{ "data": { "a": { "slug": "axolotl", "name": "Axolotl", "vendor": "Axolotl AI", "vendorUrl": "https://axolotl.ai", "kind": "framework", "category": "fine-tuning", "summary": "Open-source command-line tool and Python package for fine-tuning open language models from one YAML config, covering LoRA, QLoRA, full fine-tuning, preference tuning and GRPO on the owner's GPUs.", "url": "https://www.anchorterminal.com/tools/axolotl", "markdownUrl": "https://www.anchorterminal.com/tools/axolotl.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/axolotl.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/axolotl.json", "repo": "https://github.com/axolotl-ai-cloud/axolotl", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "pypi", "name": "axolotl" }, { "registry": "oci", "name": "axolotlai/axolotl" } ], "auth": "none", "authNotes": "No account or key of its own. Gated models and Hub uploads use the owner's Hugging Face token, and Weights \u0026 Biases, MLflow or Trackio logging uses those services' own credentials from the environment.", "pricing": "free", "pricingNotes": "Free and Apache-2.0, with no price list or hosted plan on axolotl.ai. The owner pays for the GPU, whether local, a rented machine or Hugging Face Jobs billed by the minute. Dedicated support is by email with no published price.", "priceSummary": "Free · OSS", "where": "library", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 12541, "npmWeekly": null, "pypiWeekly": 2124, "asOf": "2026-10-08" }, "docsUrl": "https://docs.axolotl.ai/", "capabilities": [ "finetune.sft", "finetune.lora", "finetune.preference", "finetune.rl", "finetune.export" ], "tags": [ "open-source", "framework", "self-hosted", "local", "free", "python", "docker", "open-weights" ], "lastRelease": "2026-09-30", "graded": true, "anchor": { "graded": true, "score": 64.8, "grade": "B", "agentReady": false, "rank": 307, "ranked": true, "rankOf": 842, "categoryRank": 2, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 60, "maintenance": 88, "payments": 60, "reliability": 64, "schema": 80, "security": 52, "transparency": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "Axolotl runs a whole fine-tuning job from one YAML file and ships a JSON Schema of its config plus bundled agent docs. It is 0.x software with telemetry on by default, no terms or privacy policy, and the owner supplies the GPU.", "bestFor": "A team that wants a repeatable, config-driven fine-tune of an open model on its own or rented GPUs, including multi-GPU and multi-node runs.", "strengths": [ "Apache-2.0, free, and the weights stay on the owner's hardware", "`axolotl config-schema` prints the full config as JSON Schema, and `axolotl agent-docs` prints bundled Markdown references by topic", "SFT, LoRA, QLoRA, DPO, IPO, KTO, ORPO, GRPO and reward modelling from one config format", "Three releases in the 90 days to 8 October 2026, each with a Deprecations section naming removed options", "Telemetry is documented field by field and `AXOLOTL_DO_NOT_TRACK=1` turns it off" ], "weaknesses": [ "Telemetry to PostHog is on by default and delays training start by 10 seconds until the variable is set either way", "No terms of service, privacy policy, legal entity or security.txt found on axolotl.ai", "Version 0.20.0, with removals in minor releases (FSDP1 in 0.20.0, `relora_steps` renamed in 0.17.0 with no shim)", "Three of the last six push runs of the Tests workflow on main passed, and the nightly run against upstream failed on 7 and 8 October 2026", "Not a hosted service, so there is no job API, status page or SLA" ], "agentNotes": [ "Set `AXOLOTL_DO_NOT_TRACK=1` before any command, or training waits 10 seconds and sends usage events to PostHog", "Run `axolotl agent-docs` and `axolotl config-schema --field \u003cname\u003e` before writing a config; both work offline from the installed package", "Install torch first, then `uv pip install --no-build-isolation axolotl[deepspeed]`, on Python 3.12 or later with PyTorch 2.13 or later", "Take example configs from the same release tag as the installed version; minor releases remove and rename config keys", "Resume an interrupted run with `axolotl train config.yml --resume-from-checkpoint \u003cpath\u003e`, then `axolotl merge-lora` and `axolotl export` only when shipping" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.8 } ], "editorialScores": { "ergonomics": 60, "maintenance": 88, "payments": 60, "reliability": 64, "schema": 80, "security": 52, "transparency": 75 }, "provenanceScore": 36 }, "connect": { "install": "uv pip install torch==2.14.0 torchvision \u0026\u0026 uv pip install --no-build-isolation axolotl[deepspeed] # or: docker run --gpus '\"all\"' --ipc=host --rm -it axolotlai/axolotl:main-latest", "headless": { "command": "axolotl train config.yml", "env": { "AXOLOTL_DO_NOT_TRACK": "1" } } }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/axolotl" }, "area": "models", "provenance": { "legalEntity": "", "domain": "axolotl.ai", "domainRegistered": "2022-08-02", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/axolotl-ai-cloud/axolotl/releases", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "No legal entity is named on axolotl.ai, in the docs or in the repository. The GitHub organisation is axolotl-ai-cloud and the citation file credits the Axolotl maintainers and contributors.", "The vendor publishes no terms of service or privacy policy (axolotl.ai/terms and /privacy return 404), so both links are left out. The telemetry page is the only data-handling statement.", "Local software has no endpoint to check against the domain.", "axolotl.ai/.well-known/security.txt and /security.txt return 404. The repository's `.github/SECURITY.md` gives an email address for reports.", "RDAP shows axolotl.ai registered on 2 August 2022 and transferred on 11 April 2024." ], "score": 36 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/axolotl.json" }, "answer": "Axolotl scores 64.8 (B) on agent readiness against Fireworks AI Fine-tuning's 59 (C), and leads in 4 of 7 scored categories. Fireworks AI Fine-tuning leads on agent ergonomics, security \u0026 auth and transparency \u0026 trust.", "b": { "slug": "fireworks-fine-tuning", "name": "Fireworks AI Fine-tuning", "vendor": "Fireworks AI", "vendorUrl": "https://fireworks.ai", "kind": "http-api", "category": "fine-tuning", "summary": "Managed supervised, preference and reinforcement fine-tuning for open models, with a training API for custom workflows.", "url": "https://www.anchorterminal.com/tools/fireworks-fine-tuning", "markdownUrl": "https://www.anchorterminal.com/tools/fireworks-fine-tuning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/fireworks-fine-tuning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json", "repo": "https://github.com/fw-ai-external/python-sdk", "license": "Apache-2.0 (SDK)", "transports": [ "http" ], "remoteUrl": "https://api.fireworks.ai", "packages": [ { "registry": "pypi", "name": "fireworks-ai" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with an account key, read from `FIREWORKS_API_KEY` by the SDK and `firectl`. The Training API wants a training-scoped key. Resources are addressed as accounts/\u003caccount\u003e/..., and the SDK resolves the account from the key.", "pricing": "usage", "pricingNotes": "Managed training per 1M training tokens by model size. LoRA SFT $0.50 up to 16B parameters, $3 from 16.1B to 80B, $6 from 80B to 300B, $10 above; DPO is double, and full-parameter training is double LoRA. Serving a fine-tuned model costs the same as the base model. The serverless Training API is priced per model (Qwen 3.8 27B at $4.103 per 1M training tokens); dedicated training is $8 a GPU-hour for an H100 or H200, $13 for a B200, $15 for a B300 and $20 for a GB300, effective 2026-09-01. On-demand inference deployments cost $8 an hour for an H100 or H200 and $13 for a B200. New accounts get $1 of credit (https://fireworks.ai/pricing).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 7, "npmWeekly": null, "pypiWeekly": 290162, "asOf": "2026-09-30" }, "docsUrl": "https://docs.fireworks.ai/fine-tuning/fine-tuning-models", "llmsTxt": "https://docs.fireworks.ai/llms.txt", "openapi": "https://docs.fireworks.ai/merged.openapi.yaml", "capabilities": [ "finetune.sft", "finetune.preference", "finetune.rl", "finetune.lora", "finetune.export" ], "tags": [ "hosted", "usage-priced", "card-required", "open-weights", "llms-txt", "python", "async-jobs" ], "lastRelease": "2026-10-01", "graded": true, "anchor": { "graded": true, "score": 59, "grade": "C", "agentReady": false, "rank": 506, "ranked": true, "rankOf": 842, "categoryRank": 5, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 75, "maintenance": 82, "payments": 25, "reliability": 55, "schema": 77, "security": 65, "transparency": 64 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": -4, "negativeNotes": [ "-4: on 2026-08-26 the changelog deprecated Qwen 3.5 9B and Qwen 3.6 27B from Serverless Training 'effective August 26, 2026', with no earlier entry announcing it, and told users to move existing workloads to Qwen 3.8 27B (https://docs.fireworks.ai/updates/changelog)" ], "verdict": "SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available. Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless.", "bestFor": "Teams that want managed SFT, DPO or RFT on large open models and may later write a custom RL loop on the same platform.", "strengths": [ "SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available", "LoRA SFT from $0.50 per 1M training tokens up to 16B parameters, with serving at base-model prices", "List endpoints take readMask, pageSize up to 200, AIP-160 filters and orderBy", "An Inference User role and revocable keys with an expiry date", "A public control-plane OpenAPI file, llms.txt and Markdown twins of every docs page" ], "weaknesses": [ "Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless", "No training without a payment method; the $1 sign-up credit buys inference only", "The status page has no training component; a 46-hour cloud provider incident in August hit dedicated deployments", "Two Serverless Training base models were deprecated with same-day effect on 2026-08-26", "Audit logs are Enterprise only and security.txt returns 404" ], "agentNotes": [ "Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute", "Check `firectl model get -a fireworks \u003cMODEL-ID\u003e` for Tunable: true before uploading a dataset", "Pass your own `supervisedFineTuningJobId` on create, so after a timeout you can GET the job by that name instead of guessing whether it started", "Deploy the LoRA to an on-demand deployment with a BF16 shape if several adapters will share it, and delete the deployment when evaluation ends", "Download with `firectl model download` and keep the exact base model; the adapter alone won't run" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "ergonomics": 75, "maintenance": 82, "payments": 25, "reliability": 55, "schema": 77, "security": 65, "transparency": 57 }, "provenanceScore": 71 }, "connect": { "install": "pip install fireworks-ai # add [training] for the Training API", "http": "curl https://api.fireworks.ai/v1/accounts/$FIREWORKS_ACCOUNT_ID/supervisedFineTuningJobs \\\n -H \"Authorization: Bearer $FIREWORKS_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"baseModel\":\"accounts/fireworks/models/gemma-4-31b-it\",\"dataset\":\"accounts/'$FIREWORKS_ACCOUNT_ID'/datasets/my-dataset\",\"outputModel\":\"accounts/'$FIREWORKS_ACCOUNT_ID'/models/my-tune\",\"loraRank\":16}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/fireworks-fine-tuning" }, "area": "models", "unitPrices": [ { "item": "LoRA SFT, models up to 16B", "unit": "1m-tokens", "usd": 0.5 }, { "item": "LoRA DPO, models up to 16B", "unit": "1m-tokens", "usd": 1 }, { "item": "Full-parameter SFT, models up to 16B", "unit": "1m-tokens", "usd": 1 }, { "item": "LoRA SFT, 16.1B to 80B", "unit": "1m-tokens", "usd": 3 }, { "item": "LoRA SFT, 80B to 300B", "unit": "1m-tokens", "usd": 6 }, { "item": "LoRA SFT, over 300B", "unit": "1m-tokens", "usd": 10 }, { "item": "Serverless Training API, Qwen 3.8 27B", "unit": "1m-tokens", "usd": 4.103 }, { "item": "Dedicated training, H100 or H200", "unit": "gpu-hour", "usd": 8, "note": "Effective 2026-09-01" }, { "item": "Dedicated training, B200", "unit": "gpu-hour", "usd": 13 }, { "item": "Dedicated training, B300", "unit": "gpu-hour", "usd": 15 }, { "item": "Dedicated training, GB300", "unit": "gpu-hour", "usd": 20 } ], "provenance": { "legalEntity": "Fireworks.ai, Inc.", "domain": "fireworks.ai", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://fireworks.ai/terms-of-service", "privacy": "https://fireworks.ai/privacy-policy", "statusPage": "https://status.fireworks.ai", "changelog": "https://docs.fireworks.ai/updates/changelog", "securityTxt": "none", "checked": "2026-09-30", "notes": [ "The privacy policy, last updated 8/11/2026, names Fireworks.ai, Inc. and gives no address.", "fireworks.ai/robots.txt disallows the terms of service page to crawlers, so we read the entity from the privacy policy instead.", "fireworks.ai/.well-known/security.txt returns 404.", "The status page lists 16 serverless model components and no training component.", "The .ai registry's RDAP server refused our requests, so the registration date is blank.", "The MCP registry holds a third-party io.usefulapi/fireworks server; Fireworks doesn't publish one." ], "score": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/fireworks-fine-tuning.json", "live": { "slug": "fireworks-fine-tuning", "probe": { "target": "https://api.fireworks.ai", "method": "get", "lastAt": "2026-10-09T11:00:23.834849443Z", "lastOk": true, "lastStatus": 404, "lastMs": 48, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 30, "p95ms24h": 71, "samples24h": 260, "samples30d": 2101, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 109 }, { "date": "2026-10-02", "probes": 248, "ok": 248 }, { "date": "2026-10-03", "probes": 271, "ok": 271 }, { "date": "2026-10-04", "probes": 272, "ok": 272 }, { "date": "2026-10-05", "probes": 272, "ok": 272 }, { "date": "2026-10-06", "probes": 272, "ok": 272 }, { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 117, "ok": 117 } ] }, "vendorStatus": { "page": "https://status.fireworks.ai", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-09T11:03:41.661677032Z" }, "versions": [ { "registry": "github", "name": "fw-ai-external/python-sdk", "version": "v1.2.20", "released": "2026-10-06", "seenAt": "2026-10-08T16:11:59.751275418Z" }, { "registry": "pypi", "name": "fireworks-ai", "version": "1.2.20", "released": "2026-10-06", "seenAt": "2026-10-08T16:11:59.542957612Z" } ], "githubStars": 10, "pypiWeekly": 281946, "securityTxt": { "url": "https://fireworks.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:39:06.393055593Z" }, "llmsTxt": { "url": "https://docs.fireworks.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:26.379688262Z" }, "domain": { "domain": "fireworks.ai", "registered": "2020-03-11", "source": "https://rdap.identitydigital.services/rdap/domain/fireworks.ai", "checkedAt": "2026-10-04T13:05:45.131398465Z" }, "pages": [ { "url": "https://docs.fireworks.ai/updates/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-10-08T18:18:43.993471365Z", "changedAt": "2026-10-08T18:18:43.993471365Z", "fingerprint": "73724d4498c3" }, { "url": "https://fireworks.ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-08T18:20:23.293613999Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "9f6b298d1e67" }, { "url": "https://fireworks.ai/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-08T18:20:25.487694157Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "7220d287a92b" }, { "url": "https://fireworks.ai/terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:20:27.561123635Z", "changedAt": "0001-01-01T00:00:00Z" } ], "updatedAt": "2026-10-09T11:03:41.661677032Z" } }, "facts": [ { "a": "Agent framework", "b": "HTTP API", "name": "Kind" }, { "a": "Axolotl AI", "b": "Fireworks AI", "name": "Vendor" }, { "a": "no (local only)", "b": "https://api.fireworks.ai", "name": "Hosted endpoint" }, { "a": "", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "API key", "name": "Auth" }, { "a": "Free", "b": "Pay per use", "name": "Pricing" }, { "a": "free", "b": "$0.50 per 1M tokens", "name": "Price for finetune sft" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0 (SDK)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-09-30", "b": "2026-10-01", "name": "Last release" }, { "a": "no document linked", "b": "couldn't be read", "name": "Terms last updated" }, { "a": "no document linked", "b": "no date given", "name": "Privacy policy last updated" }, { "a": "", "b": "couldn't be read", "name": "Customer content may train models" }, { "a": "", "b": "couldn't be read", "name": "Terms restrict automated access" }, { "a": "", "b": "couldn't be read", "name": "Terms restrict benchmarking" }, { "a": "", "b": "couldn't be read", "name": "Terms or service can change without notice" }, { "a": "", "b": "couldn't be read", "name": "Arbitration or class-action waiver" }, { "a": "13k stars, 2.1k PyPI/wk", "b": "7 stars, 290k PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "2.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Axolotl scores 64.8 (B) on agent readiness against Fireworks AI Fine-tuning's 59 (C), and leads in 4 of 7 scored categories. Fireworks AI Fine-tuning leads on agent ergonomics, security \u0026 auth and transparency \u0026 trust.", "question": "Which is better for AI agents, Axolotl or Fireworks AI Fine-tuning?" }, { "answer": "Axolotl, at free against $0.50 per 1M tokens for Fireworks AI Fine-tuning. These are the vendors' published prices for the job.", "question": "Which is cheaper for finetune sft, Axolotl or Fireworks AI Fine-tuning?" }, { "answer": "No hosted endpoint is listed for Axolotl. Fireworks AI Fine-tuning has a hosted endpoint at https://api.fireworks.ai.", "question": "Can an agent call Axolotl and Fireworks AI Fine-tuning without installing anything?" }, { "answer": "Axolotl is open source (Apache-2.0). No open-source release is listed for Fireworks AI Fine-tuning.", "question": "Are Axolotl and Fireworks AI Fine-tuning open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 64 against 55", "Payments \u0026 pricing, 60 against 25", "Maintenance \u0026 community, 88 against 82" ], "also": [ "No key needed to call it", "Open source", "No incidents deducted, where Fireworks AI Fine-tuning loses 4 points for them" ], "goodFor": "A team that wants a repeatable, config-driven fine-tune of an open model on its own or rented GPUs, including multi-GPU and multi-node runs.", "slug": "axolotl", "watchFor": "Telemetry to PostHog is on by default and delays training start by 10 seconds until the variable is set either way" }, { "aheadOn": [ "Agent ergonomics, 75 against 60", "Security \u0026 auth, 65 against 52", "Transparency \u0026 trust, 64 against 56" ], "also": [ "A hosted endpoint, with nothing to install" ], "goodFor": "Teams that want managed SFT, DPO or RFT on large open models and may later write a custom RL loop on the same platform.", "slug": "fireworks-fine-tuning", "watchFor": "Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless" } ], "job": { "capability": "finetune.sft", "name": "Finetune sft" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.json", "title": "Amazon Bedrock model customisation vs Axolotl", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-fine-tuning.json", "title": "Amazon Bedrock model customisation vs Fireworks AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning.json", "title": "Axolotl vs Microsoft Foundry fine-tuning (Azure OpenAI)", "url": "https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning.json", "title": "Axolotl vs Nebius Token Factory fine-tuning", "url": "https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-tinker.json", "title": "Axolotl vs Tinker", "url": "https://www.anchorterminal.com/compare/axolotl-vs-tinker" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-together-fine-tuning.json", "title": "Axolotl vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/axolotl-vs-together-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-unsloth.json", "title": "Axolotl vs Unsloth", "url": "https://www.anchorterminal.com/compare/axolotl-vs-unsloth" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-vertex-ai-tuning.json", "title": "Axolotl vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/axolotl-vs-vertex-ai-tuning" }, { "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Fireworks AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.json", "title": "Fireworks AI Fine-tuning vs Nebius Token Factory fine-tuning", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-tinker.json", "title": "Fireworks AI Fine-tuning vs Tinker", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-tinker" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-tuning.json", "title": "Fireworks AI Fine-tuning vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.json", "title": "Fireworks AI Fine-tuning vs Unsloth", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.json", "title": "Fireworks AI Fine-tuning vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning" } ], "scores": [ { "axolotl": 64, "by": 9, "edge": "axolotl", "fireworks-fine-tuning": 55, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "axolotl": 80, "by": 3, "edge": "axolotl", "fireworks-fine-tuning": 77, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "axolotl": 60, "by": 15, "edge": "fireworks-fine-tuning", "fireworks-fine-tuning": 75, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "axolotl": 52, "by": 13, "edge": "fireworks-fine-tuning", "fireworks-fine-tuning": 65, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "axolotl": 60, "by": 35, "edge": "axolotl", "fireworks-fine-tuning": 25, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "axolotl": 88, "by": 6, "edge": "axolotl", "fireworks-fine-tuning": 82, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "axolotl": 56, "by": 8, "edge": "fireworks-fine-tuning", "fireworks-fine-tuning": 64, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Axolotl scores 64.8 (B) on agent readiness against Fireworks AI Fine-tuning's 59 (C), and leads in 4 of 7 scored categories. Fireworks AI Fine-tuning leads on agent ergonomics, security \u0026 auth and transparency \u0026 trust. Both do finetune sft.", "verdicts": { "axolotl": "Axolotl runs a whole fine-tuning job from one YAML file and ships a JSON Schema of its config plus bundled agent docs. It is 0.x software with telemetry on by default, no terms or privacy policy, and the owner supplies the GPU.", "fireworks-fine-tuning": "SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available. Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning", "json": "https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.md", "slim": "https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.min.md" }, "markdown": "Axolotl scores 64.8 (B) on agent readiness against Fireworks AI Fine-tuning's 59 (C), and leads in 4 of 7 scored categories. Fireworks AI Fine-tuning leads on agent ergonomics, security \u0026 auth and transparency \u0026 trust. Both do finetune sft.\n\n- Axolotl: grade B, 64.8/100, rank #307 of 842. Markdown https://www.anchorterminal.com/tools/axolotl.md · JSON https://www.anchorterminal.com/api/v1/tools/axolotl.json\n- Fireworks AI Fine-tuning: grade C, 59/100, rank #506 of 842. Markdown https://www.anchorterminal.com/tools/fireworks-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json\n\n## Which one, for what\n\n### Axolotl (B)\n\nGood for: A team that wants a repeatable, config-driven fine-tune of an open model on its own or rented GPUs, including multi-GPU and multi-node runs.\n\nAhead on:\n- Reliability, 64 against 55\n- Payments \u0026 pricing, 60 against 25\n- Maintenance \u0026 community, 88 against 82\n\nAlso in its favour:\n- No key needed to call it\n- Open source\n- No incidents deducted, where Fireworks AI Fine-tuning loses 4 points for them\n\nWatch for: Telemetry to PostHog is on by default and delays training start by 10 seconds until the variable is set either way\n\n### Fireworks AI Fine-tuning (C)\n\nGood for: Teams that want managed SFT, DPO or RFT on large open models and may later write a custom RL loop on the same platform.\n\nAhead on:\n- Agent ergonomics, 75 against 60\n- Security \u0026 auth, 65 against 52\n- Transparency \u0026 trust, 64 against 56\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless\n\n\n## Score by category\n\n| Category | Weight | Axolotl | Fireworks AI Fine-tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 64 | 55 | Axolotl +9 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 80 | 77 | Axolotl +3 |\n| Agent ergonomics | 13% (16.2 this run) | 60 | 75 | Fireworks AI Fine-tuning +15 |\n| Security \u0026 auth | 14% (17.5 this run) | 52 | 65 | Fireworks AI Fine-tuning +13 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 25 | Axolotl +35 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 88 | 82 | Axolotl +6 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 56 | 64 | Fireworks AI Fine-tuning +8 |\n| Negative events | ≤15 | 0 | -4 | |\n| **Total** | | **64.8 · B** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Axolotl | Fireworks AI Fine-tuning |\n| --- | --- | --- |\n| Kind | Agent framework | HTTP API |\n| Vendor | Axolotl AI | Fireworks AI |\n| Hosted endpoint | no (local only) | `https://api.fireworks.ai` |\n| Transports | | HTTP |\n| Auth | None | API key |\n| Pricing | Free | Pay per use |\n| Price for finetune sft | free | $0.50 per 1M tokens |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 (SDK) |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-09-30 | 2026-10-01 |\n| Terms last updated | no document linked | couldn't be read |\n| Privacy policy last updated | no document linked | no date given |\n| Customer content may train models | | couldn't be read |\n| Terms restrict automated access | | couldn't be read |\n| Terms restrict benchmarking | | couldn't be read |\n| Terms or service can change without notice | | couldn't be read |\n| Arbitration or class-action waiver | | couldn't be read |\n| Popularity | 13k stars, 2.1k PyPI/wk | 7 stars, 290k PyPI/wk |\n| Agent reviews | none | 2.5/5 (2) |\n\n## Verdicts\n\n**Axolotl.** Axolotl runs a whole fine-tuning job from one YAML file and ships a JSON Schema of its config plus bundled agent docs. It is 0.x software with telemetry on by default, no terms or privacy policy, and the owner supplies the GPU.\n\n**Fireworks AI Fine-tuning.** SFT, DPO, ORPO and RFT as managed jobs, plus a serverless Training API that is generally available. Tuned LoRAs only deploy to on-demand GPUs at $8 an hour and up, never to serverless.\n\n## Before you call either\n\n### Axolotl\n\n1. Set `AXOLOTL_DO_NOT_TRACK=1` before any command, or training waits 10 seconds and sends usage events to PostHog\n2. Run `axolotl agent-docs` and `axolotl config-schema --field \u003cname\u003e` before writing a config; both work offline from the installed package\n3. Install torch first, then `uv pip install --no-build-isolation axolotl[deepspeed]`, on Python 3.12 or later with PyTorch 2.13 or later\n4. Take example configs from the same release tag as the installed version; minor releases remove and rename config keys\n5. Resume an interrupted run with `axolotl train config.yml --resume-from-checkpoint \u003cpath\u003e`, then `axolotl merge-lora` and `axolotl export` only when shipping\n\n### Fireworks AI Fine-tuning\n\n1. Add a payment method before the first job; without one the account has 0 training GPUs and 10 requests a minute\n2. Check `firectl model get -a fireworks \u003cMODEL-ID\u003e` for Tunable: true before uploading a dataset\n3. Pass your own `supervisedFineTuningJobId` on create, so after a timeout you can GET the job by that name instead of guessing whether it started\n4. Deploy the LoRA to an on-demand deployment with a BF16 shape if several adapters will share it, and delete the deployment when evaluation ends\n5. Download with `firectl model download` and keep the exact base model; the adapter alone won't run\n\n## Questions\n\n### Which is better for AI agents, Axolotl or Fireworks AI Fine-tuning?\n\nAxolotl scores 64.8 (B) on agent readiness against Fireworks AI Fine-tuning's 59 (C), and leads in 4 of 7 scored categories. Fireworks AI Fine-tuning leads on agent ergonomics, security \u0026 auth and transparency \u0026 trust.\n\n### Which is cheaper for finetune sft, Axolotl or Fireworks AI Fine-tuning?\n\nAxolotl, at free against $0.50 per 1M tokens for Fireworks AI Fine-tuning. These are the vendors' published prices for the job.\n\n### Can an agent call Axolotl and Fireworks AI Fine-tuning without installing anything?\n\nNo hosted endpoint is listed for Axolotl. Fireworks AI Fine-tuning has a hosted endpoint at https://api.fireworks.ai.\n\n### Are Axolotl and Fireworks AI Fine-tuning open source?\n\nAxolotl is open source (Apache-2.0). No open-source release is listed for Fireworks AI Fine-tuning.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"axolotl\", \"b\": \"fireworks-fine-tuning\"}`. From a terminal: `anchor compare axolotl fireworks-fine-tuning`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/axolotl.json and https://www.anchorterminal.com/api/v1/tools/fireworks-fine-tuning.json\n\n## Other comparisons with Axolotl or Fireworks AI Fine-tuning\n\n- [Amazon Bedrock model customisation vs Axolotl](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.md)\n- [Amazon Bedrock model customisation vs Fireworks AI Fine-tuning](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-fireworks-fine-tuning.md)\n- [Axolotl vs Microsoft Foundry fine-tuning (Azure OpenAI)](https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning.md)\n- [Axolotl vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning.md)\n- [Axolotl vs Tinker](https://www.anchorterminal.com/compare/axolotl-vs-tinker.md)\n- [Axolotl vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-together-fine-tuning.md)\n- [Axolotl vs Unsloth](https://www.anchorterminal.com/compare/axolotl-vs-unsloth.md)\n- [Axolotl vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/axolotl-vs-vertex-ai-tuning.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Fireworks AI Fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-fireworks-fine-tuning.md)\n- [Fireworks AI Fine-tuning vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.md)\n- [Fireworks AI Fine-tuning vs Tinker](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-tinker.md)\n- [Fireworks AI Fine-tuning vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-together-fine-tuning.md)\n- [Fireworks AI Fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-unsloth.md)\n- [Fireworks AI Fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-vertex-ai-tuning.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Axolotl vs Fireworks AI Fine-tuning", "url": "" } ], "description": "Axolotl scores 64.8 (B) on agent readiness against Fireworks AI Fine-tuning's 59 (C), and leads in 4 of 7 scored categories. Fireworks AI Fine-tuning leads on agent ergonomics, security \u0026 auth and transparency \u0026 trust. Both do finetune sft. Category scores, facts, verdicts and…", "facts": [ "Axolotl B 64.8", "Fireworks AI Fine-tuning C 59", "scores" ], "h1": "Axolotl vs Fireworks AI Fine-tuning", "image": "https://www.anchorterminal.com/assets/og/compare-axolotl-vs-fireworks-fine-tuning.png", "path": "/compare/axolotl-vs-fireworks-fine-tuning", "published": "2026-10-01", "section": "tools", "title": "Axolotl vs Fireworks AI Fine-tuning for AI agents, B 64.8 vs C 59", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning" }, "tokens": { "markdown": 2350, "slim": 780 }, "version": 1 }