Groq

Overview

What kind of product Category ?

Inference provider Not verified

Marketplaces resell many providers behind one key. Gateways add governance on top. Open-source projects you run yourself. Cloud platforms are hyperscaler surfaces. Inference providers host models on their own hardware.

Who runs it Deployment model ?

Managed only Not verified

Managed means the vendor operates it. Self-host means you run it on your own infrastructure. Both means you can choose.

Licence Licence ?

Proprietary Not verified

Proprietary products cannot be inspected or forked. Open licences such as MIT and Apache-2.0 let you audit, modify, and run the code without permission.

Company Company ?

Groq Verified 2 days ago

The organisation that maintains the product.

Who you would be signing with Vendor status ?

Independent company Not verified

Whether the product is still an independent company, has been acquired, is a large cloud vendor’s product line, is run by a software foundation, or has been put into maintenance mode. Maintenance mode means bug fixes and security patches only — no new features.

Last shipped an update Latest release ?

2026-04-18 Not verified

Most recent GroqCloud changelog entry. Four months without a public update is an outlier among the actively developed products here.

The date of the most recent release or version tag. A product that has not shipped in a year is a different risk from one that shipped last week, regardless of what its marketing site says.

GitHub stars GitHub stars ?

Not published Not verified

A rough proxy for community size on open-source projects. Not a quality measure.

Cost

Markup on model prices Token markup ?

Not published Not verified

How much the product adds on top of what the underlying model provider charges. Zero means you pay the same per-token price you would pay the model provider directly.

Fee to add funds Credit purchase fee ?

Not published Not verified

A percentage charged when you top up your balance, separate from token prices. It is easy to miss because it does not appear on the per-token price list.

Monthly cost per person Seat fee ?

Not published Not verified

A recurring per-user platform charge that applies regardless of how much you use the models.

Can use your own provider accounts BYOK supported ?

Not published Not verified

Bring Your Own Key: you keep direct contracts with OpenAI, Anthropic and others, and the gateway only routes traffic. This preserves negotiated rates and committed-spend discounts.

Cost of using your own accounts BYOK terms ?

Not published Not verified

What the product charges to route traffic through your own provider keys.

Free tier Free tier ?

Free tier with per-model rate limits (for example gpt-oss-120b at 30 requests/min, 1,000 requests/day, 8,000 tokens/min); the paid Developer plan raises this to about 1,000 RPM and 250,000 TPM and adds Batch and Flex processing. Verified 2 days ago

What you can do without paying, useful for evaluation.

Enterprise plan from Enterprise plan from ?

Not published Not verified

Annual entry price for the enterprise tier, where one is published or credibly reported.

Cost to run it yourself Self-host cost ?

No self-host option; Groq sets its own per-token prices on its own LPU/LPX hardware rather than marking up another vendor's list price. Representative rates: gpt-oss-120b $0.15 input / $0.60 output per 1M tokens, gpt-oss-20b $0.075/$0.30, Qwen3.6-27B $0.60/$3.00. Verified 2 days ago

What self-hosting actually costs once you account for infrastructure and any paid tier.

How the vendor makes money Pricing model ?

Bundled — inference is the product Not verified

The shape of the vendor’s bill: does the routing layer charge a percentage on top of tokens, a flat monthly fee, both, neither (because inference is the product), or nothing at all (open source with no paid tier).

How pricing works, briefly Pricing model detail ?

Pay-for-own-inference on a free/Developer/Enterprise ladder; pay-as-you-go per token with progressive billing. No separate gateway or platform fee — routing/API layer is $0. Not verified

A one-paragraph description that covers the caveats a pricing category cannot: introductory rates, per-feature meters, tier gating, and pricing that resets on a specific date.

Minimum commitment Minimum commitment ?

None stated. Bills in arrears; progressive billing thresholds at $1, $10, $100, $500 and $1,000 (India: $1, $10, then recurring $100). $0.50 minimum before billing. Not verified

Whether the vendor requires a minimum contract term, a minimum spend, or a provisioned-capacity purchase to get its published rate.

Charges that fire after you go over an allowance Overage terms ?

None stated. Downgrading stops usage charges immediately and returns the account to Free tier rate limits and restrictions. Not verified

The line items that scale with usage after an included allowance is exhausted — log storage, extra requests, per-feature meters, data export — which is where cost estimates usually go wrong.

Prompt cache offered Cache mechanism ?

Prefix cache Not verified

Whether the gateway offers its own response cache, what kind of match it does (exact request, prefix, semantic), or simply passes provider caching through unchanged.

Discount on cached input Cache-read discount ?

50% Not verified

How much cheaper cached tokens are than fresh input, when the vendor publishes a single figure. Bundled-inference clouds usually price this per model instead of as one number.

Premium on cache writes Cache-write premium ?

None Not verified

How much more the first write of a cached prefix costs versus a plain input token. A high write premium and a low hit rate can leave you paying more than you save, so this matters as much as the read discount.

Who captures the cache saving Cache economics ?

Prefix prompt caching, free to use. 50% discount on cached input tokens; pricing discount applies only on successful cache hits. No cache-write charge. Clean win for the customer — no feature fee, no write premium — with one caveat: batch requests already receive a 50% discount on all tokens, no additional discount applied to cached tokens in batch requests. The two discounts do not stack. Not verified

Whether the customer keeps the full saving from caching or the vendor captures part of it — and any conditions attached (write premium, storage fees, best-effort hits).

What you can split spend by Cost attribution ?

Not stated. Not verified

The dimensions the vendor documents for splitting spend — per key, per user, per team, per tag, per customer. Matters if you need to chargeback internally or bill an end customer.

How you get cost data out Cost export ?

Not stated. Not verified

The mechanisms the vendor publishes for exporting cost and usage data: CSV, an API, webhooks, S3, a data warehouse, or nothing at all. Any per-unit price is included.

Who pays the model bill BYOK mode ?

Their credits only Not verified

Whether you bring your own provider accounts (BYOK), buy inference from this vendor, or can do either. This is the single biggest commercial difference between these products: it decides who holds the contract with the model provider and who carries the spend.

Catalog

Models available Models available ?

6–16 Not verified

How many models you can call, shown as a range because several vendors publish different totals on different pages. Vendor-reported either way, so counts are not directly comparable — some count every provider variant of the same model separately.

Model providers reachable Upstream providers ?

Not published Not verified

How many distinct model providers or labs you can reach, shown as a range where the vendor’s own pages disagree. More providers usually means better redundancy when one has an outage.

Works with standard OpenAI code OpenAI-compatible API ?

Yes Verified 2 days ago

If yes, you can usually switch to it by changing one base URL, and switch away just as easily. This is the main defence against lock-in.

OpenAI chat endpoint POST /v1/chat/completions ?

Yes Not verified

The endpoint almost every application ports first. "Not documented" means the vendor never states it, which is different from a documented no.

Anthropic messages endpoint POST /v1/messages ?

Not documented Not verified

Whether Anthropic-shaped calls work without rewriting them. Several products support this only as SDK compatibility or provider passthrough rather than a native endpoint — the detail page says which.

OpenAI Responses endpoint POST /v1/responses ?

Yes Not verified

The newer stateful OpenAI surface. Support is much thinner across this market than chat completions.

Embeddings endpoint POST /v1/embeddings ?

Not documented Not verified

Whether you can generate vectors through the same gateway, or need a second integration for retrieval workloads.

Image generation endpoint POST /v1/images/generations ?

Not documented Not verified

Whether image models are reachable through the same surface as text.

Audio endpoints POST /v1/audio/* ?

Yes Not verified

Speech-to-text and text-to-speech. Frequently the first gap in an otherwise complete gateway.

Batch jobs endpoint POST /v1/batches ?

Yes Not verified

Asynchronous bulk processing, usually at a discount. Commonly undocumented, and commonly the reason a migration stalls late.

Needs the vendor’s own code library Requires a vendor-specific SDK ?

No Verified 2 days ago

A proprietary client library spreads through your codebase and has to be torn out again if you leave. “No” is the better answer here, and it means the standard OpenAI client works.

You can export your request history Logs / usage data export ?

Not published Not verified

Whether you can get your own request logs, traces, or usage records back out — through an API, a bulk export, or a download. Decides whether you leave with your history or abandon it.

Settings can live in version control Declarative config-as-code ?

No Verified 2 days ago

Whether routing, fallback, and budget rules can be declared in a file you keep in Git, rather than existing only as settings clicked into a hosted dashboard.

Embeddings Embeddings ?

Not published Not verified

Text-to-vector models, needed for search and retrieval features.

Image generation Image generation ?

Not published Not verified

Whether image models are reachable through the same interface.

Speech and audio Speech and audio ?

Yes Verified 2 days ago

Text-to-speech or transcription models through the same interface.

Video generation Video generation ?

Not published Not verified

Whether video models are reachable through the same interface.

Batch processing Batch processing ?

Yes Verified 2 days ago

Submitting large jobs for cheaper, slower processing. Often 50% off for work that is not time-sensitive.

Routing & reliability

Uptime it promises in writing Contractual SLA uptime ?

Not published Not verified

The uptime percentage in a published, contractual service level agreement. A public status page is not an SLA — it reports what happened, it does not promise anything or pay you back when it breaks.

Automatic failover Automatic failover ?

Not published Not verified

When a model provider goes down or rate-limits you, traffic moves to a backup automatically instead of returning errors to your users.

Load balancing Load balancing ?

Not published Not verified

Spreads requests across several providers or keys to raise your effective rate limit.

Rule-based routing Conditional routing ?

Not published Not verified

Send different requests to different models based on rules — for example a cheap model for free users and a strong model for paying ones.

Response caching Response caching ?

Not published Not verified

Reuses the answer when the exact same request comes in again, which cuts both cost and latency.

Similar-question caching Semantic cache ?

Not published Not verified

Reuses an answer when a new question means roughly the same thing as an earlier one. Saves far more than exact-match caching but can return subtly wrong answers if tuned loosely.

Where you set the timeout Request timeout surface ?

Not documented Not verified

`not_documented` on the fetched Groq docs ([Groq text generation docs](https://console.groq.com/docs/text-chat)).

Where a request timeout can be set: per request, in a config file, in the vendor dashboard, or nowhere. Nine of the twenty products documented here do not describe a request timeout at all, so the worst case of a hung upstream call is unknowable from the docs.

Where you set retries Retry policy surface ?

Not documented Not verified

`not_documented`; default count and backoff: `n.a.` ([Groq text generation docs](https://console.groq.com/docs/text-chat)).

Where retry count and backoff are configured. Worth knowing alongside billing: a retried streaming call can be charged more than once.

Where you set fallbacks Fallback surface ?

Not documented Not verified

`not_documented` ([Groq OpenAI compatibility](https://console.groq.com/docs/openai)).

Where the fallback chain is defined. Almost every product claims fallback; the useful question is whether you can change it from code or only by hand in a dashboard.

Shape of the fallback chain Fallback shape ?

Shape not documented Not verified

An ordered list tries targets in sequence; a weighted split sends a percentage of traffic to each, which is what you need to trial a new model on 5% of requests. Weighted splits are much rarer than the marketing implies.

Upstream health tracking Health checks / circuit breaking ?

Not documented Not verified

`not_documented` ([Groq OpenAI compatibility](https://console.groq.com/docs/openai)).

Whether the product notices a failing upstream and stops sending traffic to it, and whether you can tune the thresholds. This is what turns a provider outage into a blip rather than a sustained error rate, and only four of the twenty expose it.

Cross-region failover you control Multi-region failover surface ?

Not documented Not verified

`not_documented` ([Groq OpenAI compatibility](https://console.groq.com/docs/openai)).

Whether you can define what happens when a region degrades. A vendor running many regions is not the same as a vendor letting you configure failover between them; only three document a user-controlled mechanism.

Where you set load balancing Load balancing surface ?

Not documented Not verified

`not_documented` ([Groq text generation docs](https://console.groq.com/docs/text-chat)).

Where traffic distribution across upstreams or keys is configured.

Operations

Usage dashboards and logs Observability ?

Not published Not verified

Built-in visibility into what was sent, what came back, what it cost, and how long it took.

Spending limits Budget controls ?

Not published Not verified

Hard caps that stop spend before it becomes a surprise invoice. The single most valuable control for a small team.

Rate limits Rate limits ?

Yes Verified 2 days ago

Caps on request volume per key or per user, useful for protecting against abuse and runaway loops.

Separate keys per team or app Virtual keys ?

Not published Not verified

Issue scoped keys with their own budgets and permissions so you can attribute cost and revoke access without rotating everything.

Prompt versioning Prompt management ?

Not published Not verified

Store and version prompts outside your code so they can be changed without a deploy.

Quality testing Evals ?

Not published Not verified

Built-in tooling to score model output against test cases, so you can tell whether a model swap made things better or worse.

MCP support MCP support ?

Not published Not verified

Native support for the Model Context Protocol, the emerging standard for connecting models to external tools.

What gets logged Logged content ?

Metadata only Not verified

`metadata_only` — request metadata is always retained; customer prompt/completion data is not retained by default, though abuse-detection logs may be held for up to 30 days ([Your data](https://console.groq.com/docs/your-data))

Whether full prompts and responses are stored, only metadata, or nothing. Full-body logging is the most useful debugging feature here and the one most likely to need a conversation with your compliance team.

You can turn logging off Body-logging opt-out ?

Yes Not verified

Zero Data Retention is available through Data Controls; metadata retention is not optional ([Your data](https://console.groq.com/docs/your-data))

Whether prompt and response bodies can be suppressed while still keeping usage metrics. Nineteen of the twenty document a way to do this; the mechanisms range from a per-request header to an organisation-wide setting.

Traces you can take elsewhere Distributed tracing ?

OpenTelemetry Not verified

Via third-party OTel instrumentation: Arize Phoenix tracing is documented for Groq calls ([Arize + Groq](https://console.groq.com/docs/arize))

Whether the product emits OpenTelemetry, a proprietary format, or nothing. OpenTelemetry means the traces land in the tooling you already run instead of only in the vendor’s dashboard.

Where telemetry can go Export destinations ?

Not published Not verified

Observability integrations are client-side: Arize, MLflow, and LangSmith ([Integrations](https://console.groq.com/docs/integrations)); Arize Phoenix specifically for OTel traces ([Arize + Groq](https://console.groq.com/docs/arize))

Documented sinks for logs and metrics. This is a good proxy for how replaceable the vendor’s own dashboard is: around twenty destinations means you never have to depend on it, while a CSV download means you do.

Can record user feedback Feedback capture API ?

No Not verified

`n.a.` ([Integrations](https://console.groq.com/docs/integrations))

Whether there is an API to attach a rating or score to a logged request, which is what lets production traffic feed quality work later.

Scores live traffic Online eval hooks ?

No Not verified

`n.a.` from Groq; evaluation happens in the connected observability platform ([Integrations](https://console.groq.com/docs/integrations))

Whether automated scorers can run against real production requests, rather than only against a test set you assemble yourself.

Performance

Delay it adds Proxy overhead ?

Not published Not verified

Extra time the product itself adds to each request, on top of however long the model takes. Usually irrelevant next to multi-second model latency, but it matters for high-volume or streaming-sensitive workloads.

Requests per second ceiling Throughput ?

1,000 rps Verified 2 days ago

Published sustained request rate before the product becomes the bottleneck. Only relevant at genuinely high volume.

What the request path runs on Architecture class ?

Undisclosed vendor service Not verified

`vendor_saas` on the software side, with disclosed custom hardware: the LPU Inference Engine served through GroqCloud and the Groq API. No proxy runtime is disclosed ([Groq LPU independent benchmark](https://groq.com/newsroom/groq-lpu-inference-engine-leads-in-first-independent-llm-benchmark)).

The comparable way to talk about latency here. An edge worker, a compiled Go or Rust binary, and a Python proxy have different overhead floors no matter which figures each vendor publishes. Products that never disclose their runtime are recorded as undisclosed rather than assumed.

You can run the request path yourself Self-hostable data plane ?

No Not verified

Only client SDKs are distributed (`pip install groq`, Groq Python and Groq TypeScript libraries) ([Groq text generation docs](https://console.groq.com/docs/text-chat), [Groq OpenAI compatibility](https://console.groq.com/docs/openai)).

Whether the component that actually carries your prompts can run on your own infrastructure. Distinct from a vendor offering a self-hosted control plane while still proxying traffic through their network.

Streaming responses Streaming support ?

Yes Not verified

Supported via `stream=True`, returning "an iterator of completion deltas rather than a single, full completion"; async streaming supported with `AsyncGroq`. Non-streaming caveats on the OpenAI-compatible surface: `logprobs`, `logit_bias`, `top_logprobs` and `messages[].name` are unsupported and return `400`; `N` must equal `1`; `temperature: 0` is converted to `1e-8`; audio transcription/translation do not support `vtt` or `srt` ([Groq text generation docs](https://console.groq.com/docs/text-chat), [Groq OpenAI compatibility](https://console.groq.com/docs/openai)).

Whether token-by-token streaming is documented. The caveats matter more than the yes: some products cannot cancel a stream without still being billed, and several timeout and fallback mechanisms stop applying once the first token has been sent.

Security & compliance

Does your prompt reach their servers Prompt transits vendor ?

Yes Not verified

Hosted inference. Where retention does occur, the data sits in Google Cloud buckets in the United States.

Whether the text you send passes through this company’s own infrastructure. If it does, every other promise on this page is a policy commitment rather than a physical impossibility. Self-hosted products can answer no outright.

What they keep if you change nothing Logging default ?

Nothing stored by default Not verified

Groq does not retain customer data for inference requests by default. Usage metadata is collected for all users and contains no inputs or outputs. Temporary logging happens only to troubleshoot reliability-degrading errors or investigate suspected abuse.

Defaults matter more than options. A product that stores full prompts and replies unless you find the right header will have stored them by the time you read the docs.

How long they keep it Default content retention (days) ?

Nothing kept by default Not verified

Zero by default. Exceptions: up to 30 days for the reliability and abuse logs, 30 days for batch inputs and outputs, and fine-tuning artifacts held until you delete them.

Default retention for request content, in days. Zero means nothing is kept. Read the note: several products keep nothing as a rule but make timed exceptions for abuse review or specific models.

Could they train on your prompts Training on customer data ?

Not published — silence, not a no Not verified

The data page is silent, and the privacy policy explicitly routes the question to the services agreement and data-processing addendum. Read that as silence, not a no.

Whether the vendor may use your prompts and outputs to train models. “Not published” means we could not find any position, which is not the same as a no — ask for it in writing.

Where it runs, and what you can pin Region and residency control ?

Retained data is US-only, in Google Cloud buckets. Inference itself spans 13 data centres on four continents including Finland, the UK, Saudi Arabia and Australia, but no customer region pinning is documented. Not verified

Which regions are offered and whether you can force processing to stay in one. A global endpoint that silently picks a region is a different compliance story from an endpoint you pin yourself.

Where safety filters run Guardrail execution location ?

No guardrails offered Not verified

No Groq-operated guardrail or PII-redaction feature was found.

A filter that strips personal data only helps if it runs before the data leaves your boundary. If guardrails execute in the vendor’s cloud, the vendor has already received whatever you wanted redacted.

Who else touches the data Subprocessor list ?

https://trust.groq.com/subprocessors Not verified

The published list of third parties the vendor passes your data to. No list means you cannot know the full chain, which most data-protection agreements require you to.

SOC 2 audited SOC 2 audited ?

Yes Verified 2 days ago

An independent audit of security controls. Enterprise buyers and their procurement teams routinely require it.

Will sign a HIPAA agreement HIPAA BAA ?

Not published Not verified

Required before you may send protected health information through the service. Without a signed BAA, healthcare data is off limits.

GDPR commitments GDPR commitments ?

Not published Not verified

Published data processing terms for handling personal data of people in the EU and UK.

Can keep data in the EU EU data residency ?

No Verified 2 days ago

Requests can be processed inside the EU rather than routed to US infrastructure. Often the deciding constraint for European customers.

Does not retain your data Zero data retention ?

Yes Verified 2 days ago

Prompts and responses are not stored after the request completes. Sometimes a paid add-on rather than the default.

Strips personal data PII redaction ?

Not published Not verified

Detects and removes identifiers such as names, emails, and card numbers before the request reaches the model provider.

Content guardrails Content guardrails ?

Not published Not verified

Policy checks on inputs and outputs — blocking unsafe content, enforcing formats, or catching prompt-injection attempts.

Runs fully disconnected Air-gapped deployment ?

Not published Not verified

Can be deployed in a network with no internet access, which some regulated and defence environments require.

Blocks personal data in prompts PII / DLP enforcement ?

Not documented Not verified

`not_documented` — no PII guardrail feature ([Your data](https://console.groq.com/docs/your-data))

Whether personal data detection sits on the request path and can stop the call, merely inspects and forwards it, or is not documented. A control that only reports is a logging feature, not a policy control.

Blocks prompt injection Injection / jailbreak enforcement ?

Not documented Not verified

Llama Prompt Guard 2 is offered as a model you call yourself ([Content moderation](https://console.groq.com/docs/content-moderation))

Whether injection and jailbreak detection can stop a request. Most products offering this call a partner classifier rather than shipping their own.

Blocks harmful content Toxicity / moderation enforcement ?

Not documented Not verified

`not_documented` as an inline platform control — moderation is available only as callable models: GPT-OSS-Safeguard 20B, Llama Guard 4, and Llama Prompt Guard 2. The application must make a second call and act on the verdict; Groq does not filter the request path ([Content moderation](https://console.groq.com/docs/content-moderation))

Whether hate, violence, sexual and self-harm categories are checked inline and can stop a request, in either direction.

Your own policy rules Custom policy hooks ?

Not documented Not verified

GPT-OSS-Safeguard accepts a custom policy in its prompt, but this is a model capability rather than a gateway policy engine ([Content moderation](https://console.groq.com/docs/content-moderation))

Whether you can add your own rule — a regex, a webhook, or your own classifier — rather than choosing from the vendor library.

Where guardrails run Guardrail execution location ?

Not documented Not verified

Whether guardrail evaluation happens inside your infrastructure or on the vendor’s servers. This decides whether the prompt you are trying to protect leaves your network in order to be checked.

If the guardrail itself fails Guardrail failure mode ?

Not documented Not verified

`not_documented` ([Content moderation](https://console.groq.com/docs/content-moderation))

What happens when the guardrail service times out or errors: does the request proceed unchecked, or is it blocked? This is the worst-documented field in the entire catalogue — only two vendors state it plainly, which means most teams are running a control whose failure behaviour they cannot know.

Third-party guardrail vendors Guardrail integrations ?

Not published Not verified

Named external guardrail services the product can call. A long list means the product is a router for policy engines rather than a policy engine itself — which also means another vendor bill and another hop.

Fit & integration

Work to try it Evaluation work shape ?

Install a package Not verified

The shape of the work on the vendor’s own quickstart, from swapping one base URL through to deploying infrastructure. An ordinal class rather than a duration, because elapsed time depends on accounts and quota we cannot see.

Work to run it Production work shape ?

Install a package Not verified

The same scale applied to the vendor’s recommended production path. For several products this is much heavier than the quickstart, which is exactly why both are recorded.

Steps on the quickstart Numbered quickstart steps ?

0 Not verified

The quickstart is prose plus a code sample with no numbered procedure.

A literal count of numbered steps on the vendor’s quickstart, recorded as evidence beside the work shape. Zero means the page publishes no numbered procedure at all. Large counts usually mean interleaved language tracks rather than more work.

Can you self-host it today Self-host install documentation ?

No self-hosting Not verified

Whether an install command is actually published. Several products advertise self-hosting while publishing no command to start from, which a plain yes/no would hide.

Works with the OpenAI SDK OpenAI SDK drop-in ?

Yes Not verified

Yes. Groq supports OpenAI’s client libraries when configured with the base URL `https://api.groq.com/openai/v1` and a Groq API key. ([Groq OpenAI compatibility docs](https://console.groq.com/docs/openai))

Whether an existing OpenAI-compatible client can be pointed at it by changing the base URL and key.

Vercel AI SDK support AI SDK provider package ?

Official provider package Not verified

Yes. Groq provides Vercel AI SDK integration through `@ai-sdk/groq` and `ai`; the provider can be imported from `@ai-sdk/groq` and used as `groq('llama-3.3-70b-versatile')`. ([Groq AI SDK integration docs](https://console.groq.com/docs/ai-sdk/))

Whether a first-party AI SDK provider package exists, or only a community package, a documented workaround, or the generic OpenAI provider pointed at a custom base URL.

Python framework integrations Documented Python frameworks ?

LangChain, LlamaIndex Not verified

Groq documents LangChain integration through `langchain-groq` and the `ChatGroq` class. LlamaIndex integration is also documented for Python and JavaScript, with the LlamaIndex page additionally documenting installation of LlamaIndex. ([Groq LangChain integration docs](https://console.groq.com/docs/langchain))

LangChain, LangGraph, LlamaIndex and similar orchestration frameworks with a documented integration.

Callable from Cloudflare Workers Cloudflare Workers support ?

Documented by Cloudflare, not the vendor Not verified

Documented by Cloudflare, not Groq: Groq is a supported Cloudflare AI Gateway provider, with base URL `https://gateway.ai.cloudflare.com/v1/{account_id}/{gateway_id}/groq` replacing `https://api.groq.com/openai/v1`; no Workers binding is documented on that page. ([Cloudflare AI Gateway - Groq provider](https://developers.cloudflare.com/ai-gateway/usage/providers/groq/))

Whether the docs show calling this product from your own Worker. Deliberately separated from the several products whose own gateway runs on Workers, which is a fact about their infrastructure and not about your edge compatibility.

Kubernetes install Helm chart availability ?

Not documented Not verified

n.a. (not documented)

Whether a named, published Helm chart exists, versus Helm being referenced with no chart named, versus generic cluster documentation that has nothing to do with this product.

Terraform support Terraform provider or modules ?

Not documented Not verified

n.a. (not documented)

Whether you can declare this in code: an official provider, official modules, resources inside a hyperscaler’s provider, a community provider, or only Terraform code shipped in a repo.

Reuses your cloud identity Cloud IAM reuse ?

Not documented Not verified

n.a. (not documented)

Whether you can authenticate with IAM roles, workload identity or managed identities instead of another long-lived API key. Distinguished from products that only accept static upstream provider credentials.

Fits behind your API gateway API gateway integration ?

Not documented Not verified

n.a. (not documented)

Whether AI traffic can go through a gateway you already run, and who documented that — the product’s vendor or the gateway’s.

MCP support MCP surface shape ?

MCP tools in the API Not verified

Yes. Groq’s Responses API supports remote MCP over HTTPS using tools such as `type: "mcp"`, `server_label`, `server_url`, `headers`, `require_approval`, and `allowed_tools`; remote MCP is currently in beta. Groq also documents a `groq-mcp` MCP server package usable with Claude Desktop and other MCP clients. ([Groq remote MCP docs](https://console.groq.com/docs/tool-use/remote-mcp))

Which kind of MCP support this is: a gateway that governs many MCP servers, a hosted MCP server you connect to, MCP tools accepted inside the completion API, or client tooling. These are different products behind one acronym.

Needs your own provider key Upstream provider key required ?

Not needed Not verified

No. The first call uses `GROQ_API_KEY` ([Quickstart - GroqDocs](https://console.groq.com/docs/quickstart))

Whether an upstream provider account and key must exist before your first call works. A prerequisite rather than a step, and it can differ between the hosted and self-hosted forms of the same product.

Gate before models work Model access gate ?

Not documented Not verified

n.a. (not documented) ([Quickstart - GroqDocs](https://console.groq.com/docs/quickstart), [Rate Limits - GroqDocs](https://console.groq.com/docs/rate-limits))

Whether an enablement click, a quota grant, a paid tier or an approval form stands between a valid key and a working model call.

Official client languages First-party client SDK languages ?

Python, TypeScript Not verified

Groq provides official Python and JavaScript/TypeScript libraries: Python package `groq` with the `Groq` class, and JavaScript/TypeScript package `groq-sdk` with the `Groq` class; both offer synchronous and asynchronous clients. Groq is also compatible with OpenAI’s client libraries, and its documentation shows Vercel AI SDK packages `ai` and `@ai-sdk/groq`. ([Groq client libraries docs](https://console.groq.com/docs/libraries))

Languages with a first-party client library. An empty list can still mean the product is usable from any language via an OpenAI-compatible SDK.

Additional charges

These are the fees that do not appear on a per-token price list, and they are where estimates usually go wrong.

• Speech-to-text (whisper-large-v3) $0.111 per hour of audio ($0.04/hour for v3-turbo)
• Text-to-speech (Orpheus) $22-$40 per 1M characters