parameters per customer as configuration.
- 04Call it from your product and expose the same connection to your agents through the MCP gateway.
Why teams use the Llama 2 integration
What you get by embedding it with fastn instead of building it yourself.
- Ship a Llama 2 integration without building it. Your customers connect their own Llama 2 account inside your product and work their models, requests and outputs there, with no per-customer code on your side.
- Handle the part that actually costs time: per-customer keys, quotas and cost attribution matter more than schema here, because every call is billed. fastn owns the auth, token refresh, rate limits, pagination and breaking-change fixes, so a Llama 2 update is not your on-call problem.
- One integration serves your product and your agents. The same governed Llama 2 connection powers in-product features and gives AI agents scoped, audited access, so you give your product and your agents a model call each customer pays for themselves without wiring it twice.
Used by these teams
Compare with
Hugging Face
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AmplitudeLlama 2 integration FAQ
How do I add a Llama 2 integration to my product?
Enable the Llama 2 connector in your fastn dashboard, then let each customer authenticate their own Llama 2 account. fastn handles the OAuth flow, token storage and refresh per tenant, so there is no Llama 2 client code in your app and no per-customer branch in your codebase. Setup is 4 steps.
Do my customers each connect their own Llama 2 account?
Yes. Every connection is scoped to the individual customer, so each authorises their own Llama 2 account and only ever sees their own models, requests and outputs. That per-tenant isolation is the point of an embedded integration: you support the long tail of customer setups without maintaining an integration per customer.
Can AI agents use this Llama 2 integration?
Yes. The same connection is exposed to your agents through the fastn MCP gateway, with permissions scoped per tenant and every call audited. An agent uses Llama 2 for generation while its access to every other connected system stays scoped and audited.
Who maintains the Llama 2 integration?
fastn does. When Llama 2 changes an endpoint, deprecates a field or alters its auth, the fix lands in the connector rather than in your backlog, and your customers' connections keep working.
Whose Llama 2 API key and quota does each call use?
Each customer authorises their own Llama 2 account, so usage, rate limits and cost land on the customer that caused them. You are not metering a shared key and re-billing it, and one heavy customer cannot exhaust another's quota.
Are inputs and outputs auditable?
Yes. Every call is logged per tenant with the call, the input and the result, so an output can be traced back to what produced it. That matters more here than in most integrations, because a generated answer or an extracted field cannot be reconstructed from the request alone.
What can I build with the Llama 2 integration?
A common starting point: add a Llama 2 backed drafting or summarising feature to your product, with each customer's own endpoint credentials held per tenant. Teams also use it for the other use cases listed above, and expose it to agents for governed reads and writes.
How much does the Llama 2 integration cost?
It is included. Pricing is based on connected accounts, not on how many connectors you enable, so adding Llama 2 does not change your per-connector cost. You can start free with 3 connected accounts.
Add Llama 2 to your product
Start free with 3 connected accounts. No sales call required, and no per-customer integration code.