DreamPrompting
A recurring no-cost API allowance covering 7 cataloged models.
https://dreamprompting.com/api/v1Models mentioned
google/gemini-3.1-flash-litegroq/llama-3.3-70b-versatilenvidia/meta/llama-3.3-70b-instructopenrouter/google/gemma-4-31b-it:freemistral/mistral-small-latestcohere/command-a-03-2025chat/ch.atEquivalent paid value
How this was valued: The documented rolling 24-hour per-key pool of 500,000 tokens (treated as shared input+output) assigned entirely to output of the costliest currently listed caller-selectable model, cohere/command-a-03-2025, at Cohere's current first-party rate of $2.50 input / $10.00 output per 1M tokens. Request ceilings (5,000 requests/24h, 100 requests/minute per IP, 32,000 max input and 8,192 max output tokens per request) never bind. The API documents explicit provider/model selection, so the prior unknown-router-mix objection no longer applies; cohere/command-r-plus-08-2024, also listed live, carries the identical $2.50/$10.00 first-party rate and yields the same maximum. Assumes uninterrupted saturation and upstream availability of this young aggregator's donated/free upstream capacity.
Limits and terms
- Requests Per Minute Per Ip
- 100
- Tokens Per Rolling 24 Hours
- 500,000
- Requests Per Rolling 24 Hours
- 5,000
- Max Input Tokens
- 32,000
- Max Output Tokens
- 8,192
What happens to your prompts?
Prompts can be stored or publicly shared and user content may be used to improve the service without a gateway-specific no-training promise.
- Plan Scope
- DreamPrompting website, public prompt-sharing platform, and free multi-provider API gateway.
- Prompt Retention
- The privacy policy describes submitted prompts as stored and, for sharing features, publicly visible; it does not state a separate API-gateway prompt TTL.
- Response Retention
- not documented
- Ordinary Logging
- IP, device, page, account, and usage information is collected; API content logging is not specifically disclosed.
- Model Training
- No service-wide no-training commitment was found for API inputs or outputs.
- Product Improvement
- Collected information and user content may be used to provide and improve the service.
- Human Or Operator Access
- Stored/shared prompt content is accessible to the service and can be public; gateway upstreams process API requests.
- Subprocessors And Routing
- The gateway advertises routing across multiple providers with automatic failover, so the selected upstream's policy also applies.
- Deletion Controls
- Users can remove submitted public prompts and request account deletion; no upstream API-request deletion control is documented.
- Caveat
- The legal pages focus heavily on the public prompt-sharing product and do not cleanly distinguish gateway traffic. Do not assume public-prompt deletion covers routed API content.
Governing documents
- Terms Of Service
- https://dreamprompting.com/terms
- Privacy Policy
- https://dreamprompting.com/privacy
Eligibility
- Account Required
- Yes
- Payment Method Required
- No
Primary sources
Before you build with DreamPrompting
Read the classification narrowly
This record has current first-party evidence for some zero-cost hosted inference. The label describes the bounded offer supported by evidence on 2026-09-19; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every region.
Resolve the live model route
This snapshot records 7 model IDs. Match the exact ID against the provider's live catalog before using it in code; zero-price routes and aliases can rotate while an older documentation page remains online. The recorded base endpoint is https://dreamprompting.com/api/v1, but the provider's current API reference remains authoritative for paths, authentication, and request shape.
Confirm account and billing boundaries
An account is required, so public catalog evidence cannot by itself prove the limits, balance, or billing behavior attached to your workspace. The reviewed evidence explicitly says no payment card is required. Re-check the signup flow because eligibility and billing controls can change after the snapshot. The equivalent paid value shown here prices a documented allowance where possible; it is not cash, guaranteed savings, or protection from overage.
Re-read the prompt policy for your plan
This record classifies the reviewed handling as “Not private”: Prompts can be stored or publicly shared and user content may be used to improve the service without a gateway-specific no-training promise. Provider policies can distinguish free, paid, enterprise, opted-in, and feature-specific traffic, so verify the governing terms for the exact account and route that will receive your data.
Follow the evidence, then re-check it
The record links 7 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest governing document or live catalog when sources disagree, and submit a correction when a provider changes a material term.
Match the quota to the workload shape
Translate the published allowance into the traffic pattern you actually expect instead of comparing headline totals alone. A daily token pool can look generous while a low requests-per-minute or concurrency ceiling blocks interactive bursts; a high request limit can still fail a long-context job when input, output, or per-request tokens are capped. Separate prompt tokens from generated tokens, include retries and tool calls, and test the largest realistic payload. If the provider documents more than one limit window, the tightest window at your peak load is the practical ceiling. Research, experimental, and community access can also carry eligibility or fair-use constraints that cannot be modeled as a simple number.
Plan fallback without changing the rules
A fallback should preserve more than API syntax. Confirm that the substitute route supports the required modality, context length, streaming behavior, structured output, tools, and safety controls, then compare its prompt-retention and training terms. An OpenAI-compatible request shape does not make providers operationally or contractually equivalent. Decide which errors may trigger a retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. If deterministic output matters, record the model revision and sampling settings; rotating aliases and free-model pools can change behavior even when the endpoint remains available.
Monitor the offer as a dependency
Capture the model ID, response model field, rate-limit headers, usage fields, latency, HTTP status, and any provider request identifier for each test. Watch for authorization failures, quota exhaustion, catalog removal, policy revisions, and dashboard balance changes as separate failure modes. Re-check the provider’s live catalog and governing pages on a schedule proportionate to the workload’s importance, and keep the dated sources that supported your decision. Free capacity is especially suitable for prototypes, evaluations, fallbacks, and bounded workloads when the application can tolerate change; a production dependency still needs observability, an exit path, and an owner responsible for re-verification.
Test one complete request before scaling
Start with the smallest permitted request using the exact credential, model ID, endpoint, and account type you intend to deploy. Record the HTTP status, response headers, usage fields, latency, and any dashboard balance change. Then exercise the failure path: an invalid model, an exhausted quota, or a rate-limit response should fail clearly without silently switching to a paid route. If the provider supports streaming or tool calls, validate those features separately because a free model can expose a narrower capability set than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and avoid sending sensitive data until the observed route matches the reviewed data agreement.